Abstract:
A system and method for organizing information in a personal information space. The personal information space includes at least one data source holding at least a portion of the personal information space. The system includes an agent for the data source which provides interaction information regarding data in the data source; and a interaction evaluation engine including one or more weighting characteristics for each interaction, and providing an output reflecting a weighting of one or more characteristics of the interaction. The method may comprise the steps of determining when an interaction between a contact on one of the contact data sources occurs; analyzing one or more characteristics of the interaction event to determine a at least one trait about the interaction; and generating a result based on said step of analyzing.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is directed to controlling and managing personal information, and in particular, to managing personal information in an individual user&#39;s personal information space. 
     2. Description of the Related Art 
     People increasingly manage their personal information through electronic means, such as personal digital assistants, on-line contact and calendar applications and even wireless phones. Hence, a migration of contact, document, financial, and other personal information has been made away from paper calendars, address books and records toward electronic systems. Both the type and the quantity of personal information in electronic form are growing. 
     Personal Information Managers (PIMs) generally comprise software applications running on a processing device such as a computer and personal digital assistants (PDAs). PDA&#39;s are small, electronic devices of varying types, which store reminder, contact, task, notes, and text information as well as other types of files. In the desire to increase the manageability of personal information, PIMS and PDA&#39;s have merged with cellular telephones, PIMs have migrated into online contact managers and pocket personal computers have become more and more powerful. 
     Generally, software PIMs include products such as Microsoft Outlook, Interactive Commerce Corporation&#39;s ACT!, and other similar programs are designed to run on a computer. PDA devices include devices such as those using the Palm® or Microsoft Windows Pocket PC operating systems, as well as other, more basic contact and calendar devices. Each PDA generally includes calendar, contact, personal tasks, notes, documents, and other information, while more sophisticated devices allow a user to fax, send e-mails, and communicate from within the application over a physical or wireless network. Even advanced cellular phones carry enough memory and processing power to store contact information, surf the web, and provide text messaging. Along with the growth in the sophistication of these devices, the need to transfer information between them has grown significantly as well. 
     Online personal information managers make access to data from any networked terminal possible. Many Internet web portals also now provide file storage, contact and calendar services. For example, major service portals such as Yahoo!, Excite, Lycos, MSN and others provide on-line calendar and contact manager services via a web browser to registered users. This allows a user to log in to their own calendar and address book from any Internet-capable web browsing application since the user&#39;s individual data is stored on a host server maintained by the web portal provider. Each of these services includes a data store as well. 
     All such personal information operated on and stored by a user can be considered within that user&#39;s “personal information space.” In this context, a “personal information space” is a data store of information customized by, and on behalf of the user which contains both public data the user puts into their personal space, private events in the space, and other data objects such as text files or data files which belong to the user and are manipulated by the user. The personal information space is defined by the content which is specific to and controlled by an individual user, generally entered by or under the control of the individual user, and which includes “public” events and data, those generally known to others, and “private” events and data which are not intended to be shared with others. It should be recognized that each of the aforementioned criteria is not exclusive or required, but defines characteristics of the term “personal information space” as that term is used herein. In this context, such information includes electronic files such as databases, text files, word processing files, and other application specific files, as well as contact information in personal information managers, PDAs and cellular phones. 
     Once a personal information space is defined, the challenge becomes managing information in the space particularly between different devices. For example, if an individual keeps a calendar of information on a personal computer in his or her office using a particular personal information manager application, the individual would generally like to have the same information available in a cellular phone, hand-held organizer, and perhaps a home personal computer. The individual may additionally need some characterization of the information, such as what information is more relevant or important to have in a particular location, and which people the user interacts with regularly, and how the user interacts with them. 
     Mechanisms exist for moving data between a number of devices and keeping a user&#39;s personal information on those devices current between all the devices. 
     Co-pending application Ser. Nos. 09/490,550, 09/491,675 and 09/491,694 disclose a novel method and system for synchronization of personal information including that which is conventionally found in desktop applications, personal digital assistants, palm computers, and website calendar services, as well as any content in the personal information space including file systems, contact information and/or calendaring information. Such systems can keep information on different systems in sync, but no qualitative method for evaluating the importance of the information to a user is provided. 
     Hence, a system whereby a user can automate the process of determining the importance of personal information on one or more of the user&#39;s devices based on characteristics of the user&#39;s interactions with such information would be useful. 
