JP2013500519A - Location-based information readout and analysis - Google Patents

Location-based information readout and analysis Download PDF

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Publication number
JP2013500519A
JP2013500519A JP2012521786A JP2012521786A JP2013500519A JP 2013500519 A JP2013500519 A JP 2013500519A JP 2012521786 A JP2012521786 A JP 2012521786A JP 2012521786 A JP2012521786 A JP 2012521786A JP 2013500519 A JP2013500519 A JP 2013500519A
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Prior art keywords
mobile device
data
user
location
method
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Japanese (ja)
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ジョン・シー・マクドナフ
ハドリー・ルパート・スターン
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エフエムアール エルエルシー
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Priority to US12/508,514 priority Critical
Priority to US12/508,509 priority
Priority to US12/508,509 priority patent/US20110022312A1/en
Priority to US12/508,514 priority patent/US20110022540A1/en
Application filed by エフエムアール エルエルシー filed Critical エフエムアール エルエルシー
Priority to PCT/US2010/042924 priority patent/WO2011011616A1/en
Publication of JP2013500519A publication Critical patent/JP2013500519A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/16Real estate
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06Investment, e.g. financial instruments, portfolio management or fund management

Abstract

  The disclosed methods and apparatus include computer program products for tracking patterns associated with mobile devices. The system includes a server computer configured to receive location information from a mobile device associated with the user. The position information includes position information based on GPS information transmitted from the mobile device. One or more activity patterns associated with the user of the mobile device are generated by the server computer. The generation includes tracking location information over a period of time and determining one or more categories of repetitive activities based on the tracking. The method and apparatus disclosed herein includes a computer program product for location based address determination and real estate assessment. The current location information from the mobile device is received by the server computer. The location information includes global positioning data related to the mobile device, one or more photos acquired by the mobile device, and photo data related to the location. A street address based on the location information is obtained by a server computer. Photo data is processed in association with global positioning data, where the photo data is used to determine the street address. Financial data associated with the street address is received.

Description

  The subject matter of this application generally relates to a method and apparatus for tracking and generating mobile device activity patterns, location based address determination and real estate assessment, including computer program products.

  Mobile devices having a global positioning system (GPS) function, such as mobile phones, personal digital assistant (PDA) devices, and smartphones, have become popular. With these devices, the user's location can be determined with some patience, and often triangulation methods are used to calculate the geographic coordinates of the device. Once calculated, the location can be used to present to the user along with specific information (e.g., driving directions, local maps, neighbors with GPS devices that indicate the current location, and the like).

  In one aspect, a computerized method for tracking activity patterns associated with mobile devices is provided. The location information from the mobile device related to the user is received by the server computer. The position information is based on GPS information transmitted from the mobile device. The location information is tracked (tracked) over a period of time. The occurrence of repetitive activity is determined based on the tracking. One or more activity patterns are generated. One or more activity patterns are associated with the mobile device. One or more activity patterns are based on the occurrence of repetitive activities.

  In another aspect, a system for tracking activity patterns associated with mobile devices is provided. The system includes a server computer that is configured to receive location information from a mobile device associated with the user. The location information is based on GPS information transmitted from the mobile device. Location information is tracked over a period of time. The occurrence of repetitive activity is determined based on the tracking. One or more activity patterns are generated. One or more activity patterns are associated with the mobile device. One or more activity patterns are based on the occurrence of repetitive activities.

  In another aspect, a system for tracking activity patterns associated with mobile devices is provided. The system includes means for receiving location information from a mobile device associated with the user. The location information is based on GPS information transmitted from the mobile device. The system includes means for tracking location information over a period of time. The system includes means for determining the occurrence of repetitive activity based on the tracking. The system includes means for generating one or more activity patterns associated with the mobile device. One or more activity patterns are based on the occurrence of repetitive activities.

  In another aspect, a computer program product for tracking activity patterns associated with mobile devices is provided. The computer program product is recorded on a computer readable storage medium. The computer program product includes instructions that cause the data processing device to receive location information from the mobile device associated with the user. The location information is based on GPS information transmitted from the mobile device. Location information is tracked over a period of time. The occurrence of repetitive activity is determined based on the tracking. One or more activity patterns are generated. One or more activity patterns are associated with the mobile device. One or more activity patterns are based on the occurrence of repetitive activities.

  Any aspect may include one or more of the following features. Location information may include purchase history data, personal interest data, demographic data, social networking data, mobile device usage data, financial portfolio data, or combinations thereof. Tracking location information may include receiving location information from a mobile device and storing the location information in a storage device. One or more activity patterns may include travel routes, places visited, purchase processing, and financial portfolio processing. The generation may include retrieving personal information associated with the user, generating one or more activity pattern features based on the personal information, and associating the one or more activity patterns with the features. Personal information may include purchase history data, personal interest data, demographic data, social networking data, mobile device usage data, financial portfolio data, or any combination thereof.

  In other embodiments, one or more activity patterns may be compared to a second user's second activity pattern to determine a match between the one or more activity patterns and the second activity pattern. A message can be sent that indicates the presence of a match between the one or more activity patterns of the user and the activity pattern of the second user.

  In some embodiments, the message may include the location of the second user. The message can be sent to a mobile device application, a website, an Email account, an instant messaging service, or any combination thereof. The message can be generated in response to receiving a user query. The message may include a graphical representation of each contrasting user's location.

  In another embodiment, the travel route safety indicator may be determined based on crime data associated with locations that are geographically close to the travel route. Crime data may include crime type, crime frequency, crime severity, crime density, and any combination thereof. Crime data may be based on law enforcement reports, news reports, user-submitted reports, or any combination thereof. You may display the safety parameter | index of the 1 or more position close | similar to a travel route. An alternative route having a higher safety index than the travel route may be provided to the user.

  In one embodiment, a purchase history can be generated that is based on processing performed by the user at the visited location. The message can be generated when the mobile device is near a location associated with the purchase process. The message may include rebate information, discount information, product information, consumption information, or any combination thereof.

  In other embodiments, alerts can be generated for one or more securities associated with financial portfolio processing. An alert may be sent to the user.

  In some embodiments, one or more activity patterns can be associated with the date and time when location information is received. Location information can be automatically received at regular intervals when the mobile device operates.

  In another aspect, a computerized method for seeking a real estate assessment on a location basis is disclosed. The current location information from the mobile device is received by the server computer. The location information includes global positioning data associated with a mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. A street address based on the location information is obtained by a server computer. Photo data is processed along with global positioning data, where the photo data is used to determine the street address. Financial data associated with the street address is received.

