JP6875231B2 - Information processing device - Google Patents

Description

The present invention relates to an information processing device that uses location information.

Patent Document 1 below describes a technique for learning stock price fluctuation knowledge information from news information and predicting the stock price of a specific stock based on the knowledge information when the new news contains the knowledge information. ..

Japanese Unexamined Patent Publication No. 2012-15700

However, since the technique described in Patent Document 1 predicts economic indicators such as stock price forecasts based on published news information, economic indicators such as stock price forecasts are made before the news information is published. Indicators cannot be predicted.

Therefore, an object of the present invention is to provide an information processing device capable of appropriately predicting economic indicators regardless of whether or not news information is published.

In order to solve the above-mentioned problems, the information processing apparatus of the present invention has a model storage unit that stores a prediction model set based on the position information of the terminals, and an acquisition unit that acquires the position information of one or more terminals. A prediction model stored in the model storage unit is provided with a prediction unit that predicts a designated economic indicator by applying the position information acquired by the acquisition unit, and the acquisition unit is predetermined. For a terminal set according to a corporate contract as a terminal, location information is acquired for each industry of the corporation, and the forecasting unit obtains the designated economic indicator based on the location information acquired for each industry. Predict.

According to the present invention, it is possible to predict economic indicators based on the flow of people such as the position information of terminals. Therefore, quick prediction can be made regardless of the presence or absence of publicly available information such as news and SNS.

According to the present invention, economic indicators can be predicted based on the position information of the terminal.

It is a figure which shows the system structure of the communication system including the information processing apparatus 100 of 1st Embodiment. It is a block diagram which shows the functional structure of the information processing apparatus 100 of 1st Embodiment. It is a figure which shows the movement history information stored in the position information storage unit 102. A specific example of the information stored in the regional information storage unit 110 is shown. It is a flowchart which shows the prediction model learning process of the information processing apparatus 100. It is a flow chart which shows the prediction processing by an information processing apparatus 100. It is a flowchart which shows the area type estimation process in the information processing apparatus 100. It is a block diagram which shows the functional structure of the information processing apparatus 100a in 2nd Embodiment. It is a figure which shows an example of the hardware composition of the information processing apparatus 100 (including the information processing apparatus 100a) which concerns on this embodiment.

An embodiment of the present invention will be described with reference to the accompanying drawings. When possible, the same parts are designated by the same reference numerals and duplicate description is omitted.

FIG. 1 is a diagram showing a system configuration of a communication system including the information processing device 100 of the first embodiment. As shown in FIG. 1, the communication system includes an information processing device 100, a location information server 200, and a mobile terminal 300.

The information processing device 100 acquires the position information of each mobile terminal 300 from the position information server 200 or the mobile terminal 300 and stores it as movement history information. Then, the information processing device 100 predicts an economic index designated according to the area type based on the staying state of the mobile terminal 300 in the area type set in each area.

The location information server 200 is a server that manages the location information of the mobile terminal 300 managed by each telecommunications carrier. In the present embodiment, the information processing device 100 may use the position information managed by the position information server 200, or may acquire and use the position information measured by the mobile terminal 300 by GPS or the like. The mobile terminal 300 is, for example, a mobile phone, and its position is managed by a telecommunications carrier.

Next, the functional configuration of the information processing apparatus 100 of the present embodiment will be described. FIG. 2 is a block diagram showing a functional configuration of the information processing apparatus 100 of the first embodiment. The information processing device 100 includes an input unit 101 (acquisition unit), a position information storage unit 102, a position information aggregation unit 103 (acquisition unit), a learning unit 104, a prediction model storage unit 105, a prediction unit 106, and a region selection unit 107. It includes an output unit 108, a region type estimation unit 109 (region type determination unit), and a region information storage unit 110.

The input unit 101 is a part for inputting the position information and the time information (time) staying at the position from the position information server 200 or each mobile terminal 300.

The position information storage unit 102 stores the position information and the time information for each mobile terminal 300 input by the input unit 101. In addition, in order to prevent the user of the mobile terminal from being identified, concealment processing such as removing or modifying ID information or the like for identifying the user is performed.

FIG. 3 is a diagram showing movement history information stored in the position information storage unit 102. As shown in FIG. 3, the terminal ID, the date and time, the latitude, and the longitude are stored in association with each other. In addition, it may be used as rough position information such as an area ID instead of latitude and longitude. Further, as a matter of course, it may be changed to the terminal ID and used as other identification information.

The position information totaling unit 103 is a part that totals the number of terminals in a designated area in a designated time zone based on the position information and time information stored in the position information storage unit 102.

The aggregation timing of the position information aggregation unit 103 and the aggregation unit thereof are as follows, for example.

・ Aggregate location information by time and region. The time may be in any unit such as every hour or every day. The area is arbitrary, such as a mesh unit or an administrative boundary unit.

・ You may rebate the population using the census, basic resident register, etc.

Each position information may be weighted in the aggregation. For example, the weight of the position information for each user may be changed according to the frequency of the position information for each user. For example, the weight of the position information for each attribute may be changed according to the number of users for each attribute (age group, gender, etc.).

The learning unit 104 is a part for generating a prediction model. In the initial state, the learning unit 104 applies the number of terminals in each region and each economic indicator aggregated by the location information aggregation unit 103 in a predetermined period to a preset model formula as an explanatory variable and an objective variable. Then, generate a forecast model for each economic indicator. For example, the following coefficients are calculated using, for example, the least squares method to generate a prediction model corresponding to each economic indicator.