     SUMMARY OF THE INVENTION 
     The invention, in one aspect, comprises a system for organizing information in a personal information space. The personal information space includes at least one data source holding at least a portion of the personal information space. The system includes an agent for the data source which provides interaction information regarding data in the data source; and a interaction evaluation engine including one or more weighting characteristics for each interaction, and providing an output reflecting a weighting of one or more characteristics of the interaction. 
     In a further aspect, the invention is a method for managing a user&#39;s personal contact information from a variety of contact data sources, each data source comprising a device or application storing contact information used by the user. In this embodiment, the method may comprise the steps of determining when an interaction between a contact on one of the contact data sources occurs; analyzing one or more characteristics of the interaction event to determine a at least one trait about the interaction; and generating a result based on said step of analyzing. 
     In yet another aspect, the invention comprises one or more processor readable storage devices having processor readable code embodied on said processor readable storage devices, said processor readable code for programming one or more processors to perform a method. In such aspect, the method may comprise the steps of: determining when an interaction between a contact on one of the contact data sources occurs; analyzing one or more characteristics of the interaction event to determine a at least one trait about the interaction; and generating a result based on said step of analyzing. 
     In a still further aspect, the invention comprises a contact management method. In this aspect, the method may comprise the steps of determining interactions between a user and the user&#39;s contacts via at least one communication means; analyzing one or more characteristics of the interaction to determine at least one trait about the interaction; rating contacts based on the trait; and updating the user&#39;s contact information based on said ranking. 
     In another aspect, the invention is a system for managing a user&#39;s personal information. The system may include means for monitoring data interaction by a device or application on a per-user basis; means for evaluating the interaction based on one or more characteristics of the interaction; and means for weighing elements of the data based on said step of evaluating. 
     The present invention can be accomplished using hardware, software, or a combination of both hardware and software. The software used for the present invention is stored on one or more processor readable storage media including hard disk drives, CD-ROMs, DVDs, optical disks, floppy disks, tape drives, RAM, ROM or other suitable storage devices. In alternative embodiments, some or all of the software can be replaced by dedicated hardware including custom integrated circuits, gate arrays, FPGAs, PLDs, and special purpose computers. These and other objects and advantages of the present invention will appear more clearly from the following description in which the preferred embodiment of the invention has been set forth in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention will be described with respect to the particular embodiments thereof. Other objects, features, and advantages of the invention will become apparent with reference to the specification and drawings in which: 
         FIG. 1  is a block diagram of a first hardware system for implementing the present invention. 
         FIG. 2A  is a flowchart illustrating one embodiment of a method in accordance with the present invention. 
         FIG. 2B  is a flowchart illustrating a second embodiment of a method in accordance with the present invention. 
         FIG. 3  is a block diagram of a processing device suitable for implementing software to perform the method of the present invention. 
         FIG. 4  is a functional diagram showing functional components of a first embodiment of the present invention. 
         FIG. 5  is a diagram showing functional components of a second embodiment of the present invention. 
         FIG. 6  is a diagram illustrating an embedded embodiment of the present invention. 
         FIG. 7  is a diagram showing functional components of a third embodiment of the present invention. 
         FIG. 8  is a UML representation of a content data record for Use in the present invention. 
         FIG. 9  is a UML representation of data interaction in the system of the present invention. 
         FIG. 10  is a sequence diagram showing a first set of interactions of functional components of the present invention. 
         FIG. 11  is a sequence diagram showing a second set of interactions of functional components of the present invention. 
         FIG. 12  is a sequence diagram showing a third set of interactions of functional components of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The system and method of the present invention provide a means for a user to automatically sort and prioritize information belonging in the user&#39;s personal information space. In general, the term “smart-filtering” is used to describe the system and method which aid the user in managing personal information. However, such term should not be construed as limiting the invention to a “filter.” The invention includes mechanisms to characterize information without limiting, rejecting or altering data under consideration. In a basic embodiment, the system analyzes a user&#39;s interaction with the user&#39;s contact records, and updates devices containing those records to hold only those records used the most, or chosen by the user to be held. In more complex embodiments, the system can analyze a user&#39;s interaction with any type of data in the personal information space, and make judgments about the data based on the user&#39;s interaction with the data. The system then allows the user to manipulate the data based on this analysis in any number of ways. 
