the detailed description provided below in connection with the appended drawings is intended as a description of the present examples and is not intended to represent the only forms in which the present example may be constructed or utilized . the description sets forth the functions of the example and the sequence of steps for constructing and operating the example . however , the same or equivalent functions and sequences may be accomplished by different examples . although the present examples are described and illustrated herein as being implemented in networking and computing environments , the environments described are provided as examples and not limitations . as those skilled in the art will appreciate , the present examples are suitable for application in a variety of different types of environments . fig1 is block diagram showing an example intent prediction and response (“ ipr ”) environment 100 . elements of ipr environment 100 may include : mobile devices , such as mobile device 114 , optionally associated with entities , such as person 112 , forming managed entities , such as managed entities 110 , 120 , 122 , and 124 ; sensors , such as sensor 130 ; ipr servers , such as server 140 ; ipr data stores , such as database 150 ; and communications networks , such as network 180 . an intent prediction and response system typically operates within such an ipr environment . example managed entity 110 includes an entity , such as person 112 , and a mobile device 114 . mobile device 114 is associated with entity 112 forming a managed entity . an entity may be a person , vehicle , animal , or any other physical object , data , or the like for which intent prediction and response may be desirable . a mobile device may be a personal data assistant , cell phone , electronic bracelet , electronic tag , electronic badge , implantable device , electronic chip , computer , or the like . such a mobile device may include a means for location tracking , such as global positioning system (“ gps ”) position tracking , and communication , such as wireless communication to a network such as the internet , a cellular network , a corporate network , or the like . such a mobile device may also include computer processing capability such that intent prediction and response software and / or firmware may be operate on the device . such a mobile device may be capable of creating and joining ad - hoc networks with other such devices , federating with other such devices , and otherwise communicating with other such devices . managed entities , such as entities 110 , 120 , 122 , and 124 are typically coupled to a network , such as example network 180 , via communication links , such as link 116 . such communication links may be wireless links or any other type of link suitable to the device and / or entity being managed . in one example , such a link may be provided , at least in part , by a cellular network , a wireless network , a wired network , or the like . each mobile device typically includes a unique identifier , such as a global unique identifier (“ guid ”) or the like , so as to enable each mobile device to be uniquely recognized and accessed by an ipr system . each mobile device may include identification information for the entity to which it is associated , if any . such information may be maintained in a profile or the like , such as an entity profile or a role profile as described in connection with fig2 . for example , mobile device 114 is shown associated with entity 112 , a person , and may include identifying information for person 112 sufficient for intent prediction and response purposes . in one example , when the entity is a person , the identity information includes name , address , and other contact and identification information . other information may include birth date , biometric information , license information ( such as a driver &# 39 ; s license number , state , etc . ), passport information , employee identification information , health information ( such as blood type , disability descriptions , doctor information , etc . ), emergency contact information , or the like . one purpose of such entity information is to uniquely identify the entity associated with the mobile device . if the entity is a vehicle , animal or some other object , data , or the like , the identity information maintained is generally sufficient for intent prediction and response purposes . such entity information may be stored in a profile or the like . a mobile device may be a “ dumb ” device , capable of limited data storage and communication capability . such limited communication capability , for example , may be limited to short - distance radio frequency (“ rf ”) or infrared (“ ir ”) communication or the like . such limited storage capability for example , may include providing limited data storage and identification information , such as with some types of electronic badge . further limitations may include periodic versus continuous communications , reply - only communications , or the like . with such a dumb device , most or all intent prediction and response processing may be performed remotely by other mobile devices and / or ipr servers , such as server 140 . in another example , a “ smart ” mobile device may include local intent prediction and response data storage and processing capability . for example , such a smart device may download and / or generate intent models , data and other information during communication with other mobile devices and / or ipr servers , and be capable of processing such information even when later out of communication with such devices and / or servers . such a smart device may also be capable of establishing ad - hoc networks with other devices , of federating with other devices , and sharing information about associated managed entities and / or intent models . an ipr environment may include ipr servers , such as server 140 , coupled to network 180 via communication links , such as link 142 . such communication links may be wireless or wired links or any other type of link suitable to the server being utilized . such servers may be coupled 160 to ipr data stores , such as database 150 , which may be local or remote to a server . such servers may host ipr software sufficient to enable the configuration and management of entities , sensors , ipr functionality , or the like . ipr servers may alternatively be implemented as a web service providing ipr functionality , or as some other type of centralized or distributed service . in an alternative example , an ipr system may not include any ipr servers . ipr servers , such as example irp server 140 , may host portions of an irp system including intent models and / or persistent ipr data . irp servers may be any device or mechanism that provides access to data or information useful for intent prediction or response purposes . the term “ device ” as used herein may refer to a mobile device , an irp server , or any other device , system , server , service , computer , or data source capable of providing processing , data , or information useful in intent prediction or response . such devices may be include or be associated with a data store , such as database 150 . such a data store may be maintained via any