  In another aspect, a system for requesting real estate assessment on a location basis is provided. The server computer is configured to receive current location information from the mobile device. The location information includes global positioning data associated with a mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. A street address based on the location information is obtained by a server computer. Photo data is processed along with global positioning data, where the photo data is used to determine the street address. Financial data associated with the street address is received.

  In another aspect, a system for requesting real estate assessment on a location basis is provided. The system includes means for receiving current location information from the mobile device. The location information includes global positioning data associated with a mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. The system includes means for determining a street address based on the location information. Photo data is processed along with global positioning data, where the photo data is used to determine the street address. The system includes means for receiving financial data associated with the street address.

  In another aspect, a computer program product recorded on a computer readable storage medium for location based real estate assessment is provided. The computer program product includes instructions that cause the data processing apparatus to operate to receive current location information from the mobile device. The location information includes global positioning data associated with a mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. A street address based on the location information is determined. Photo data is processed along with global positioning data, where the photo data is used to determine the street address. Financial data associated with the street address is received.

  Any aspect may include one or more of the following features. Real estate assessments can be generated based on financial data. Real estate assessments can generate relative comparison values compared to assessments of similar real estate.

  The financial data may include sales price, estimated tax amount, assessment amount, proof of ownership, or any combination thereof. Processing the photographic data may include determining a compass orientation of the photographic data. Processing the photographic data may include identifying text in the photographic data. The real estate assessment can be transmitted to the remote device. A financial portfolio associated with the property owner may be retrieved.

  When the current location is destroyed, photo data for one or more neighborhoods can be received, where the photo data is used to determine the street address of the destroyed location. Photo data associated with one or more locations can be received, where the photo data is used to generate a list of real estate associated with the one or more locations. A list of real estate can be sorted according to conditions specified by the user of the mobile device. Photo data can be acquired by a device other than the mobile device. Photo data can be transmitted to a mobile device via a communication network.

FIG. 1 is a block diagram of an exemplary system for location-based information readout and aggregation for mobile devices. FIG. 2 is a workflow diagram of an exemplary method for mobile device activity pattern generation and tracking. FIG. 3 is a schematic diagram of tracking location information over a period of mobile device activity pattern generation and tracking. FIG. 4 is an exemplary acquired image of a message sent from the server to the user's mobile device, showing the presence of a match between the activity patterns of the first user and other users. FIG. 5 is an exemplary acquired image of a message sent from the server to the user's mobile device, showing the user's travel route activity pattern with associated safety indicator values. FIG. 6 is an exemplary acquired image of a message sent from the server to the user's mobile device, showing the visited site activity pattern along with purchase history and discount information. FIG. 7 is an exemplary acquired image of a message sent from a server to a user's mobile device, showing a financial portfolio processing activity pattern along with financial securities (eg, holdings) alerts and financial portfolio information. FIG. 8 is a flowchart of an exemplary method for determining location-based real estate assessment using the system.

  Generally speaking, the techniques described below encompass methods and apparatus for location-based information retrieval and aggregation for mobile devices. This technology relates to tracking location information associated with a user's mobile device and generating activity patterns based on the tracking, and to location-based addressing and real estate assessment. FIG. 1 is a block diagram of an exemplary system 100 for location-based information retrieval and aggregation for mobile devices. The system 100 includes a mobile computer 102, a communication network 104, a server computer 106, and a data source 108. The server 106 includes a mobile communication module 110 and a mobile data aggregation module 112. The server 106 and the data supply source 108 can exist in the same physical area, but may be distributed in different physical areas. Server 106 and data source 108 can reside on the same physical device, but can also be distributed on different physical devices. The server 106 and the data supply source 108 can communicate via a communication network, for example, the communication network 104.

  The mobile computer 102 is a device that transmits location information to the server computer 106 and receives messages and other information related to activity patterns. The location information can include several elements such as location coordinates, date and / or time stamp, an identifier of the device providing the location information, and the like. Each transmission by the mobile device 102 may include a single location information input (location information entry) or multiple location information inputs. The mobile device 102 can transmit location information in real time, or the mobile device 102 maintains location information locally and transmits location information in a batch mode (eg, once at the end of the day). be able to.

  Specific examples of the device include, but are not limited to, a global positioning system (GPS) device, a smartphone, a portable video game system, an internet device, a personal computer, or the like. In certain embodiments, mobile device 102 can be incorporated into a vehicle. The mobile device 102 can be configured to include an integrated digital camera device and a storage module (eg, flash memory) that stores photos, videos or other information acquired by the camera. Mobile device 102 includes network-interface components that allow a user to connect to a communication network 104 such as the Internet. The mobile device 102 also includes application software that allows the user to view messages and other information received from the server computer 106. In one embodiment, the application software is browser software such as Microsoft Internet Explorer or Mozilla Firefox. In other examples, the application is an instant message application (eg, AOL Instant Messenger), a short message service (SMS) application, or other social media application (eg, Twitter). In other embodiments, the application may be a dedicated application programmed to perform any of the functions disclosed herein.

  The server computer 106 communicates with the mobile device 102 via a communication network, for example, the communication network 104. The server 106 includes a mobile communication module 110 and a mobile data aggregation module 112. Mobile communication module 110 provides a data interface between mobile device 102 and server 106. The mobile communication module 110 receives location information from the mobile device 102. The mobile data aggregation module 112 is associated with location information such as location coordinates, an identifier (eg, a MAC address) associated with the mobile device 102, a date, and / or a time stamp to form an activity pattern associated with the mobile device 102. Based on the data elements, the received location information can be tracked and categorized. The mobile data aggregation module 112 can analyze and categorize one or more location information inputs at a time. For example, if a single location information input includes location coordinates, date and time stamp, the mobile data aggregation module 112 can classify the location information input as a visited location. In another embodiment, the mobile data aggregation module 112 can analyze several location information inputs at different locations in a day. The mobile data aggregation module 112 can classify the position information input as a travel route, for example. The mobile communication module 110 can send a message to the mobile device 102 based on one or more generated activity patterns.

  The communication network 104 channels communication from the mobile device 102 to the server 106. The communication network 104 may be a local network such as a LAN, or a wide area network such as the Internet or the World Wide Web. The communication network 104 may utilize satellite communication technology. For example, the mobile device 102 transmits and receives information via a communication link to a satellite and travels around to communicate with the server 106. Mobile device 102 and server 106 may transmit data using standard transmission conventions such as XML, HTTP, HTTPS, SMS, or other similar data communication technologies.

  Data source 108 stores tracking data associated with mobile device 102. The tracking data may include location information and / or location information. The data source 108 can also hold data related to the user, such as demographic data / demographic data. Although one data source 108 is disclosed, multiple data sources may be in the system 100. The data supply source may be a database application host computer. In other embodiments, the data source 108 may be a data feed received from various commercial and / or government agencies, collecting and obtaining the necessary data readable by the server 106. Make it possible.