Economic indicators = a ₀ + a ₁ · x ₁ + a ₂ · x ₂ + a ₃ · x ₃ + ... an _n · x _n
(X: _variable, a 1 ~a _n: coefficient, a _0: a constant term (compensation value))
Further, x is a variable in which the number of location information (number of terminals) for each region is input. The area designated by x may be each area of the whole of Japan, or may be only each area of a wide area such as the metropolitan area. There are appropriate regional types for forecasting economic indicators. Therefore, it is preferable to use the number of location information of the region corresponding to the region type determined according to the economic index. Further, the coefficient a of the area that does not correspond to the area type may be set to 0.

Further, a model formula including the variable x and the coefficient a for each time zone in which the position information is aggregated may be used. For example, the following can be considered.

Economic indicators _{= a 0 + a 101 · x} 101 + a 102 · x 102 + a 103 · x 103 + ... a 124 · x 124+ ··· a n24 · x n24
a ₁₀₁ is a coefficient between 0:00 and 1:00 in the time zone in region: 1, and x ₁₀₁ is the number of position information in which the time zone in region: 1 is between 0:00 and 1:00. The time zone is an example and can be other than the unit of one hour.

In addition, there are good times to predict economic indicators. Therefore, the position information aggregation unit 103 may determine the time zone for aggregation based on the economic index to be learned.

Economic indicators include interest rate indicators, economic indicators (GDP, business sentiment survey, consumption trends), employment indicators (employment statistics), price indicators (price increase rate), trade indicators (trade deficit), etc. There is. Specifically, there are the Nikkei Stock Average, the manufacturing business index, the consumer price index, the business conditions index, and the like. In addition, there may be retail sales and the number of homes sold in each region. In addition, DI (Diffusion Index) based on the Economy Watchers Survey is also conceivable.

The learning unit 104 learns the prediction model at predetermined period intervals, updates the coefficients as appropriate, and generates the optimum prediction model.

The prediction model storage unit 105 stores the prediction model generated for each economic indicator generated by the learning unit 104.

The prediction unit 106 predicts that the number of position information of each mobile terminal 300 that the position information aggregation unit 103 has aggregated most recently or in a predetermined period is prepared for each index stored in the prediction model storage unit 105. By applying it to the model, it is possible to predict economic indicators specified or predetermined by the operator.

The forecasting unit 106 may select a forecast model based on an economic indicator designated by the operator, or designates an economic indicator based on the regional type selected by the regional selection unit 107, which will be described later, and uses the economic indicator as the economic indicator. A predictive model based on it may be selected. In addition to the economic indicators of Japan as a whole, the forecast target may be indicators in one region, such as retail sales, consumption, number of houses, and house prices.

The area selection unit 107 selects one or a plurality of areas corresponding to the area type specified by the operator from the information stored in the area information storage unit 110 when the prediction process is performed by the prediction unit 106. Is. The prediction unit 106 predicts a designated economic index based on the number of position information of the mobile terminals 300 located in one or a plurality of areas selected by the area selection unit 107.

Further, the area selection unit 107 may directly accept the selection of the area from the operator regardless of the area type. Further, the area type corresponding to the economic indicator specified by the operator may be specified, and the area corresponding to the area type may be selected. For example, if the designation of labor-related indicators such as the unemployment rate is accepted, the area corresponding to the office area is selected, and if the index is related to the restaurant business, the area corresponding to the downtown area / tourist area is selected. If it is the Nikkei Stock Average, select an area that corresponds to the office area, downtown area, or tourist area.

The output unit 108 is a unit that outputs an economic indicator predicted by the prediction unit 106. The output unit 108 may output to a display (not shown) or may output to another device via a network.

The area type estimation unit 109 is a part that estimates the area type for each area based on the number of position information of each mobile terminal 300 in a predetermined period stored in the position information storage unit 102.

For example, the area type estimation unit 109 estimates the area type according to the following rules.

Comparing the location information aggregation results of weekday daytime, weekday nighttime, holiday daytime, and holiday nighttime, the areas where there are many weekday daytime are office areas,
Areas that are often used during the daytime on weekdays and holidays are commercial areas.
Areas that decrease during the daytime on weekdays are residential areas,
The area where holidays are almost gone is the industrial area,
May be. For example, it is from 11:00 to 15:00 in the daytime and from 18:00 to 21:00 in the evening. The classification method is not limited to the above. Moreover, the time division is not limited to the above. Part or all of the above classification may be replaced with a correspondence table of any region / type.

Further, the area type estimation unit 109 may capture not only the tendency of a fixed cycle such as weekdays and holidays but also the tendency of a long period of time, and estimate the area type based on the long-term tendency.

The area information storage unit 110 is a part that stores the area type estimated by the area type estimation unit 109 in association with the area. The area information storage unit 110 may store the area and the area type registered by the operator operation regardless of the estimation process by the area type estimation unit 109.

FIG. 4 shows a specific example of the information stored in the regional information storage unit 110. As shown in FIG. 4, the area ID and the area type are stored in association with each other.

Next, the prediction model learning process of the information processing apparatus 100 of the present embodiment will be described. FIG. 5 is a flowchart showing the prediction model learning process of the information processing apparatus 100.