       FIG. 1  shows a block diagram overview of a system for implementing the present invention. Shown in  FIG. 1  are a smart filtering engine  100  and a smart filtering database  120 . Arrow  110  represents two-way communication between smart filtering engine  100  and smart filtering database  120 . Engine  100  and database  120  may be provided on individual processing devices such as those disclosed in  FIG. 3 , or may be provided on a single combined device. A number of applications  50 ,  60 ,  70 ,  80 , and  90  communicate with the smart filtering engine via communication channels  55 ,  65 ,  75 ,  85 , and  95 . Communication channels  55 ,  65 ,  75 ,  85 , and  95 , may be network communications, wireline communications, wireless communications, or any means of communicating data in analog or binary format between the applications and the smart filtering engine. As will be explained herein, applications may include any application which contains data in a user&#39;s personal information space. Examples include instant messaging applications such as MSN Messenger  50 , e-mail applications, contact manager applications, and consolidated email and contact applications such as Microsoft Outlook  60 , other personal information manager software  70 , a web personal information manager  80 , and a mobile telephone  90 . 
     Each of the applications contains a data store which includes information relative to the particular application. For example, MS Outlook will have associated with it a data store, such as a Microsoft Exchange database or a personal settings file associated with a particular user which stores information in that user&#39;s personal information space, such as e-mail, contact, journal, task, and other information. Mobile phone  90  will have, for example, a built-in memory for storing a number of telephone numbers and other contact information. 
     In accordance with the present invention, the smart filtering engine extracts information from each of the data stores associated with the applications  50 ,  60 ,  70 ,  80 , and  90 , and assesses the data to determine its importance to the user based on one or more characteristics of the data, and returns an output to the user. One simple example is to rank contact information based on the frequency of use of the information by the user. The engine may comprise code operable on a processing device for implementing this method. In accordance with the method, engine  100  may communicate with database  120  to store certain elements of the information from the applications in order to utilize this information in the analysis of the importance of the data to process it in accordance with the present invention. 
     In one embodiment of the present invention, the database  120  need not be a physically separate database, but represents a logical database. For example, in one embodiment, the engine may associate data characteristics with individual data records in each application&#39;s own data store, by using custom fields or additional records in the application&#39;s data store. All equivalent methods of associating data needed to perform the methods of the present invention with user personal information are contemplated as being within the scope of the present invention. 
       FIG. 2   a  shows a first, simple embodiment of a method of the present invention. In one context, the method of the present invention may be optimally utilized to extract contact information from, for example, a mobile phone, based on the frequency of usage that a user uses that contact information, and populate the phone or other devices with the more frequently used contacts. In the more general embodiment, described with respect to  FIG. 2   b  the method of the present invention involves analyzing any type of information in a user&#39;s personal information space and returning the relative importance of the information based on any number of characteristics of the data. Such characteristics may include frequency of use, the character of use, the nature of the use, who the use is interacted with, and any number of other factors listed herein. 
     In the embodiment of  FIG. 2   a , the invention is described in the context of analyzing data for updating a telephone address book. As shown in  FIG. 2   a , a first step  210  involves setting a limit on the number of contacts that user wishes to have input into the device. The step  210  may comprise a limit which is set by the user, or may comprise a physical limit of memory spaces in the telephone. While more recent models of cellular telephones include hundreds of spaces for contact information, a user may not want to populate all of this information space, the user may have contacts which exceed the number of spaces allowed in the phone, or the number of contacts in the phone may be limited by the user not wishing to include more than a certain number of contacts in the phone. The dashed arrow between step  210  and step  220  indicates that the limitation step  210  is an optional step in accordance with the present invention. 
     At step  220 , the user interacts with a contact by making a call using an entry from the phone book. Most cellular phones track recent call information and identify if such information is for a user in the phone&#39;s address book. At step  230 , the use of the contact is noted, and at step  240 , all contacts under analysis are ranked according to their frequency of use. A simplistic organization might be ranking the contacts in the order of the frequency that they are contacted by the user. In a cellular phone embodiment, this may involve simply analyzing the number of times a user telephones a particular contact in his address book and using the frequency analysis to rank the contact relative to other contacts. In this manner, if the user has, for example 700 contacts, the system will analyze and rank the contacts, and the user may then use that information as needed. For example, the user could instruct the system to update the user&#39;s phone with the most frequently called 100 contacts. The frequency of use and ranking may be stored in the smart filtering database at step  280  whether or not the user chooses to update the user&#39;s phone. If the user chooses to update the phone at step  250 , the contact list in the device is updated at step  260 . The storage step  280  may be performed whether or not the device is updated. 