  FIG. 2 is a flowchart of an exemplary method for generating and tracking an activity pattern of a mobile device. The mobile communication module 110 of the server 106 receives location information from the mobile device 102 via the communication network 104 (202). Server 106 stores the location information in a storage device, eg, data source 108 (204). The mobile data aggregation module 112 of the server 106 tracks location information over a period of time and determines the occurrence of recurring activity based on this tracking (206). The mobile data aggregation module 112 generates an activity pattern based on one or more types of repetitive activities (208). The mobile communication module 110 sends a message to the mobile device 102 based on the activity pattern (210).

  The location information is associated with the mobile device 102. The location information can include geographic coordinates generated by the GPS function of the mobile device 102. For example, the mobile device 102 can send the current position coordinates of the device to the server 106.

  The location information can be automatically transmitted from the mobile device 102 to the mobile communication module 110. For example, when the mobile device 102 detects a change in its own position (that is, when the position determination coordinate changes), the mobile device 102 can transmit the position information to the mobile communication module 110, and any operation that results in operation of the mobile device. Does not require the user to enter information. In certain embodiments, the mobile device 102 can transmit location information to the mobile communication module 110 at regular time intervals. For example, the mobile device 102 can be configured to transmit location information at 5-minute intervals. In other embodiments, the mobile device 102 can transmit location information based on an operation by a user. For example, a user can make a call using the mobile device 102. When a user dials a phone number or presses a button to initiate a call, the mobile device 102 can send location information to the server 106.

  In another embodiment, the position information can be transmitted from the mobile device 102 to the mobile communication module 110 based on the reception of the position information request from the mobile communication module 110. The request may be initiated by the mobile communication module 110 when the mobile data aggregation module 112 receives data from a data source, eg, the data source 108. For example, the mobile data aggregation module 112 can receive credit card processing data from the data source 108 indicating that the user has just completed a purchase using a credit card. Next, the mobile communication module 110 can initiate a request for location information from the mobile device 102, for example, to determine the location of the user's purchase. The mobile device 102 can return location information to the mobile communication module 110, which can be stored for activity pattern generation by the mobile data aggregation module 112.

  Once the location information is received by the mobile communication module 110, the server 106 can store the location information in a data source, eg, the data source 108. Server 106 may store data elements associated with location information, such as location coordinates, identifiers (eg, MAC addresses) associated with mobile device 102, dates, and / or timestamps. Server 106 may store a separate data input for each receipt of location information. After the server 106 stores multiple location information inputs, the mobile data aggregation module 112 can generate an activity pattern by tracking the mobile device 102 and analyzing the location information. The analysis of location information can be done, for example, by performing comparisons, scans, and other data evaluation techniques. The analysis is performed by utilizing specific database routines and commands, or by executing generic and unique sequences using diversified database commands.

For example, the mobile data aggregation module 112 can determine that the location of the mobile device 102 has changed multiple times during a single time period (eg, one day). FIG. 3 is a diagram of tracking location information over a period of time to generate and track an activity pattern of a mobile device. At t 0 on the first day, the mobile device 102 associated with the user is located at the first location 302a. The mobile device 102 transmits a location information input 304a including information related to the first location 302a to the server 106. The mobile communication module 110 stores the location information input 304 a in the data source 108. Then, when t 1, mobile device 102 is moved to the second position 302b. The mobile device 102 sends the location information input 304b associated with the second location 302b to the server 106, and the mobile communication module 110 stores the information 304b. Finally, when t 2, mobile device 102 is moved to the third position 302c, the mobile device 102 transmits the location information input 304c associated with the third position 302c, which is followed by the mobile communication module 110 Is stored. On the second day of the next day, at t ′ 0 , the mobile device 102 is located at the first position 302a. The mobile device 102 transmits the position information input 304 a ′ to the server 106. The mobile device 102 moves positions in the same order (eg, 302b and 302c), and at each position, the mobile device 102 sends position information inputs (eg, 304b ′ and 304c ′) to the server 106. .

  After the server 106 receives the location information input 304c ′ for the third location 306, and after the second day, the mobile data aggregation module 112 determines that based on all the different location information inputs 304a-c and 304a′-c ′, It is possible to determine (determine) the occurrence of repetitive activities (for example, travel routes).

  In some embodiments, the mobile data aggregation module 112 may analyze the location information input of the mobile device for longer or shorter time periods to determine the occurrence of repetitive activity and generate an activity pattern. . For example, the mobile data aggregation module 112 may determine the occurrence of repetitive activities based on the location information input collected over a week. The mobile data aggregation module 112 may determine the occurrence of recurring activity in many different time periods, for example, hour, day, month, year, and the mobile data aggregation module 112 may determine which of the different time periods. Or, all related activity patterns can be generated.

  In some embodiments, the mobile data aggregation module 112 may be based on two or more location information inputs or groups of inputs (entries) associated with mobile devices that share one or more common features, eg, the mobile device 102. An activity pattern can be generated. The common feature may be based on individual location information inputs, eg, data elements of inputs 304a-c and 304a'-c '. For example, if several different location information entries indicate that a user visits a specific place (eg, a main street coffee shop at 7:45 am) at the same time every week in January, the mobile data aggregation module 112 can utilize these common features to determine that the user's activity pattern includes a location data element and a time stamp data element. Server 106 may store activity patterns in a data storage device, eg, data source 108.

  The mobile data aggregation module 112 can incorporate the time stamp of the location information input into the determination of the activity pattern. For example, the mobile data aggregation module 112 may share a common location and require that the mobile device 102 be associated with location information input that occurs daily and at approximately the same or same time each day. The mobile data aggregation module 112 may be used, for example, to narrow the scope of an activity pattern to a specific time of a day, or other information related to the mobile device 102 or user based on the time of the day when other information is stored. When reading out, the time stamp can be used to determine the activity pattern.

  The mobile data aggregation module 112 can also increase the activity pattern by reading additional information related to the user from various data sources. Additional information may include purchase history, personal interests or preferences, user demographics, social networking information, mobile device usage information, financial portfolio data, or other similar data related to the user. Following the example above, the mobile data aggregation module 112 can read the activity pattern associated with the user visiting the main street coffee shop at 7:45 am and apply additional information about the user to the activity pattern. can do. For example, the mobile data aggregation module 112 can communicate with a data source (eg, 108) that stores the user's bank account information, and all debit cards made by the user at a main street coffee shop in the past month. Processing can be read. The mobile data aggregation module 112 can extract debit card processes having a time stamp at or near 7:45 am, and the mobile data aggregation module 112 can associate these processes with activity patterns.