When the learning operation of the prediction model is received from the operator by the learning unit 104, in the initial state, the position information aggregation unit 103 aggregates the location information for a predetermined period for each region (S101). Next, the learning unit 104 receives an economic indicator from the operator (S102). The learning unit 104 acquires from the location information aggregation unit 103 the number of regions of the region type according to the received economic indicators and the number of location information in the predetermined time zone. For example, when an economic trend index is accepted as an economic indicator, an area with a commercial area, an industrial area, etc. as an area type is selected from all over Japan, and the number of location information in that area is displayed. To be acquired.

In addition, for example, if it is an index related to labor such as the unemployment rate, an area corresponding to an office area is selected, and if it is an index related to a restaurant business, an area corresponding to a downtown area / tourist area is selected. In addition, if it is the Nikkei Stock Average, the area corresponding to the office area, downtown area, and tourist area is selected.

Then, a forecast model for each economic indicator is generated (S103).

This prediction model is updated at a predetermined timing. For example, at a timing such as once a month, the location information for the period of one month is aggregated, the economic indicator at that time is input, and the forecast is updated (S104).

Next, the prediction processing using the prediction model learned in this way will be described. FIG. 6 is a flow chart showing prediction processing by the information processing apparatus 100.

When the operator performs an operation for the prediction process, the location information aggregation unit 103 aggregates the location information in the region, time, and time according to the economic indicator (S201). At the time of aggregation, the area type is selected by the operator, and the location information of the area according to the area type is aggregated. Not limited to this, a predetermined region type may be selected according to economic indicators, or a region may be directly selected.

Then, the prediction unit 106 reads out the prediction model corresponding to the designated economic index from the prediction model storage unit 105, and performs prediction processing using the prediction model (S202).

In addition, a plurality of economic indicators may be specified, and in that case, a prediction model corresponding to each is read and prediction processing is performed.

Next, the area type estimation process will be described. This process is a process for automatically determining the area type of each area based on the position information of the mobile terminal 300. FIG. 7 is a flowchart showing the area type estimation process in the information processing apparatus 100.

When the operator performs an operation for region type estimation processing, the location information aggregation unit 103 performs aggregation processing in each region and each time zone (S301). The area type estimation unit 109 estimates the area type according to the tendency of the number of position information in each time zone (S302). Then, the area type in each area is stored in the area information storage unit 110 (S303).

Prediction processing can be performed using the information stored in the regional information storage unit 110.

As described above, the information processing apparatus 100 has a function for predictive model learning, a function for predictive processing, and a function for estimating a region type, but a function for predictive model learning and a region. The function for estimating the type is not essential.

In the above description, it has been described that the prediction model is learned and the economic indicators are predicted by using the position information of a general mobile terminal without any particular limitation. However, it is also possible to limit the mobile terminals to be aggregated. For example, by targeting only the mobile terminal 300 set by the corporate contract as the aggregation target, it is possible to predict better economic indicators. Based on the terminal ID, the location information aggregation unit 103 can determine whether the mobile terminal is a mobile terminal set in a general contract (individual contract) or a mobile terminal set in a corporate contract. Only information can be aggregated.

In general, the increase in the number of mobile terminals 300 under corporate contracts is considered to mean that the financial condition of each company is improving. Therefore, it is possible to better predict labor-related economic indicators such as stock prices and the unemployment rate, which are closely related to the financial situation. In addition, by aggregating the location information of the mobile terminal 300 of the corporate contract for each industry, the trend of the industry can be predicted. For example, the aggregated result of the location information of corporate customers in the logistics industry can better predict the index related to the logistics industry. In performing such processing, it is necessary to obtain consent from each corporation and each individual as to whether or not it may be used as information for prediction processing.

It is also possible to use an IoT (Internet of Things) device as the mobile terminal 300. IoT devices may be provided in electricity meters, vending machines, heavy equipment, and the like. When the location information of such an IoT device is acquired in a certain area or in a wide area, it can be grasped that the industry using the IoT device is becoming active. Therefore, the economic indicators of each industry can be better estimated by using the aggregated result of the position information of the IoT device. Since the position information aggregation unit 103 can determine whether it is a general mobile terminal or an IoT device based on the terminal ID, it is possible to aggregate only the location information. It is also possible to distinguish the type of industry according to the type of device. Therefore, it is possible to estimate the economic indicators of each industry according to the type of device.

In addition, the location information aggregation unit 103 may aggregate only overseas contractors. This can also be determined based on the terminal ID. It is generally known that foreigners visiting Japan purchase many products when they come to Japan. Therefore, by using the result of aggregating the location information of users who have contracted mobile phones overseas in Japan, it is possible to better estimate the economic indicators related to commerce. Furthermore, by using aggregated information of location information by users in a specific country, for example, a country with a high purchasing motivation, for prediction, it is possible to accurately estimate economic indicators.

The location information aggregation unit 103 makes it possible to access a database that associates the terminal ID with contract information, terminal type, etc., and indicates that the terminal ID is a corporate contract, an overseas contract, or an IoT device described above. It can be distinguished based on the above.

In the above description, it is assumed that the aggregation of position information is narrowed down, but of course, a prediction model that combines these may be constructed. For example, by constructing the following prediction model, it is possible to perform prediction processing using the position information of various mobile terminals (including IoT devices). The following is a specific example of the prediction model.