persistent media , such as non - volatile memory , magnetic media such as a disk drive or tape , or the like . such devices may be associated with multiple ipr systems , with such associations changing over time as required for intent prediction and response purposes . in some situations , such as with a person entity carrying a mobile device such as a cell phone , mobile computer , personal data assistant , or the like , it may be desirable to periodically validate that the correct person is in current possession of the mobile device . in one example , authentication and validation may be accomplished by periodically requiring a log - on , such as with a private password , or via a biometric scan , such as a finger print , iris scan , voice print , or the like . in another example , such an authentication and validation may be required at a particular point in time . in general , the purpose of such authentication and validation is typically to verify that the mobile device is currently in the possession of the correct entity . ipr environment 100 may also include sensors , such as sensor 130 . such sensors may be coupled to example network 180 via communication links , such as example link 132 , and thereby coupled to devices such as mobile device 1114 . such a coupling may be direct or via some intermediary system . alternatively , some sensors may be directly connected to a mobile device . communication links , such as link 132 , may be wireless or wired links or any other type of link suitable to the sensor . such sensors may be configured to monitor ambient , environmental or other conditions suitable for intent prediction and response purposes . for example , various sensors may be positioned at or around areas of interest so as to monitor weather conditions , degree of daylight , noise , moisture , temperature , or any other environmental condition of interest . in another example , a sensor may be a smoke alarm , a burglar alarm , a carbon monoxide detector , or the like . in yet another example , a sensor may monitor for power outages , natural gas pressure , phone line functionality , or the like . other example sensors may monitor speed , altitude , acceleration , direction , position , movement , road conditions , or the like . yet other example sensors may be applied to persons , animals , or other living creatures to monitor vital signs such as heart rate , blood pressure , brain wave activity , stress , or the like . yet other example sensors include data access mechanisms , such as an interface to information in a database or to data describing attributes of objects , such as the current position of an elevator , parking garage space availability , occupancy of a meeting room , current usage status of a bathroom , or the like . in short , and without limitation , a sensor may provide data or information about any real or virtual object , attribute , condition , entity , data , or the like . a sensor typically includes a unique identifier , such as a global unique identifier (“ guid ”) or the like , so as to enable the sensor to be uniquely recognized and accessed by an ipr system . additional sensor management , control , discovery , and access mechanisms may also be employed in ipr environment 100 . in many cases a single device may not have all the information required to disambiguate intent . multiple devices and / or sensors may be required to gather sufficient data and / or event sequence information . for example , multiple devices coupled to distinct sets of sensors with various sensor abilities distributed at different locations within an environment may provide distinct sources or viewpoints of event sequences . some devices may report real - time information while others may report historical information , such as longer - term caches of evidential patterns . for example , a fixed - position camera sensor may not be able to accurately see how many people are in a group , but an antenna sensor may be employed to determine how many distinct cell phone hand - shake signals are detected from the group , the combined data from the sensors potentially providing a more accurate estimate of the size of the group . the term “ intent ” as used herein refers to something planned or the purpose behind some sequence of events , even when all of the sequence has yet to unfold . intent may be applied to a human being wherein it is typically embodied in the thoughts and mind of the human being . intent as used herein may also be applied to non - cognitive entities , animals , objects , data , or anything else . in this case , intent refers to the plan , meaning , purpose , or the like behind the existence of , the activity of , the variation in , or the like of such an entity . fig2 is block diagram showing example profiles usable in an intent prediction and response (“ ipr ”) environment 100 . managed entity 110 , comprising person 112 and associated mobile device 114 , may include one or more profiles , such as example profiles 250 and 260 . example entity profile 150 typically includes data and information specific to a managed entity , such as entity 110 . such an entity profile is typically unique to the entity with which it is associated . in addition to information descriptive of the entity , such a profile typically includes intent prediction and response data that tends to be modified and augmented over time by an irp system . such ipr data may be maintained in a secure manner and may include access control provisions so as to limit unauthorized access . one or more role profiles , such as example role profile 260 , may also be associated with an entity . a role profile typically includes descriptive information and ipr data related to a role associated with the entity . for example , given that entity 110 is a person , an associated role profile may be an employee profile that includes data and information common to all employee entities having such a profile , as well as data specific to employee entity 110 . an entity may include multiple role profiles . for example , entity 110 may also be a father and include a father profile , may be an organ donor and include a donor profile , etc . further , an entity may include transient profiles , such as a bus rider profile that is associated with an entity while riding a bus or while holding a valid bus pass , or a patient profile while the entity is registered in a hospital , or the like . in another example , a vehicle may also be a taxi cab and include a taxi cab role profile in addition to a vehicle identity profile . such a vehicle , along with all other related vehicles , may also include a company asset role profile and a state vehicle registration role profile , for example . such profiles may be associated with an entity manually or automatically by an ipr or related system . fig3 is a block diagram showing an example intent prediction and response system 300 . at the core of system 300 is an intent prediction and response processor 310 that may be centralized or distributed . processor 310 may be implemented in hardware , software , or in a combination of hardware , software , and / or firmware . inputs to processor 310 may include : sensor data from various sensors , such as example sensor 350 ; event