  An example of a data source may be a user profile. In some embodiments, the user profile can include various types of information about the user (eg, demographics, finance, hobbies, etc.) and a user predefined relationship (eg, Associated with a particular input (entry) with (Fidelity Investments). For example, the user profile can include detailed information about the account. In other examples, the user profile may include information from third party sources such as, for example, credit card companies, banks, social networking websites, email services, and the like. The user profile can include information entered by the user and information read from internal and / or external data sources. The user profile can be configured by the user via a network application (eg, a web page). Users can log in, update their user profile, and adjust what kind of data the mobile data aggregation module 112 can access. For example, the user logs into his fidelity account page and verifies that the fidelity account is associated with two credit cards (eg, one for his use and one for his wife's use). be able to. By configuring their user profile, users can only see information associated with their use credit card when the mobile data aggregation module 112 generates an activity pattern.

  The mobile data aggregation module 112 can categorize activity patterns based on activity types. For example, as shown in FIG. 3, if the location information input indicates that the mobile device 102 has visited the same location many times over a period of days, the mobile data aggregation module 112 can determine the location coordinates of each input. Can be evaluated to determine that the coordinates are close to each other, and the position information input can be analyzed. Based on this analysis, the mobile data aggregation module 112 determines that the mobile device 102 has visited a particular location (eg, the first location 302a) more than once over a predetermined period. Next, the mobile data aggregation module 112 can determine that the frequent visits of the mobile device 102 are visits of the first location 302a, and thus the mobile device 102 associated with the first location 302a. Generate activity patterns.

  In another example, the mobile data aggregation module 112 can determine whether the activity pattern is a purchase process. For example, if the mobile device 102 visits several different locations this week and the location information input indicates that each location is associated with a particular franchise store, the mobile data aggregation module 112 analyzes the location information input. Then, it is determined that the mobile device 102 has visited several different locations (eg, first location 302a, second location 302b, etc.) over a predetermined period of time. The mobile data aggregation module 112 can increase the location information input by determining that the visited locations share a common feature. For example, all locations include the franchise locations of nationwide coffee shop chains. The mobile data aggregation module 112 can further augment the location information input, for example, by retrieving purchase processing data associated with the user's bank account based on associating the date and time of processing with the input. Next, the mobile data aggregation module 112, for example, when a user visits a particular coffee shop in the franchise, is always between $ 2.5 and $ 4.00, (i) medium coffee and One can seek to purchase either tonuts or (ii) medium sized latte and bagels. The server 106 generates an activity pattern based on this activity.

  In another example, the mobile data aggregation module 112 can determine that the activity pattern is a travel route. For example, if the location information input indicates that the mobile device 102 has visited several different locations in the same order over the past three days, the mobile data aggregation module 112 analyzes the location information input to analyze the mobile device 102. Determine that they visited a particular set of locations that share approximate location coordinates in the same order every day. As shown in FIG. 3, the mobile data aggregation module 112 is configured so that the position information input 304a-c of the mobile device 102 is continuously changed from the first position 302a to the second position 302b over the first day. Can be determined to have moved. For example, the position information input 304a′-c ′ of the mobile device 102 on the second day immediately after the first day indicates that the mobile device 102 has continuously moved from the first position 302a to the second position 302b to the third position 302c. May indicate that. The mobile data aggregation module 112 analyzes the first and second day location information inputs 304a-c and 304a'-c 'to determine that the mobile device 102 travels the same travel route every day. Next, the mobile data aggregation module 112 can determine that the repetitive activity of the mobile device 102 is a travel route from the first location 302a to the second location 302b to the third location 302c, and is associated with the travel route. An activity pattern of the mobile device 102 is generated.

  The mobile data aggregation module 112 can increase the location information input of the travel route activity pattern by reading demographic data or personal preference data associated with the user of the mobile device. For example, the user provides personal preference data indicating that it is an activity that he likes jogging in advance. The mobile data aggregation module 112 can apply preference jogging to travel routes to generate activity patterns associated with jogging and travel routes.

  Once the activity pattern is generated, the mobile data aggregation module 112 compares the activity pattern with the activity pattern of the second user to determine whether each activity pattern matches. The comparison is based on features associated with the activity pattern, for example location information or time stamp information. The comparison can include an analysis of additional information related to the user, such as demographic data or personal preference data. Each activity pattern can be compared to determine the degree of approximation of the two activity patterns. In some embodiments, the comparison is weighted to emphasize certain features or data, thereby producing different degrees of matching based on the weighting.

  The mobile data aggregation module 112 determines whether there is a match between the activity patterns of each of the user and the second user, and the server 106 sends a message indicating the presence of the match (correspondence) to one or both users. can do. Messages can also provide more detailed information related to activity patterns and similarities between patterns. In other embodiments, the user can provide a query to the server 106 and request the location of other nearby users who share the user's activity patterns and / or other information. For example, a user can issue a query by pressing a button or operating the mobile device 102 in some other way.

  Following the above example, the mobile data aggregation module 112 indicates that the second user has an individual preference that the favorite activity is jogging, and the second user is associated with a travel route near that user's travel route. Ask for that. The mobile data aggregation module 112 can determine a match for each activity pattern based on similar travel routes and shared favorite activities.

  FIG. 4 is an exemplary acquired image of a message sent by the mobile communication module 110 to the user's mobile device 102, showing the presence of a correspondence between the first user and other users' activity patterns. The message can include any or all locations of the first user and other users. For example, in determining the presence of a match, the mobile communication module 110 can message the first user's location 402 (shown as a hollow dot) in a graphical representation (eg, a street map shown in FIG. 4). The first user's location 402 can include a text message 406 identifying the first user. The message can also include a graphical representation of other users close to the user 402 (eg, shaded dots 404), where the other users are associated with an activity pattern that matches the user's activity pattern. The message can include a text message (not shown) that identifies each other user. For example, the mobile data aggregation module 112, like the user 402, can seek other users 404 that enjoy jogging and run on similar daily routes. The mobile communication module 110 includes the location of these other users 404, along with other information or features about the user 404 that would be common with the first user 402, such as a favorite hobby, etc. You can send a message to be displayed. The message may also include a shared activity pattern reference 410. As a result, the user 402 can immediately grasp other users 406 having a common activity pattern and close to the position of the user 402.

  Messages can be sent to a number of different devices or accounts associated with the user. For example, the mobile communication module 110 can send a message to the user's mobile device 102. In other embodiments, the mobile communication module 110 can send a message to a website, an Email account, an instant messaging service, or other similar application. A user can interact with any of these applications to receive messages from the mobile communication module 110.