Economic indicators = a ₀ + a ₁₁ · x ₁₁ + a ₁₂ · x ₁₂ + a ₁₃ · x ₁₃ + ... a _1n · x _1n + a ₂₁ · x ₂₁ + a ₂₁ · x ₂₁ + ... a _2n · x _2n … + a _n1 · x _n1 ＋… a _mn・ x _mn
The variable x indicates the number of each mobile terminal in the area p (p is 1 to m). The details are as follows.

Variable x _p1 Number of mobile terminals with individual contract Variable x _p2 Number of mobile terminals with corporate contract Variable x _p3 Number of IoT terminals Variable x _p Number of contract terminals of overseas carriers ...
The other number coefficients of x _pn indicate the coefficients in the area p (p is 1 to m). The details are as follows.

Coefficient for the coefficients _{a p1} contract terminal of the individual contract of mobile coefficient coefficient _{a p} overseas carriers for the coefficients coefficient _a p3 IoT terminal to the mobile terminal of the coefficient coefficient _{a p2} corporate contract for the terminal ...
Coefficients for coefficient a _pn and others When learning a prediction model and making a prediction using the prediction model, the variable x may be weighted depending on the economic index. For example, the index related to purchasing power can improve the prediction accuracy by weighting the number of location information of foreigners. Further, as described above, the variable x and the coefficient a may be subdivided for each time zone.

Next, the second embodiment will be described. FIG. 8 is a block diagram showing a functional configuration of the information processing apparatus 100a according to the second embodiment. As shown in FIG. 8, the information processing device 100a is different in that the semantic information adding unit 111 is added to the configuration of the information processing device 100 of the first embodiment.

The meaning information giving unit 111 is a part that gives meaning to the position information. The semantic information giving unit 111 may acquire the semantic information input by the user of the mobile terminal 300 together with the position information and add it to the position information of the mobile terminal 300, or is stored in the position information storage unit 102. Based on the movement history, the semantic information of the position information constituting the movement history may be determined and the semantic information may be added. The semantic information giving unit 111 stores the acquired or determined semantic information in association with each position information of the position information storage unit 102.

The position information totaling unit 103 totals the position information of each mobile terminal 300 based on the semantic information. The learning unit 104 and the prediction unit 106 perform learning processing of the prediction model and prediction processing using the prediction model based on the position information aggregated based on the semantic information.

In the second embodiment, the semantic information giving unit 111 can analyze and grasp the semantic information based on the movement history of each mobile terminal 300. Specifically, the following processing can be mentioned.

When the semantic information giving unit 111 moves a predetermined distance per predetermined time or continues to move for a predetermined time or longer based on the movement history information stored in the position information storage unit 102, the movement is performed. Judge that it is for sightseeing purposes. Similarly, it can be determined that the purpose is commuting, business, etc., based on the distance and activity time. In this way, the meaning information giving unit 111 can give the movement purpose as the meaning information.

Further, as another example, the semantic information giving unit 111 can determine what kind of vehicle the vehicle has moved based on the movement history information stored in the position information storage unit 102. For example, when the speed calculated based on the travel distance and the time is equal to or greater than a predetermined value, the section where the speed is estimated to be equal to or greater than the predetermined value is presumed to be traveled by an airplane. By the same method, it is possible to estimate the movement by rail, the movement by car, and the movement by bus. In this way, when the moving means is set as the semantic information, the semantic information giving unit 111 estimates the moving means and gives the moving means as the semantic information.

In addition, the semantic information giving unit 111 can determine what kind of area the user has visited based on the movement history information stored in the position information storage unit 102. For example, when the user (mobile terminal 300) visits a store / area handling high-end supermarkets and high-end products at a predetermined frequency during a predetermined period, the semantic information giving unit 111 determines that the user is a wealthy class. To do. Then, the location information of each mobile terminal 300 determined to be the wealthy class is given the meaning information indicating that the mobile terminal 300 is the wealthy class.

Similarly, the semantic information giving unit 111 can determine what kind of area the person lives in based on the movement history information stored in the position information storage unit 102. For example, a place that is in the service area at night can be determined to be home. When it is determined that the place where the home is located is a so-called high-class residential area, the user is determined to be a wealthy class.

In addition, the semantic information giving unit 111 may give information indicating that the group is acting as semantic information. For example, if there are a plurality of mobile terminals 300 that take the same movement history based on the movement history stored in the position information storage unit 102, the semantic information giving unit 111 can determine that they are in the same group. Further, when the meaning information giving unit 111 has been set in advance to act as a group by user setting, the meaning information indicating that the mobile terminal 300 is acting as a group is added to the position information of the mobile terminal 300. You may. This semantic information may include the number of people forming the group.

The location information aggregation unit 103 can aggregate the location information of the mobile terminal 300 indicating that it is acting as a group or acting alone as semantic information, and at that time, distinguishes the number of people forming the group. It may be aggregated. For example, a two-person group and a three-person group are distinguished and their position information is aggregated. For mobile terminals 300 in the same group, only one of them is subject to aggregation.

When the learning unit 104 and the prediction unit 106 handle the number of position information, the accuracy of each prediction model generation and prediction processing is improved by using values weighted according to the number of people forming the group. Let me.