     The method then waits for the next use of the phone (or “data interaction”) at step  270 . 
     Each use of the phone with the contact information therein comprises an interaction with the data. It should be understood that uses of the phone can include not only making a call using the number from the phone&#39;s internal phone book, but also adding, deleting and modifying entries in the phone book, receiving a call, or using the contact information to send an email or text message from the phone. Hence, in the context of this invention, an interaction with data in a user&#39;s personal information space is any use of the personal information space data by the user to any degree. 
       FIG. 2   b  shows a more generalized embodiment of the system of the present invention. In  FIG. 2   b , any type of personal information space data maybe analyzed. Initially, at step  215 , data parameters (such as, for example, phone book limits or other parameters) may be set by the user or by the physical limitations of the device. The dashed line between step  215  and  225  indicates that the limitation step  215  is optional. 
     At step  225 , a user interacts with the data in some form. Examples of such interaction might include using a contact for telephone or email or messenger communications, using a messenger application to contact a “buddy” (thereby interacting with the buddy data entry), modifying a data file, such as a word processing file or spreadsheet file, accessing a database file, or any number of tasks and interactions that a user makes with data in the user&#39;s personal information space. 
     At step  235 , data from an application is analyzed based on one or more rating factors. One such factor may be frequency, as described with respect to  FIG. 2   a . The nature and number of such factors may depend on the type of information analyzed. For example, if the data is contact information, the type of analysis which may occur can be the frequency of the use, the amount of use (in terms of the length of time one communicates with the contact), a weighting of the use (based on any number of a combination of factors), the length of time between interactions with the contact, the duration of the contact, the type of interaction with the contact, or other types of interactions. After analyzing the character of the use at step  235 , the data is organized based on one or more of the characteristics available to the filtering engine  100  at step  235 . One example of such organization is ranking the contacts. Other examples of organizing the contacts may include filtering based on the type of contact, the source of the contact (such as, for example, all users at one domain or physical address), or other user-defined filtering criteria. 
     The implementation with respect to a cell phone and contacts is described with respect to  FIG. 2   a . Other examples include: updating a frequently used file list to enable files to be synchronized to one or more devices; updating a buddy list in an instant messenger application; updating database records frequently used, and the like. 
     At step  245 , an update step for the user&#39;s personal information store may be implemented. This update step may include updating any number of device databases, or a separate database, such as the smart filtering database (described below), with the characteristic analysis of the user&#39;s personal information analyzed in step  235 . For example, if a user wishes to populate the user&#39;s telephone with  100  of the most frequently used contact, the user can allow his phone to be updated at step  255 . Other examples include updating a frequently used file list, or updating other lists maintained by applications which track the use of information in the personal information space by the user. 
     If the device is not to be updated, at step  285 , the information may be stored in smart filtering database  120  for future use in analysis step  235 . If the device is to be updated, at step  255  or prior thereto, the user may elect or have elected to mark certain records or data to manually determine what happens during the update step  255 . For example, the user may wish to designate certain records as never to be included in the phone, or always to be included in the phone. If the update step is for a phone, at step  255 , the present invention must check the contact record associated with the data from the application to ensure that the user has not manually selected one of a number of options with respect to the data. If the user has indicated that one or more records should “never” or “always” be contained in the data store, then at step  255 , the records are updated according to the user preference first and the characterization by the system second. If any record can be updated, then at step  255 , the contact list in the device is updated with all records available. At step  265 , the system then waits for the next analysis point, which may be each individual interaction with the user data from an application, or an analysis based on a timed interaction whereby the system waits a specific amount of time to reexamine the user&#39;s contact or application data usage before reanalyzing the data. 
       FIG. 3  shows one example of a hardware architecture for computers used to implement the present invention. The hardware includes a processor  302 , a memory  304 , a mass storage device  306 , a portable storage device  308 , a first network interface  310 , a second network interface  312  and I/O devices  314 . The choice of processor is not critical as long as a suitable processor with sufficient speed is chosen. Memory  304  can be any conventional computer memory. Mass storage device  306  can include a hard drive, CD-ROM or any other mass storage device. Portable storage  308  can include a floppy disk drive or other portable storage device. The computer may include one or more network interfaces. The network interface can include a network card for connecting to an Ethernet or other type of LAN. In addition, one or more of the network interfaces can include or be connected to a firewall. One of the network interfaces will typically be connected to the Internet or a LAN. I/O devices  314  can include one or more of the following: keyboard, mouse, monitor, display, printer etc. Software used to perform the methods of the present invention are likely to be stored in mass storage  306  (or any form of non-volatile memory), a portable storage media (e.g. floppy disk or tape) and/or, at some point, in memory  304 . Various embodiments, versions, and modification of the system of  FIG. 3  can be used to implement the present invention, and the above described hardware architecture is just one suitable example depicted in a generalized and simplified form. The present invention could include dedicated hardware, a firmware to implement the invention or other software and/or hardware architectures that are suitable. 