  In another embodiment, the mobile data aggregation module 112 allows other users 408 (shown as solid dots) to be associated with the same activity pattern as the first user 402, but It can be determined that it is associated with a contradictory feature. For example, when indicating that a first user 402 prefers a sports team, another user 408 may prefer a rival sports team that is disgusting with the team of the first user 402. The mobile communication module 110 can also include the other user's location 408 in the same message, and the first user 402 can know to avoid the other user 408.

  In other embodiments, before the mobile communication module 110 sends a message to the user's mobile device 102, the mobile data aggregation module 112 needs to determine the presence of a match between the activity patterns of the first user and the second user. Absent. The mobile communication module 110 can send a message to the user's mobile device 102 based solely on activity patterns associated with the user, such as a visited location or purchase process.

  In one embodiment, the mobile data aggregation module 112 determines that the activity pattern is a travel route, and the mobile data aggregation module 112 generates a safety indicator based on crime data associated with locations that are geographically close to the travel route. The mobile communication module 110 can send a message to the user's mobile device 102 and the travel route is displayed on the screen along with the safety indicator value. FIG. 5 is an exemplary acquired image 500 of a message sent by the mobile communication module 110 to the user's mobile device 102, showing the travel route 502 of the user's activity pattern, along with associated safety indicator values 504a-e. In determining the safety values 504a-e, the mobile data aggregation module 112 can retrieve the travel route activity pattern associated with the user. The mobile data aggregation module 112 can contact a data source 108 that includes criminal data relating to locations close to the user's travel route. Crime data can include crime type, crime frequency, crime severity, crime density, or any combination thereof. Crime data can be based on law enforcement reports, news reports, user submitted reports, government reports, or other similar statistics or data. For example, the mobile data aggregation module 112 can retrieve crime data from a database maintained by the FBI or the United States Department of Justice.

  Once the mobile data aggregation module 112 retrieves crime data, the mobile data aggregation module 112 can generate a safety indicator for each geographic location based on the crime data. Various data elements, including criminal data, can be weighted by the mobile data aggregation module 112 based on a number of different dedicated and non-dedicated metrics. The safety indicator may be a numerical value, a letter character, or other identifying character. A safety indicator can be associated with a single geographic location (eg, street or neighborhood), or a safety indicator can be associated with multiple geographic locations (eg, travel routes). Safety indicators can be generated using a relative scale, i.e., different safety indicators related to individual geographic locations close to the user's overall travel route are compared in each, and which safety indicator is `` highest '' "(That is, the safest), and the index is given the highest value. The remaining safety indicators associated with individual geographic locations are given lower values in comparison. Instead, the server 106 can independently generate safety indicators for individual geographic locations by basing the generation only on crime data relating to these locations. The mobile data aggregation module 112 may also generate a safety indicator for the entire travel route by analyzing individual safety indicators for geographic locations close to the travel route.

  After generating the safety indicator or index, the mobile communication module 110 may identify the user travel route activity pattern (represented by the shaded path 502) and safety indicators 504a at various locations geographically close to the travel route 502. A message containing a graphical representation of e can be sent to the user's mobile device 102. For example, the portion of the user's travel route 502 along the main street to Smith Road can be represented by the A safety indicator 504a (indicating a safe area), and the portion of the user's travel route from Smith Road to Cedar Street is It can be expressed by the C safety indicator 504c (indicating a slightly non-safe area). Mobile data aggregation module 112 can generate an overall safety indicator for the user's travel route (eg, 'C' safety indicator 512), mobile communication module 100 can send a text message 510, or The mobile data aggregation module 112 can generate safety indicators 504a-e for various locations of the user's travel route 502. As a result, the user can immediately grasp which area is safer than other areas and the safety of the entire travel route 502.

  In some embodiments, the message may include an alternative travel route 506 (represented by a solid gray path 506). The mobile data aggregation module 112 may determine that an alternative route close to the activity pattern travel route 502 has a higher (ie, safer) safety indicator than the user's current activity pattern. For example, the mobile data aggregation module 112 can generate the safety indicator 504b of “A +” in an area along the Smith road between the main street and the first street. The mobile data aggregation module 112 determines that the alternative route 506 is more secure than the user's current route, and the mobile communication module 110 passes the alternative route 506 to the user's mobile device 102 as part of the overall safety indicator message. Can be sent. In some embodiments, the user can request an alternate travel route from the server 106 by pressing a button on the mobile device 102 or input utilizing a generated user interface. In other embodiments, the device 102 can execute conventional applications for displaying and manipulating travel routes and position data. In yet other embodiments, the mobile communication module 110 can automatically include an alternative travel route 506 in the message to the mobile device 102.

  In another example, the mobile communication module 110 can send a message to the user's mobile device 102 based solely on the activity pattern associated with the user, where the activity pattern is categorized as a visited location. . FIG. 6 is an exemplary acquired image 600 of a message sent to the user's mobile device 102 by the mobile communication module 110 showing the activity pattern of the visited location along with the purchase history 608 and discount information 610. For example, user 602 may be associated with an activity pattern indicating that he frequently goes to a main street clothing store (represented by shaded box 604). As the user 602 approaches the store location 604, the mobile data aggregation module 112 detects the location of the user's mobile device 102 and determines that the user is associated with the visited location activity pattern for that store 604. The mobile data aggregation module 112 retrieves a user's purchase history associated with a store from a data source (eg, 108), and the mobile data aggregation module 112 also retrieves current discount information associated with the store. The mobile communication module 110 sends a message including a graphical representation of the user's location (eg, a street map) along with a text message that includes the user's purchase history 608 and current discount information 610 associated with the store 604 to the user's mobile device 102. Send to. For example, the message can be sent via SMS, MMS, or Twitter (eg, “tweet”), indicating that the message has arrived when the device is in a standard manner, eg, when the user is in close proximity. The reception of the message is notified by timed vibration and / or voice signaling. In some embodiments, the notification is a special notification, such as a specific sound or pre-recorded speech that differs from the normal notification, and the user may ignore the timed message at a specific location and time. There has been no such thing.

  In other embodiments, the user may be associated with an activity pattern purchase process for the store 604. For example, a user 602 may have purchased several suits at different locations in the same chain clothing store over a period of time. When the user 602 is close to a particular store location 604 of the chain clothing store (regardless of whether the user has purchased at that individual location), the mobile communication module 110 shows the user's purchase history 608 and discount information 610. The message can be sent to the user's mobile device 102 (eg, text message or tweet).