As described above, in the second embodiment, the position information totaling unit 103 can perform the position information totaling process based on the semantic information such as the purpose of movement, the means of movement, the characteristics of the user, or the number of members of the group. As a matter of course, in addition to the semantic information, it is possible to aggregate by time and region. In addition, location information to which semantic information is not added may be excluded from aggregation.

Next, a modified example of the area type estimation unit 109 will be described. The area type estimation unit 109 determines the area type based on the position information stored in the position information storage unit 102, but is not limited to this. For example, as semantic information, if the purpose of travel: tourism is added to the location information and the location information satisfies a predetermined condition, the area is presumed to be a tourist destination. For example, an area containing a predetermined number of location information to which a tourist purpose is given during the daytime is presumed to be a tourist spot.

Further, the area type estimation unit 109 estimates that when a means of transportation is given as semantic information, the end point of the means of transportation is an area including important points of traffic.

Further, when the user's characteristic (here, the user attribute) is added to the position information as the semantic information, the area type estimation unit 109 is based on the position information of the mobile terminal 300 to which the user's characteristic is added. It is presumed that the defined predetermined position (region) is an region that matches the characteristics of the user. For example, when the characteristic of the user is a wealthy class, it is determined that one area is a high-class shopping area based on the location information of the mobile terminal 300 of the user who is the wealthy class.

In addition to information such as wealthy people, semantic information may be added as a user attribute based on gender, age, and the like. In addition, the characteristics of the user may be given meaning based on the way the user behaves, in addition to the characteristics of the user himself / herself. For example, the area type estimation unit 109 may estimate semantic information indicating that the user is acting as a group or acting alone as a characteristic of the user.

Then, when the location information is provided with information indicating that the user is acting as a group or acting alone as a characteristic of the user, the area type estimation unit 109 is given the semantic information. Based on the position information of the mobile terminal 300, it is estimated that the area where a predetermined number of position information is observed in a predetermined time zone is a predetermined specific area. For example, an area where a predetermined number of position information is observed in a predetermined time zone such as daytime is presumed to be a commercial area.

The prediction unit 106 can also make predictions for only each area estimated by the above-mentioned area type estimation unit 109.

Next, the effects of the information processing device 100 of the first embodiment and the information processing device 100a of the second embodiment will be described.

The information processing device 100 of the first embodiment includes a prediction model storage unit 105 that stores a prediction model set for each economic indicator that is a prediction target, and an input unit 101 that inputs position information of one or more terminals. , Prediction model Among the prediction models stored in the storage unit 105, the prediction model corresponding to the designated economic indicator is predicted by applying the position information input by the input unit 101 to predict the designated economic indicator. A unit 106 is provided.

According to the information processing device 100, economic indicators can be predicted based on the movement of a person such as the position information of the mobile terminal 300. Therefore, quick prediction can be made regardless of the presence or absence of publicly available information such as news.

Further, the information processing device 100 of the first embodiment corresponds to the area information storage unit 110 that stores each area and the area type of each area in association with each other, and the area type designated based on the operation of the operator. A regional selection unit for selecting each region to be processed from the regional information storage unit 110 is further provided.

The prediction unit 106 predicts a designated economic index by using the position information in each selected area among the position information acquired by the input unit 101.

According to the information processing apparatus 100, it is possible to select an area for which the position information is to be aggregated based on the area type designated based on the operation of the operator, and to perform the location information aggregation according to the area. Therefore, it is possible to easily aggregate the location information according to the area type. The operator's operation here includes an operation in which the operator directly specifies the area type, an operation in which the operator performs an operation for designating an economic indicator, and an operation in which the area type determined according to the economic indicator is specified.

Further, in the information processing apparatus 100, the input unit 101 acquires the position information of the predetermined mobile terminal 300 or the mobile terminal 300 set according to the predetermined contract conditions, and the prediction unit 106 obtains the position information based on the position information. , Predict the specified economic indicators.

According to the information processing device 100, the location information of the mobile terminal 300 such as an IoT device and the mobile terminal 300 whose communication is set by a special contract such as a corporate contract can be aggregated. Therefore, it is possible to obtain more appropriate position information of the mobile terminal 300 when predicting an economic indicator.

Further, in the information processing apparatus 100, the input unit 101 acquires location information for the mobile terminal 300 set according to the corporate contract as a predetermined contract condition for each type of business of the corporation, and the prediction unit 106 obtains the position information for each type of business. Predict the specified economic indicators based on the location information obtained separately.

According to the information processing device 100, for example, in a corporate contract, the location information of the mobile terminal 300 can be acquired by distinguishing each type of business. Therefore, it can be used for forecasting economic indicators by industry.

In the information processing device 100, the input unit 101 acquires the position information of the mobile terminal 300 set according to the contract of the overseas telecommunications carrier as a predetermined contract condition, and the prediction unit 106 obtains the position information based on the position information. Predict the specified economic indicators.

According to the information processing device 100, it is possible to grasp the trend of a user (mobile terminal 300) having an influence on economic indicators, such as a foreign tourist. Since foreign tourists have purchasing power, it is meaningful to obtain them separately from other location information, especially when predicting economic indicators related to commerce.

Next, the operation and effect of the information processing apparatus 100a of the second embodiment will be described. In addition to the configuration of the information processing device 100, the information processing device 100a further adds semantic information indicating the meaning to the position information of one or more terminals to the position information of the mobile terminal 300 acquired by the input unit 101. A semantic information giving unit 111 is provided. The prediction unit 106 predicts a designated economic indicator based on the position information of terminals whose semantic information matches.