       FIG. 4  is a more detailed functional block diagram of a system for implementing the present invention. Shown in  FIG. 4  are a smart filtering engine  400  and filtering engine database  410 . Engine  400  and database  410  are equivalent to engine  100  and database  120  in  FIG. 1 . Box  450  represents an individual processing device such as that shown in  FIG. 3 . Device  450  includes a number of applications  420 ,  422 ,  424 , and  426  which are available to the user and each include a data store associated therewith. For example, an e-mail application  420 , which may comprise Microsoft&#39;s Outlook or a web e-mail application, includes its own data store of information, which includes e-mail information. The e-mail application may also include contact information, which is used by the user to maintain a list of contacts with whom the user e-mails. An alternative application is an instant messenger application  422 , such as Yahoo® instant messenger, AOL® instant messenger, or MSN® messenger. Instant messenger application  422  may include information such as buddy lists and information as to the frequency of communication with each individual in the buddy list. A contact information application  424  may also be provided. An example of such an application includes not only Microsoft® Outlook, but Best Software, Inc.&#39;s Act!® and similar types of applications. It should be understood that any other suitable application  426  which correlates data between a user controlled data element which, in the example shown in  FIG. 2   a , has been a contact element, may be utilized in accordance with the present invention. However, as will be explained in further detail below, the other application  426  need not solely relate to contact element data. For example, the application  426  might be a word processing application and the data interaction might be accessing a file. Alternatively, the other application might be a database application or a spreadsheet application. In either of these cases, accessing the database or the spreadsheet file may constitute interaction with data which may be utilized by the interaction analysis system of the present invention. 
     Each of the respective applications  420 ,  422 ,  424 ,  426  has associated therewith a data store agent. For example, email application  420  has a data store agent  430 , instant messenger application  422  has a data store agent  432 , contact manager application  424  has a data store agent  434 , and other application  426  has a data store agent  436 . Each of the data store agents  430 ,  432 ,  434 ,  436  communicates with an agent interface  440 . The agent interface  440  is in communication with engine  400 . Also shown in a data store agent  438  in communication with a phone  460 , which may be a wire line, wireless or cellular telephone. Communication between the phone  460  and processing device  450  may be by a direct cable connection, a wire line network connection, a wireless network connection, or other means, such as short messaging service (SMS). 
     Each of the agents, engine and database may be operable from code provided on the mass storage device instructs the processor device to perform tasks associated with the agent, engine and database, with components of each provided in memory  304 , mass storage  306  and/or portable storage  308 . 
     Also shown is a user interface  402  allowing the user to instruct the system regarding device limits and individual aspects of data under consideration. Although the user interface  402  is shown as coupled to the database  402 , independent code communicating with database  410  and engine  400  may be provided to implement the user interface  402 . User interface  402  may take any number of forms, including a separate application, an application operable in a web browser, an application operable from within data applications  420 ,  422 ,  424 ,  426 , or an application or interface operable from phone  460 . 
     In general, each agent  430 ,  432 ,  434 ,  436 , and  438  is designed to communicate with the associated application (or device) and extract data from the personal information store for analysis by the system of the present invention. In one embodiment, each agent extracts information on each interaction with the data; in other embodiments, the agents extract interaction information on a timed basis. A number of well known mechanisms exist for communicating with such applications, including use of associated application programming interfaces or API&#39;s. Information about the interaction, including the nature of the interaction and the data interacted with, is provided to agent interface  440 . The agent interface  440  may be as simple as a port that the agents communicate through or may be a communication interface receiving agent specific communications. The interface  440  may also control when each of the agents queries the data store associated with each application to determine whether data interactions have taken place. It can provide bi-directional communications with devices and applications in the system. The interface presents a common communication mechanism for the engine, while each agent is generally specific to the application with which it must interact. Database  410  includes records associated with the interaction data. The nature of the records is shown in  FIG. 7 . In addition, database  410  may contain records on the results of the smart filtering engine analysis which are stored for later use by the system. 