  In another embodiment, the mobile communication module 110 sends a message to the user's mobile device 102 based on an activity pattern associated with the user, where the activity pattern is categorized as financial portfolio processing. FIG. 7 is an exemplary acquired image 700 of a message sent by the mobile communication module 110 to a user's mobile device 102, with financial portfolio processing alerts with financial securities 708 (eg, stockholdings) and financial portfolio information 710. Indicates an activity pattern. For example, assume that user 702 frequently buys and sells certain securities for a company embedded in his financial portfolio. As the user 702 approaches a position related to the company in which he / she owns shares, or alternatively, approaches a position related to the broker who manages his / her portfolio, the mobile data aggregation module 112 may The location is detected and the user determines that the location 704 is related to the financial portfolio processing activity pattern. The mobile data aggregation module 112 can apply only to a small group of securities owned by the user 702 at that location 704, or the mobile data aggregation module 112 can apply all of its user ownership to that location 704. You may decide that you can. In some embodiments, the mobile data aggregation module 112 reads the user's current holdings security for that location 704 from a data source (eg, 108), and the mobile data aggregation module 112 reads current information about the security (eg, , $ 78.00 stock price). The mobile communication module 110 sends a message to the user's mobile device 102 that includes the user's location (eg, a street map) along with a text message that indicates the current stock price 708 and the entire stock held by the user 710 associated with that location 704. Send.

  In some embodiments, the message also includes a prompt 712 that allows the user to follow a number of actions related to their financial portfolio. For example, a message can obtain 100 FDY stock by continuously pressing '# 22' on the mobile device 102. In some embodiments, user actions can include pressing a button, reading a word or phrase, or user input to other similar mobile devices 102. If the user presses in the necessary procedure, the mobile device 102 sends an instruction to sell the stock to the server 106. Server 106 sends a request to a data source regarding the user's financial portfolio, which then performs the desired processing.

  In other embodiments, the mobile data aggregation module 112 can collect and analyze location information from the mobile device 102 to determine a real estate assessment regarding the current location of the mobile device 102. FIG. 8 is a flowchart of an exemplary method 800 for determining location-based real estate assessment utilizing system 100. The server 106 receives position information and photo data from the mobile device 102 (802). Photo data can be collected utilizing a camera embedded in the mobile device 102 or separate from the mobile device 102 and utilizing a camera connected to the mobile device 102 via a communication link or cable. Can be obtained. The mobile data aggregation module 112 analyzes the photo data associated with the location information (804). The mobile data aggregation module 112 determines a street address for the photo data and the position information (806). The location information can include global positioning data generated by the mobile device 102 utilizing techniques known in the art. The mobile data aggregation module 112 reads financial data associated with the street address (808). The financial data can include sales prices, estimated tax amounts, appraisals, proof of ownership, or any combination thereof. The mobile data aggregation module 112 generates a real estate assessment based on financial data associated with the street address and transmits the value and / or the financial data to the mobile device 102 (810). Server 106 may maintain the value of the data source (eg, 108) for future reference.

  The mobile data aggregation module 112 determines the location received from the mobile device 102 and the street address associated with the photo data by comparing the photo data with one or more data sources including identification information. For example, the mobile communication module 110 receives a picture of a building located on 12 main streets from the user's mobile device 102 along with world location information about the current location of the mobile device. The mobile data aggregation module 112 accesses a data source containing detailed information regarding the relationship between the positioning coordinates and the physical street address to process the GPS data. Examples of such data sources include Google's Google (R) Maps and Tele Atlas's MultiNet (R). Once the mobile data aggregation module 112 determines the location, the mobile data aggregation module 112 can process the photos as well, for example, providing data information including relationships with photo information and street addresses and / or location data. Compare the source with the photo.

  In one embodiment, the mobile data aggregation module 112 can take advantage of the compass orientation of the photo to determine the street address. For example, if a photo is taken by the mobile device 102 from the west toward the target building, the mobile data aggregation module 112 matches the west photo of the building from the data source with the photo to determine a specific street address.

  In other embodiments, the mobile data aggregation module 112 can keep track of text and numbers in photos and determine street addresses. For example, if the number '229' is pasted and fixed to the target building and is included in a photograph taken by the mobile device 102, the mobile data aggregation module 112 uses the text recognition module to The number can be extracted and analyzed. The mobile data aggregation module 112 may include a street address identification number.

  In other embodiments, when the location where the property assessment is desired cannot be properly photographed (e.g., the location is destroyed by severe weather), the mobile communication module 110 is placed in the destroyed location. Photo data associated with a close location can be obtained and the photo data can be analyzed to determine the street address of the destroyed location. For example, an insurance claim assessor is investigating the presence of tornado damage. Assessors need to determine property prices for houses destroyed by the storm and process insurance claims. The assessor finds that the two houses near the destroyed house remain largely healthy. The assessor obtains two home photos and a broken home photo with the mobile device 102 and transmits the photos and GPS data to the mobile communication module 110. The mobile data aggregation module 112 analyzes the photos and GPS data in the manner described above, and the mobile data aggregation module 112 asks for one photo to correspond to the street address of 5 main streets and the other photo (broken house) ) Is indistinguishable, and the third photo is required to correspond to the street address of 9 main streets. The mobile data aggregation module 112 processes the compass orientation and location information associated with the broken house photo and concludes that the street address is 7 main streets. In one embodiment, the mobile data aggregation module 112 reads financial data associated with each street address and generates a real estate assessment for each property. In another embodiment, the mobile data aggregation module 112 retrieves financial data related only to broken street addresses and generates a real estate assessment for the property. The mobile communication module 110 then transmits the value (s) to the assessor's mobile device 102.

  The mobile data aggregation module 112 may generate a real estate assessment generated from the current location of the mobile device 102 and similar real estate values read from a data source (eg, 108) to generate relative comparison values between the various real estates. Compare For example, as part of generating a real estate assessment of the requested street address, the mobile data aggregation module 112 may be similar to the assessment of the requested street address in this region (eg, close to recently sold physical features). It automatically compares with real estate and seeks an expected assessment or similar assessment of the desired street address.

  In another embodiment, the mobile communication module 110 receives photos of multiple buildings in the area and generates a list of for sale or recently sold properties located within these buildings. For example, the mobile communication module 110 receives photos of several apartment buildings on the main street taken by the mobile device 102 with location information. In some embodiments, the mobile communication module 110 also receives certain survey conditions (eg, only two bedroom apartments) from the mobile device 102. The mobile data aggregation module 112 analyzes the photos and location information and determines that the building is located on 24 main street and 26 main street. Alternatively or in addition to providing a real estate assessment for the building, the mobile data aggregation module 112 generates a list of available or vacant apartments for each building that meets the conditions received from the mobile device 102. The list can be returned to the mobile device.