According to the information processing device 100a, the meaning of the movement characteristics, purpose, means, etc. of each mobile terminal 300 is given to the position information, and the prediction processing is performed using the meaning, so that the economy is in line with the meaning of the movement. Allows the prediction of indicators.

For example, the information processing device 100a further includes a position information storage unit 102 that stores movement history information including position information and time information input by the input unit 101 as the movement history of the mobile terminal 300, and is a semantic information giving unit. The 111 determines and gives the semantic information for the position information of the mobile terminal 300 based on the movement history information stored in the position information storage unit 102.

According to the information processing device 100a, the meaning of the position information of the mobile terminal 300 can be determined from the movement history.

Further, when the movement purpose is set as the semantic information, the information processing device 100a estimates the area type of one area and the area type of the one area based on the position information in which the movement purpose matches. The unit 109, the area information storage unit 110 that stores one area estimated by the area type estimation unit 109 and the area type of the one area in association with each other, and each area corresponding to the area type specified by the operator. The area selection unit 107 is further provided from the area information storage unit 110, and the prediction unit 106 uses the position information in each selected area among a part or all of the position information input by the input unit 101. Predict the designated economic indicators.

According to the information processing device 100a, the purpose of movement can be determined from the movement history, and the area type of one area can be estimated based on the purpose of movement. Then, based on the region type estimated in this way, it is possible to easily select the region to be the target of the economic indicator.

Further, when the moving means is set as the semantic information, the information processing apparatus 100a sets one area, which is the end point of the moving means, as an important area as a region type, based on the position information in which the moving means matches. The area type estimation unit 109 that determines that there is, the area information storage unit 110 that stores the area type estimated by the area type estimation unit 109 and the area type of the one area in association with each other, and the area information storage unit 110 designated by the operator. A region selection unit for selecting each region corresponding to the region type from the region information storage unit 110 is further provided, and the prediction unit 106 is selected from a part or all of the position information acquired by the input unit 101. Predict specified economic indicators using location information in the region.

According to the information processing device 100a, the moving means can be determined from the moving history, and the area type of one area can be estimated based on the moving means. Then, based on the region type estimated in this way, it is possible to easily select the region to be the target of the economic indicator. In particular, the entry and exit of people at railway terminals, airports, etc., where people get on and off, is expected to have a significant impact on economic indicators. According to the information processing device 100a, such an important area can be estimated from the movement history.

Further, the semantic information adding unit 111 of the information processing device 100a adds the characteristics of the user of the terminal (user attributes, user behavior characteristics, etc.) based on the position information stored in the position information storage unit 102. Then, the information processing device 100a determines the area where the terminal of each user is located in a predetermined time zone based on the position information of the terminal of each user determined to be a specific feature by the semantic information giving unit 111. Area information storage that stores the area type estimation unit 109 that determines that it is a specific area corresponding to a specific feature, and the area type of one area and the area that is determined by the area type estimation unit 109 in association with each other. The unit 110 and the area selection unit 107 for selecting each area corresponding to the area type specified by the operator from the area information storage unit 110 are further provided, and the prediction unit 106 is one of the position information acquired by the input unit 101. Predict the specified economic indicators using location information in each selected region of the department or all.

According to the information processing device 100a, the characteristics of the user can be determined from the movement history, and the area type of one area can be estimated based on the characteristics. For example, it is determined whether the user is a wealthy person or is acting by a plurality of people. Then, based on the region type estimated in this way, it is possible to easily select the region to be the target of the economic indicator.

The block diagram used in the description of the above embodiment shows a block of functional units. These functional blocks (components) are realized by any combination of hardware and / or software. Further, the means for realizing each functional block is not particularly limited. That is, each functional block may be realized by one physically and / or logically coupled device, or directly and / or indirectly by two or more physically and / or logically separated devices. (For example, wired and / or wireless) may be connected and realized by these a plurality of devices.

For example, the information processing device 100 according to the embodiment of the present invention may function as a computer. FIG. 9 is a diagram showing an example of the hardware configuration of the information processing device 100 (including the information processing device 100a) according to the present embodiment. The information processing 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, and the like.

In the following description, the word "device" can be read as a circuit, a device, a unit, or the like. The hardware configuration of the information processing device 100 may be configured to include one or more of the devices shown in the figure, or may be configured not to include some of the devices.

For each function in the information processing device 100, by loading predetermined software (program) on hardware such as the processor 1001 and the memory 1002, the processor 1001 performs an calculation, and communication by the communication device 1004, the memory 1002, and the storage It is realized by controlling the reading and / or writing of the data in 1003.

Processor 1001 operates, for example, an operating system to control the entire computer. The processor 1001 may be composed of a central processing unit (CPU) including an interface with a peripheral device, a control device, an arithmetic unit, a register, and the like. For example, the position information aggregation unit 103, the learning unit 104, the prediction unit 106, the area selection unit 107, the area type estimation unit 109, and the like may be realized by the processor 1001.