       FIG. 5  shows a networked embodiment of the present invention. In  FIG. 5 , like numerals represent like elements shown and described in the previous figures. In  FIG. 5 , each data store agent  430 ,  432 ,  434 ,  436 , communicates information to a network  550 . Network  550  may be a local area network, a wide area network, or a combination of public and private area networks, such as the Internet. Communications from each data store agent  430 ,  432 ,  434 ,  436 , are directed to an agent interface  560  provided on a filtering server  580 . Filtering server  580  may comprise one or more processing devices such as those shown and described with respect to  FIG. 3 . It should be understood that each of the processing elements  580  which perform processing functions may each be provided on their own dedicated server, or implemented on a single server and, in the case where individual dedicated servers are provided, may be coupled by a local or wide area network. 
     Agent interface  560  serves a similar function to agent interface  440  in  FIG. 4 . In one instance, it acts as a receiver for interaction data provided by the data store agents  430 ,  432 ,  434 ,  436 , and  520 . The interface  560  may also control when each of the agents queries the data store associated with each application to determine whether data interactions have taken place. It can provide bi-directional communications with devices and applications in the system. The interface  560  presents a common communication mechanism for the engine, while each agent is generally specific to the application with which it must interact. One difference in agent interface  560  from interface  440  is the ability to differentiate between users, as multiple users may connect via the network  550  to the server  580 . Also shown on server  580  is a data store agent  520  which is provided to receive data interaction information from a mobile device such as a cellular telephone  452 . Communication between mobile device  452  and agent  520  may be via a wired connection, a wireless connection, any of a number of wireless technologies such as SMS, or any suitable communications link. Also shown is a communications link between phone  452  via network  550 . 
     Optionally, a first user interface  565  may be provided via the network and is served by server  580  via any of a number of known technologies, including TCP/IP, active server pages, and the like. This interface may be viewed by device  450  using a browser  490  on device  450 . As an alternative to or in conjunction with interface  565 , a local smart filtering interface  515  may be provided on processing device  450 . The local interface may be similar to interface  402  except that it interacts with the engine on server  580  and agents  430 ,  432 ,  434 ,  436 . 
     In the implementation shown in  FIG. 5 , users coupled to processing devices  450  or mobile devices  452  communicate data interactions with the components on the filtering server  580 . Filtering server  580  can handle any number of multiple users and multiple processing devices. As described below, data from the individual users and processing devices will be identified and stored separately or identified separately in the filtering engine database  510 . It should be understood that any number of processing devices  450  and mobile devices  452  may be coupled to and served by server  580 . 
     It should be further recognized that agent  520  may be provided on device  450  (as shown in  FIG. 6 ) and hence the embedded device agent in device  450  can communicate via the network connection of device  450  over the network  550  to interface  560 . Agent interface  560  provides interaction data to a smart filtering engine  500  which is in communication with a filtering engine database  510 .  FIG. 6  illustrates a further embodiment of the data server agent wherein a data server agent  610  is embedded in a mobile device  650  and communicates directly with a smart filtering engine interface or directly with the smart filtering engine  500 . Not shown in  FIG. 6  are the connections to the smart filtering engine interface. However, it should be understood that the smart filtering engine is coupled as shown in  FIGS. 4 and 5  to a filtering engine database user interface and agent interface. 
       FIG. 7  shows yet another example of a system for implementing the present invention. In  FIG. 7 , the smart filtering system is utilized in conjunction with a synchronization system such as that described in co-pending patent application Ser. Nos. 09/490,550, 09/491,675 and 09/491,694. In  FIG. 7 , processing device  450  and applications  420 ,  422 ,  424 , and  426 , as well as agents  430 ,  432 ,  434 , and  436  are equivalent to those described above with respect to  FIGS. 4 and 5 . These agents couple to an agent interface  760  via the network  550 . Agent interface  760  is in communication with the smart filtering engine  700  and filtering engine database  510  as described above. Agent interface  760  acts in a manner similar to interfaces  440  and  560 . 
     In this embodiment, when changes to the data based on the user data interactions with applications  420 ,  422 ,  424 , and  426  are to be made, smart filtering engine  700  communicates with a synchronization engine  710  which provides changes either directly to the data store agents  430 ,  432 ,  436 , or  434 , or to the application data stores associated with applications  420 ,  422 ,  426 ,  424 , directly. It should be understood that other elements of the synchronization system such as that described in application Ser. Nos. 09/490,550, 09/491,675 and 09/491,694 may be provided in addition to the data store agents and application data stores in order to more efficiently synchronize data based on the technology used in the synchronization system. Data from multiple users is stored in filtering engine database  510  in a manner similar to described below. 