  In other embodiments, the mobile data aggregation module 112 can retrieve financial information, such as a financial portfolio associated with a recorded or identified street address owner. For example, as part of generating a real estate assessment for a requested street address, the mobile data aggregation module 112 may provide information about the owner of the real estate located at that street address, if that information is available from a data source. Can be read out. Owner information can include name, address, telephone number, capital, personal asset value, and financial portfolio information. In other embodiments, street address information can be used in combination with the safety indicator calculation described above. The mobile data aggregation module 112 can generate a safety indicator relating to the real estate or street address, and the mobile communication module 110 can send a message including the evaluation information and the safety indicator to the mobile device 102. For example, if a user is interested in purchasing a particular home in addition to financial information about that address, a safety rating may also be important to the user in determining whether to make that purchase.

  The systems and methods described above may be executable by digital electrical circuitry, computer equipment, firmware, and / or software. Its execution may be done as a computer program product (ie, a computer program recorded on a computer readable storage medium). The execution may be done, for example, by machine-readable storage devices and / or execution propagation signals that control the operation of the data processing device. Its execution is, for example, made by a programmable processor, a computer, and / or a plurality of computers.

  A computer program may be written in any programming language, including compiled and / or translated languages, which may be a stand-alone program or subroutine, element, and / or other suitable for use in a computing environment. It can be deployed in a manner that includes units. A computer program can be deployed to be executed by one computer or multiple computers at one site.

  The method steps may be executable by one or more programmable processors that execute the functions of the present invention by executing a computer program and generating output by processing input data. The method steps can be performed by special purpose logic circuits and the apparatus can also be configured. The circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), and / or other discrete circuits configured to perform the desired function. It can be. Modules, subroutines, and software agents may refer to portions of a computer program, a processor (built into a computer such as a server computer), specific circuitry, software, and / or hardware that performs that function.

  Processors suitable for the execution of computer programs may include, by way of example, general and special purpose microprocessors and any one or more of any type of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor that executes instructions and one or more storage devices for storing instructions and data. Generally, a computer may include receiving data from and / or transmitting data for data storage to one or more aggregate storage devices (eg, magnetic, magneto-optical disk, or optical disk). Operably coupled to it.

  Data transmission and commands can also occur over a communications network. Computer readable media suitable for recording computer program instructions and data may include all aspects of non-volatile memory including semiconductor memory devices as an example. The computer readable medium can be, for example, an EPROM, EEPROM, flash memory device, magnetic disk, internal hard disk, removable disk, magneto-optical disk, CD-ROM, and / or DVD-ROM disk. The processor and memory may be supplemented and / or incorporated within a special purpose logic circuit.

  In order to provide user interaction, the techniques described above may be executable on a computer having a display or transmitter. The display device can be, for example, a cathode ray tube (CRT) and / or a liquid crystal display (LCD) monitor. User interaction is, for example, displaying information to the user and a keyboard and pointing device (eg, a mouse or trackball) by which the user can provide input to the computer (eg, a user interface Interaction with the element). Other types of devices may be used to provide user interaction. Other devices may include, for example, feeding back any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback) to the user. User input may be receivable in a manner that includes, for example, acoustic, speech, and / or tactile input.

  The client device and the computer are, for example, a computer, a computer having a browser device, a telephone, an IP phone, a mobile device (for example, a mobile phone, a personal digital assistant (PDA) device, a smart phone, a laptop computer, an electronic mail device). And / or other communication devices. The browser device is, for example, a computer having a world wide web browser (for example, Microsoft (registered trademark) Internet Explorer (registered trademark) available from Microsoft Corporation, Mozilla (registered trademark) Firefox available from Mozilla Corporation). Desktop computer, laptop computer). The mobile calculator may include, for example, Blackberry (registered trademark).

  The web server is, for example, a server module (for example, Microsoft® Internet Information Service available from Microsoft, Apache web server available from Apache Foundation, Apache Tomcat web server available from Apache Software Foundation). ).

  The techniques described above can be implemented in a distributed computing system that includes a backend element. The backend element can be, for example, a data server, a middleware element, and / or an application server. The techniques described above may be executable on a distributed computing system that includes a front-end element. The front-end element can be, for example, a client computer having a graphical user interface, a web browser through which the user can interact with the exemplary implementation, and / or other graphical user interfaces for the transmitter. The components of the system can be interconnectable by any aspect or medium of digital data communication (eg, a communication network).

  The system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises thanks to computer programs that each run on a computer and each have a client-server relationship.

  The communication network described above may be implemented by a packet based network, a circuit based network, and / or a combination of a packet based network and a circuit based network. Packet-based networks include, for example, the Internet, carrier Internet protocol (IP) networks (for example, local area networks (LAN), wide area networks (WAN), campus area networks (CAN), urban area networks (MAN), home area networks) (HAN)), private IP network, IP private branch exchange (IPBX), wireless network (eg, radio access network (RAN), 802.11 network, 802.16 network, general packet radio service (GPRS) network, hyper LAN) And / or other packet-based communication networks. Circuit-based networks include, for example, public switched telephone networks (PSTN), private branch exchanges (PBX), wireless networks (eg, RAN, Bluetooth, code division multiple access (CDMA) networks, time division multiple access (TDMA) networks, It may include a global system for mobile communications (GSM network) and / or other circuit-based networks.

  The aspects comprising, including, and / or each of the aspects are non-exclusive and include enumerated elements and may include additional elements not enumerated. “And / or” is non-exclusive and includes one or more of the listed elements and includes combinations of the listed elements.

  Those skilled in the art will recognize that the invention can be practiced in other specific embodiments without departing from the spirit or essential characteristics of the invention. Accordingly, the foregoing embodiments will be understood as a description of all aspects thereof rather than limiting the invention disclosed herein.

100: System 102: Mobile device 104: Communication network 106: Server computer 108: Data supply source 110: Mobile communication module 112: Mobile data aggregation module

Claims (40)