Further, the processor 1001 reads a program (program code), a software module, and data from the storage 1003 and / or the communication device 1004 into the memory 1002, and executes various processes according to these. As the program, a program that causes a computer to execute at least a part of the operations described in the above-described embodiment is used. For example, the position information aggregation unit 103, the learning unit 104, the prediction unit 106, the area selection unit 107, and the area type estimation unit 109 of the information processing device 100 are stored in the memory 1002 and realized by a control program operated by the processor 1001. It may be realized in the same manner for other functional blocks. Although it has been described that the various processes described above are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. Processor 1001 may be mounted on one or more chips. The program may be transmitted from the network via a telecommunication line.

The memory 1002 is a computer-readable recording medium, and is composed of at least one such as a ROM (Read Only Memory), an EEPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), and a RAM (Random Access Memory). May be done. The memory 1002 may be referred to as a register, a cache, a main memory (main storage device), or the like. The memory 1002 can store a program (program code), a software module, or the like that can be executed to carry out the wireless communication method according to the embodiment of the present invention.

The storage 1003 is a computer-readable recording medium, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, an optical magnetic disk (for example, a compact disk, a digital versatile disk, a Blu-ray). It may consist of at least one (registered trademark) disk), smart card, flash memory (eg, card, stick, key drive), floppy (registered trademark) disk, magnetic strip, and the like. The storage 1003 may be referred to as an auxiliary storage device. The storage medium described above may be, for example, a database, server or other suitable medium containing memory 1002 and / or storage 1003.

The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via a wired and / or wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, or the like. For example, the above-mentioned input unit 101 and the like may be realized by the communication device 1004.

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 an 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 have an integrated configuration (for example, a touch panel).

Further, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. Bus 1007 may be composed of a single bus, or may be composed of different buses between devices.

Further, the information processing apparatus 100 includes hardware such as a microprocessor, a digital signal processor (DSP: Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). It may be configured by, and a part or all of each functional block may be realized by the hardware. For example, processor 1001 may be implemented on at least one of these hardware.

Although the present embodiment has been described in detail above, it is clear to those skilled in the art that the present embodiment is not limited to the embodiment described in the present specification. This embodiment can be implemented as a modified or modified mode without departing from the spirit and scope of the present invention determined by the description of the claims. Therefore, the description of the present specification is for the purpose of exemplifying explanation, and does not have any restrictive meaning to the present embodiment.

The notification of information is not limited to the embodiments / embodiments described herein, and may be performed by other methods. For example, information notification includes physical layer signaling (for example, DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (for example, RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, etc. It may be carried out by notification information (MIB (Master Information Block), SIB (System Information Block)), other signals, or a combination thereof. Further, 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.

Each aspect / embodiment described herein includes LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G, 5G, FRA (Future Radio Access), W-CDMA. (Registered Trademarks), GSM®, CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.11 (WiMAX), LTE 802.20, UWB (Ultra-WideBand), It may be applied to Bluetooth®, other systems that utilize suitable systems and / or next-generation systems that are extended based on them.

The order of the processing procedures, sequences, flowcharts, and the like of each aspect / embodiment described in the present specification may be changed as long as there is no contradiction. For example, the methods described herein present elements of various steps in an exemplary order, and are not limited to the particular order presented.

Information and the like can be output from the upper layer (or lower layer) to the lower layer (or upper layer). Input / output may be performed via a plurality of network nodes.

The input / output information and the like may be stored in a specific location (for example, memory) or may be managed by a management table. Input / output information and the like can be overwritten, updated, or added. The output information and the like may be deleted. The input information or the like may be transmitted to another device.

The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value (Boolean: true or false), or by comparing numerical values (for example, a predetermined value). It may be done by comparison with the value).

Each aspect / embodiment described in the present specification may be used alone, in combination, or switched with execution. Further, the notification of predetermined information (for example, the notification of "being X") is not limited to the explicit one, but is performed implicitly (for example, the notification of the predetermined information is not performed). May be good.

Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name, is an instruction, instruction set, code, code segment, program code, program, subprogram, software module. , Applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, features, etc. should be broadly interpreted.

Further, software, instructions, and the like may be transmitted and received via a transmission medium. For example, the software uses wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and / or wireless technology such as infrared, wireless and microwave to websites, servers, or other When transmitted from a remote source, these wired and / or wireless technologies are included within the definition of transmission medium.

The information, signals, etc. described herein may be represented using any of a variety of different techniques. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description are voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may be represented by a combination of.

The terms described herein and / or the terms necessary for understanding the present specification may be replaced with terms having the same or similar meanings.

The terms "system" and "network" as used herein are used interchangeably.

Further, the information, parameters, etc. described in the present specification may be represented by an absolute value, a relative value from a predetermined value, or another corresponding information. .. For example, the radio resource may be indexed.

The names used for the above parameters are not limited in any way. Further, mathematical formulas and the like using these parameters may differ from those expressly disclosed herein. Since the various channels (eg, PUCCH, PDCCH, etc.) and information elements (eg, TPC, etc.) can be identified by any suitable name, the various names assigned to these various channels and information elements are in any respect. However, it is not limited.

The terms "determining" and "determining" as used herein may include a wide variety of actions. "Judgment" and "decision" are, for example, judgment, calculation, computing, processing, deriving, investigating, looking up (for example, table). , Searching in a database or another data structure), ascertaining can be considered as a "judgment" or "decision". Also, "judgment" and "decision" are receiving (for example, receiving information), transmitting (for example, transmitting information), input (input), output (output), and access. (Accessing) (for example, accessing data in memory) may be regarded as "judgment" or "decision". In addition, "judgment" and "decision" mean that the things such as solving, selecting, choosing, establishing, and comparing are regarded as "judgment" and "decision". Can include. That is, "judgment" and "decision" may include considering some action as "judgment" and "decision".