     As noted above, the number of different hardware configurations may be utilized to implement the method of the present invention. In general, the method involves data analysis of user interactions with the user&#39;s personal information space data, which in one case may be contact information, in order to provide the user a more relevant representation of the data that the user is interacting with based on some characteristic of how the user interacts with the data. In the implementations of the invention shown in  FIGS. 4 through 7 , the data store agents are acting as data accessors to provide interaction information from the data stores with which they are associated to the filtering engine of the present invention. Hence, whenever an interaction is undertaken on any data in the data store, this interaction will be identified to the agent interface. This interaction may be as simple as a telephone call or may be as involved as determining the amount and frequency of use of a data file and the characterization of the use of the data file for applications used by the user. 
       FIGS. 8 and 9  show exemplary data structures for records used in the present invention.  FIG. 8  is a UML class model diagram showing the types of data in a given content record  800  used in conjunction with the system of the present invention. While  FIG. 8  shows an example of a content record which may be stored in the filtering engine database  410 ,  510 , it should be understood by those skilled in the art that the invention is not limited either to the particular data type (e.g. a “contact record”) or the particular information stored with the data type. 
     As shown therein, each content record  800  includes a filter type  820  and content data  830 . The filter type is a three-state variable (“never”, “always” “smart”) which may be selected by the user or by automated means to enumerate whether the content data should never be present in a device, always be present in a device, or updated according to the characterization by the smart filtering engine of the present invention. If a user wishes to ensure that a particular piece of content is never included as part of the rated information or part of the device, the user can set a this variable by means of the user interface so that the system will never include this data in any update of the device (step  255 ). If, for example, the user wishes to always include the content in the user&#39;s personal information space, the always flag will be set. In an alternative embodiment, the filter type  820  may provide an indicator as to whether the record is subjected to evaluation. For example, returning to  FIG. 2   b , in the evaluation step  235 , records having an “always” and “never” filter type may be removed from the evaluation and only those having a “smart” evaluation analyzed. This improves the efficiency of the analysis and the process at step  235 . 
     By way of an additional example, suppose the user has a number of phone book entries that the user wishes to always include in the user&#39;s phone. The user may set the always flag for each of these entries to ensure that the smart filtering system of the present invention does not remove them for lack of use. Likewise, the user may select certain entries to never be included in the user&#39;s phone address book. Alternatively, the user can let the smart filtering engine operate on the piece of data in conjunction with the rules set forth for the particular type of data in use. 
     Content record  800  also includes content data  830 . Types of content data which may be included in the content data field include a phone call record, a meeting record, a web conference record, file access, a file multiplication, a task entry, an instant message, contact data such as an e-mail address, telephone number or address, an instant messaging address, a journal entry, or any other type of data accessed by a user in a personal information space. 
       FIG. 9  shows UML state charts and a class representation of the device agent  900 , smart filtering engine  950  and a rule engine  975 . In the previous figures, the smart filtering engine and rule engine have been combined to a single box indicated by the smart filtering engine. However, in order to provide additional detail for the present invention, these two components have been separated. It should be understood that a particular architecture used in the present invention may be varied in accordance with the scope of the invention. The device engine  900  will include two identifiers, an engine identifier and an ID identifier. 
     As shown in  FIG. 9 , device agent  900  has attributes of an engine identifier (-engine) denoting smart filtering engine  950  as the engine to utilize for communications and an agent ID identifier  920  which identifies the device agent to the smart filtering engine. Two methods for device engine  900  are shown: on ObjectInteraction and updateObjects. The on ObjectInteraction behavior returns the local user ID (LUID)  930  and InteractionType data  910  to identify the particular action for a record to the smart filtering engine. The on ObjectInteraction behavior provides the local ID and interaction type to the smart filtering engine. InteractionType contains four attribute types of data interactions for, in this example, contact data, which are: access, modify, delete and create. It will be understood that for different personal information, other interaction attributes may exist as specified herein. The updateObjects behavior updates an object list data  960  in the content data record of the particular application in communication with device agent  900 . 