  1. A computerized method for generating and tracking activity patterns associated with mobile devices, comprising:
    Location information from a mobile device associated with the user, received by the server computer based on the GPS information transmitted from the mobile device,
    Tracking the location information over a period of time,
    Determine the occurrence of repetitive activities based on the tracking,
    A method of generating one or more activity patterns associated with the mobile device, wherein the one or more activity patterns are based on the occurrence of the repetitive activity.
  2.   The method of claim 1, wherein tracking the location information includes receiving the location information from the mobile device and storing the location information in a storage device.
  3.   The method of claim 1, wherein the one or more activity patterns include a travel route, a visited place, a purchase process, or a financial portfolio process.
  4. The generation further comprises:
    Read personal information related to users,
    Generating one or more features of the activity pattern based on the personal information;
    The method of claim 1, comprising associating the feature with one or more of the activity patterns.
  5.   The method of claim 4, wherein the personal information comprises purchase history data, personal interest data, demographic data, social networking data, mobile device usage data, financial portfolio data, or any combination thereof.
  6.   The method of claim 1, further comprising comparing one or more of the activity patterns with a second activity pattern of a second user to determine a match between the one or more of the activity patterns and the second activity pattern.
  7.   The method of claim 1, further comprising sending a message indicating the presence of a match between the one or more activity patterns of the user and an activity pattern of a second user.
  8.   The method of claim 7, wherein the message includes a location of the second user.
  9.   The method of claim 7, wherein the message is sent to a mobile device application, a website, an Email account, an instant messaging service, or any combination thereof.
  10.   The method of claim 7, wherein the message is generated in response to receiving a user query.
  11.   8. The method of claim 7, wherein the messages each include a graphical representation of a contrasting user location.
  12. The method of claim 1, wherein the activity pattern includes a travel route.
    The method further includes determining a safety indicator based on crime data associated with a location geographically close to the travel route.
  13.   The method of claim 12, wherein the crime data includes crime type, crime frequency, crime severity, crime density, and any combination thereof.
  14.   The method of claim 12, wherein the crime data is based on a law enforcement report, a news report, a user submission report, or any combination thereof.
  15.   The method of claim 12, further comprising displaying a safety indicator at one or more locations proximate to the travel route.
  16.   The method of claim 12, further comprising providing an alternative route to the user, the alternative route having a higher safety indicator than the travel route.
  17.   The method of claim 1, wherein the activity pattern comprises a visited location, further comprising generating a purchase history based on processing performed by the user at the visited location.
  18.   The method of claim 1, wherein the activity pattern includes a purchase process, further comprising generating a message when the mobile device is near a location associated with the purchase process.
  19.   The method of claim 18, wherein the message includes rebate information, discount information, product information, consumption information, or any combination thereof.
  20.   The method of claim 1, wherein the activity pattern comprises financial portfolio processing, further comprising generating an alert for one or more securities associated with the financial portfolio processing and sending the alert to a user. Including.
  21.   The method of claim 1, wherein one or more of the activity patterns are associated with a date and time when the location information is received.
  22.   The method of claim 1, wherein receiving the location information includes automatically receiving location information at frequent intervals when the mobile device operates.
  23. A system for tracking activity patterns associated with mobile devices,
    Location information from a mobile device associated with a user, the location information based on GPS information transmitted from the mobile device;
    Tracking the location information over a period of time,
    Determine the occurrence of repetitive activities based on the tracking,
    A system comprising: a server computer configured to generate one or more activity patterns associated with the mobile device, wherein the one or more activity patterns are based on the occurrence of the repetitive activity.
  24. A system for tracking activity patterns associated with mobile devices,
    Means for receiving location information from a mobile device associated with a user based on GPS information transmitted from the mobile device;
    Means for tracking the location information over a period of time;
    Means for determining the occurrence of repetitive activity based on said tracking;
    Means for generating one or more activity patterns associated with the mobile device, wherein the one or more activity patterns are based on the occurrence of the repetitive activity;
    A system comprising:
  25. A computer program product for tracking activity patterns associated with a mobile device, recorded on a computer readable storage medium,
    In the data processor
    Position information from a mobile device associated with a user, the position information based on GPS information transmitted from the mobile device;
    Tracking the location information over a period of time,
    Determine the occurrence of repetitive activities based on the tracking,
    A computer program product comprising instructions for causing one or more activity patterns associated with the mobile device to be generated based on the occurrence of the repetitive activity.
  26. A computerized method for seeking real estate assessments based on location,
    A server computer that stores current position information from a mobile device, including global positioning data related to the mobile device, and one or more photos acquired by the mobile device and photo data related to the location. Received by
    By processing the photo data together with the global positioning data, a street address is determined by the server based on the location information, where the photo data is used to determine the street address,
    A method of reading financial data associated with the street address.
  27.   27. The method of claim 26, further comprising generating a real estate assessment based on the financial data.
  28.   28. The method of claim 27, further comprising the step of comparing the real estate assessment with a similar real estate assessment to generate a relative comparison value.
  29.   27. The method of claim 26, wherein the financial data includes a sales price, an estimated tax amount, an assessed amount, proof of ownership, or any combination thereof.
  30.   27. The method of claim 26, wherein processing the photographic data further comprises determining a compass orientation of the photographic data.
  31.   27. The method of claim 26, wherein processing the photographic data further comprises identifying text in the photographic data.
  32.   27. The method of claim 26, further comprising transmitting the real estate assessment to a remote device.
  33.   27. The method of claim 26, further comprising reading a financial portfolio associated with the property owner.
  34.   27. The method of claim 26, further comprising receiving further photo data relating to one or more neighborhoods when the current location is destroyed, wherein the photo data is used to determine a street address of the destroyed location. Method.
  35.   27. The method of claim 26, further comprising receiving photographic data associated with one or more locations, wherein the photographic data is used to generate a list of real estate associated with the one or more locations.
  36.   36. The method of claim 35, wherein the list of real estate is sorted according to conditions specified by a user of the mobile device.
  37.   27. The method of claim 26, wherein the photo data is acquired by a device other than the mobile device, and the photo data is transmitted to a mobile device via a communication network.
  38. A system that seeks real estate assessments based on location,
    Receiving current location information from a mobile device, including global positioning data associated with the mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. ,
    By processing the photo data together with the global positioning data, a street address is obtained based on the position information, wherein the photo data is used to obtain the street address,
    A system comprising a server computer configured to retrieve financial data associated with the street address.
  39. A system that seeks real estate assessments based on location,
    Receiving current location information from a mobile device, including global positioning data associated with the mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. Means,
    Means for determining a street address based on the location information by processing the photo data together with the global positioning data, wherein the photo data is used for determining the street address;
    Means for reading financial data associated with the street address;
    A system comprising:
  40. A computer program product recorded on a computer readable storage medium, wherein the data processing device
    Receiving current location information from a mobile device, including global positioning data associated with the mobile device, and one or more photos acquired by the mobile device and photo data associated with the location. ,
    By processing the photo data together with the global positioning data, a street address is obtained based on the position information, wherein the photo data is used to obtain the street address,
    A computer program product comprising instructions for executing reading of financial data associated with the street address.
JP2012521786A 2009-07-23 2010-07-22 Location-based information readout and analysis Pending JP2013500519A (en)

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US12/508,514 2009-07-23
US12/508,509 2009-07-23
US12/508,509 US20110022312A1 (en) 2009-07-23 2009-07-23 Generating and Tracking Activity Patterns for Mobile Devices
US12/508,514 US20110022540A1 (en) 2009-07-23 2009-07-23 Location-Based Address Determination and Real Estate Valuation
PCT/US2010/042924 WO2011011616A1 (en) 2009-07-23 2010-07-22 Location-based information retrieval and analysis

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SG (2) SG10201509984YA (en)
WO (1) WO2011011616A1 (en)

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