The terms "connected", "coupled", or any variation thereof, mean any direct or indirect connection or connection between two or more elements, and each other. It can include the presence of one or more intermediate elements between two "connected" or "combined" elements. The connection or connection between the elements may be physical, logical, or a combination thereof. As used herein, the two elements are by using one or more wires, cables and / or printed electrical connections, and, as some non-limiting and non-comprehensive examples, radio frequencies. By using electromagnetic energies such as electromagnetic energies with wavelengths in the region, microwave region and light (both visible and invisible) regions, they can be considered to be "connected" or "coupled" to each other.

The phrase "based on" as used herein does not mean "based on" unless otherwise stated. In other words, the statement "based on" means both "based only" and "at least based on".

When the terms "first", "second", etc. are used herein, any reference to the elements does not generally limit the quantity or order of those elements. These designations can be used herein as a convenient way to distinguish between two or more elements. Thus, references to the first and second elements do not mean that only two elements can be adopted there, or that the first element must somehow precede the second element.

As long as "include", "including", and variations thereof are used within the scope of this specification or claims, these terms are similar to the term "comprising". Is intended to be inclusive. Furthermore, the term "or" as used herein or in the claims is intended not to be an exclusive OR.

In the present specification, a plurality of devices shall be included unless the device has only one device, which is apparently present in the context or technically.

In the whole of the present disclosure, the plural shall be included unless the context clearly indicates the singular.

100 ... Information processing device, 100a ... Information processing device, 101 ... Input unit, 102 ... Position information storage unit, 103 ... Position information aggregation unit, 104 ... Learning unit, 105 ... Prediction model storage unit, 106 ... Prediction unit, 107 ... Area selection unit, 108 ... Output unit, 109 ... Area type estimation unit, 110 ... Area information storage unit, 111 ... Semantic information addition unit, 200 ... Location information server, 300 ... Mobile terminal.

Claims (7)

A model storage unit that stores the prediction model set based on the location information of the terminal,
An acquisition unit that acquires the location information of one or more terminals,
A prediction unit that predicts a designated economic indicator by applying the position information acquired by the acquisition unit to the prediction model stored in the model storage unit, and
With
The acquisition unit acquires location information for a terminal set in accordance with a corporate contract as a predetermined terminal for each type of business of the corporation.
The forecasting unit predicts the designated economic indicator based on the location information acquired for each industry.
Information processing device. A regional information storage unit that stores each region in association with the regional type of each region,
A region selection unit for selecting each region corresponding to the region type specified based on the operation of the operator from the region information storage unit is further provided.
The prediction unit predicts the designated economic index by using the position information in each of the selected regions among the position information acquired by the acquisition unit.
The information processing device according to claim 1. A model storage unit that stores the prediction model set based on the location information of the terminal,
An acquisition unit that acquires the location information of one or more terminals,
A prediction unit that predicts a designated economic indicator by applying the position information acquired by the acquisition unit to the prediction model stored in the model storage unit, and
A granting unit that adds semantic information indicating the meaning to the position information of the one or more terminals to the position information of the terminal acquired by the acquisition unit, and
With
The prediction unit predicts the designated economic indicator based on the position information of the terminal whose semantic information matches.
Information processing device. A position information storage unit that stores the position information and the time information acquired by the acquisition unit as movement history information is further provided.
The granting unit determines and grants semantic information for the position information of the terminal based on the movement history information stored in the position information storage unit.
The information processing device according to claim 3. When information indicating the purpose of movement is added to the position information as the semantic information, the area type determination unit that determines the area type of one area and the area based on the position information that matches the purpose of movement. When,
A regional information storage unit that stores one area determined by the area type determination unit in association with the area type of the one area, and
It is further equipped with a region selection unit that selects each region corresponding to the specified region type from the region information storage unit.
The prediction unit predicts the designated economic indicator using the position information in each of the selected regions, out of a part or all of the position information acquired by the acquisition unit.
The information processing device according to claim 4. When information indicating a moving means is added to the position information as the semantic information, the area type of one area that is the end point of the moving means is set as an important area based on the position information that the moving means matches. The area type judgment department that judges that
A regional information storage unit that stores one area determined by the area type determination unit in association with the area type of the one area, and
It is further equipped with a region selection unit that selects each region corresponding to the specified region type from the region information storage unit.
The prediction unit predicts the designated economic indicator using the position information in each of the selected regions, out of a part or all of the position information acquired by the acquisition unit.
The information processing device according to claim 4. The giving unit determines the characteristics of each user based on the position information stored in the position information storage unit, and adds the characteristics of each user as semantic information.
Based on the location information that matches the semantic information given to the location information of each user's terminal, the region type of the region where the terminal of each user satisfies the predetermined conditions and is located, the region showing a specific feature. The regional type judgment department that judges that
A regional information storage unit that stores one area determined by the area type determination unit in association with the area type of the one area, and
It is further equipped with a region selection unit that selects each region corresponding to the specified region type from the region information storage unit.
The prediction unit predicts the designated economic indicator using the position information in each of the selected regions, out of a part or all of the position information acquired by the acquisition unit.
The information processing device according to claim 4.

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