     SmartFiltering engine  950  includes three methods: addObjectInteration, getFilteredObjects and rankObjects. The addObjectInteraction method uses the Agent ID, local user ID (LUIID) and Interaction Type to add the object to the list of objects to be filtered. The getFilteredObjects method returns an ObjectList  960  of all objects added by addObjectInteraction. Finally, rankObjects takes the ObjectList and provides a Result based on the filtering rules provided by the rule engine  975 . ObjectSmartFilter  975  includes a LoadRules method and a GetObjectRanking method. The LoadRules method allows for adding of specific filter rules (LoadRules) from a filter element. The getFilteredObjects method retrieves ranked object characteristics to be evaluated from the filter element. 
       FIGS. 10-12  are sequence diagrams illustrating the operation of the methods shown in  FIG. 9 . As shown in  FIG. 10 , when an object interaction in the data store  1000  occurs by a user, the on Objectlnteration method provides the LUID and InteractionType at  1002  to the data store agent  1020 . In the data store agent  1020 , the addObjectInteraction method then provides the Agent ID, LUID and InteractionType at  1004  to the smart filtering engine  1040 . The rankObjects method at  1006  then returns the object ranking based on the analysis rules provided to the rule engine  1060 , and the getFilteredObjects returns the filtered objects at  1008 . Finally, the updateObjects method returns the object list to the data store agent at  1010  to be updated by the smart filtering engine  1040 . 
       FIG. 11  represents the interaction between the SmartfilteringEngine  1040  and the ObjectSmartFilter  975 . In  FIG. 11 , the ObjectSmartFilter  975  is shown as broken down into an object filter manager  1110 , and a smart filter element  1130 . The filter manager  1110  allows one to instantiate different, customizable filters  1130  for different data types. When the rule engine  1060  is issued a function call  1006  by the rankObjects method, the getObjectFilter function call  1112  is issued to the object filter manager filter manager  1110 , and the LoadRules method  1114  and GetObjectRanking  1116  methods are invoked to return the filtering rules and object ranking, respectively, from the Smart Filter element  1130 . These are returned to the Rule Engine  1060  to implement updateObjects method, as illustrated in  FIG. 10 . 
     Other methods in addition to rating methods may be returned. For example, methods which allow elements of personal information to be associated with other elements in other applications may be returned. For example, suppose a user installs and begins use of an instant messaging application. Members of the user&#39;s communication list in the instant messenger application will likely have corresponding entries in the user&#39;s contact application. Upon entry and use of an instant messing contact, the object interaction call may call a method which attempts to match the instant messaging information to the contact information. Once matched, this association between the contact and the instant message can allow more sophisticated forms of rating, such as determining preferred methods for contacting a member of a user&#39;s contact datastore, or determining most frequently contacted individuals across multiple platforms of communication. This information may further be used to populate similar data fields across different applications. For example, one may use the new instant messaging application information to add information to the contact data store, and vice-versa. In this context, for example, each contact may be assigned a general unique identification number which is associated with a local user identification number by the aforementioned method. 
     Yet another alternative of the present invention involves performing the rating and filtering in various time sequenced contexts. In one context, analysis is performed on all information in a data store without storing the information in a separate database. This method is therefore performed in real time. Another embodiment involves storing one or more ratings and analyzing results of interactions at regular intervals. Yet another embodiment involves performing a real-time analysis concurrently with a storage analysis, and allowing the user to select between the different temporal representations of the user&#39;s interaction with the data in their personal information space. 
       FIG. 12  shows yet another alternative wherein a smart filtering aware synchronization engine  1200  is used in conjunction with a device agent  1020 , smart filtering engine  1040  and rule engine  1060 . In this instance, the smart filtering engine  1040  does not return the filtered data directly to the device agent, but rather the rule engine calculates the changes and provides them to the smart filtering aware sink engine  1200  which then sinks directly to device  1210 . Hence, in  FIG. 12 , following the return of the filtered objects at  1008 , a calculateDeviceChanges method instructs the sync engine  1200  to generate the changes required to update the data in the device  1210 . The sync engine  1200  then generates the changes and syncs to the device  1210  at  1214  in accordance with the teachings of application Ser. Nos. 09/490,550, 09/491,675 and 09/491,694. In this manner, the system of the present invention can be used in conjunction with any number of different sync systems. 
     The foregoing detailed description of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. Additionally, while the above description provided an example using the protocols and addressing currently used on the Internet, the present invention can be used with other protocols and addressing schemes. The described embodiments were chosen in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims appended hereto.