sequence 330 information ; and profile 340 information , such as from identity and / or role profiles . such inputs are typically used to feed one or more intent models , such as example intent prediction model 312 and / or example intent response model 314 . in one example , processor 310 processes model 312 resulting in an intent prediction . the intent prediction may be feed into an intent response model 314 , along with other data including sensor data , profile data , and event sequence data . intent response model 314 is processed by processor 310 typically resulting in an intent response 360 . in some examples the intent prediction model and intent response model may be combined into a single intent model . upon establishment of an intent response , such a response is typically carried out by an irp system . system 300 may be a learning system capable of detecting patterns in entity behavior and resulting in the modification and augmentation (“ feedback ”) of profile data , model structure and data , and the like . examples of such feedback are indicated in fig3 by arrows 313 , 315 , and 341 . in one example , an ipr system learns that entity “ bob ” typically sends a status email to his manager on friday afternoons . the email is typically addressed to the manager and is given a subject line of “ weekly status ”. bob typically copies the content of the same spreadsheet into the message body . the ipr system learns this behavior and , when it detects the event sequence of bob addressing an email to his manager on a friday afternoon with a subject line of “ weekly status ”, the system generates an intent prediction and response that automatically retrieves and copies the spreadsheet content into the message body . in another example , ipr system 300 may be a non - learning system performing intent prediction and response based upon pre - programmed condition / response sequences . in yet another example , system 300 may support pre - programmed intent prediction and response “ seeds ” establishing a base - line operation , and then further “ learn ” based , at least in part , on event sequences received over time for improved intent prediction and response . in yet another example , ipr system 300 may employ logical reasoning processes to identify intent consistent with event sequence information . additionally or alternatively , ipr system 300 may employ a statistical methodology for calculating and assigning probabilities to different intents . further , such a system 300 may compute the value of additional data not yet considered , and thus identify other sensors or the like from which data may be obtained to further discriminate among possible hypotheses of intent . the computation of the value of additional data may be performed , at least in part , because access to data and / or sensors may have an associated monetary cost . ipr system 300 may make use of such value computations to evaluate the monetary costs versus intent prediction and response benefits of accessing additional data and / or sensors . in one example , intent prediction and response models are maintained in an ipr data store . in an alternative example , such models are distributed among an ipr system &# 39 ; s mobile and other devices or maintained entirely by specific devices . in one example , an entity &# 39 ; s intent prediction model is maintained in an entity &# 39 ; s electronic badge , a copy of the model being transferred to other devices as required for intent prediction and response purposes . in another example , an intent response model is maintained in an ipr server and accessed as required for intent prediction and response purposes . sensors utilized by ipr system 300 may be explicitly associated with system 300 or may be unrelated and accessed in a transitory manner . in one example , an ipr system includes a sensor that is an electronic badge reader . in an alternative example , an irp system discovers an unrelated source of stock price information and accesses such data on an as - needed basis . in yet another example , a mobile device , such as a cell phone , includes a gps receiver that is accessed for position information by an ipr system . another example sensor is a contextual beacon , a device that monitors and transmits some ambient data , such as local temperature , such that the data can be accessed by an ipr system . such sensors may be associated with and / or discovered and accessed by multiple ipr systems . fig4 is a block diagram showing an example intent prediction process 400 . the process starts at block 410 upon receipt of event sequence information along with a profile related to the event sequence information . event sequence information is typically bounded and based upon a discrete set of events rather than a continuous event sequence . such a discrete event sequence may be a portion of a continuous event sequence . process 400 may be repeated for additional discrete portions of the continuous event sequence . for example , bob may be talking on his cell phone while driving his car . bob and his car , “ car a ”, are both managed entities . an irp system may receive a profile for car a and a related event sequence , “ entering parking garage ”. the system may have previously received a profile for “ bob ” and an associated event sequence , “ talking on cell phone ”. block 420 indicates applying profile information to an intent prediction model . for example , the ipr system may extract relevant data from car a &# 39 ; s entity profile and apply that data to an intent prediction model for car a . the ipr system may have previously extracted relevant data from bob &# 39 ; s entity profile and applied that data to an intent prediction model for bob . block 430 indicates applying event sequence information to an intent prediction model . for example , the ipr system may apply car a &# 39 ; s “ entering parking garage ” event sequence to the intent prediction model for car a . the ipr system may have previously applied bob &# 39 ; s “ talking on cell phone ” event sequence to the intent prediction model for bob . block 440 indicates applying sensor data to an intent prediction model . for example , the ipr system may sense data that indicates bob is inside car a and the cellular network bob is currently using does not have sufficient signal strength inside the parking garage car a is entering . the ipr system may also sense data that indicates that car a typically parks in the parking garage on the current day of the week at about the current time . the ipr system may further determine that additional data may be useful in establishing an intent prediction and calculate the value of such data . conditioned on a cost / benefit analysis or the like , the ipr system may locate appropriate sensors or data sources and retrieve the additional data . sensor data and the like is typically applied to appropriate intent prediction models . block 450 indicates establishing an intent prediction . for example , the ipr system may process car a &# 39 ; s intent prediction model along with the applied information and data , resulting in the establishment of the intent prediction , “ car a intends to park in the parking garage ”. the ipr system may further process bob &# 39 ; s intent prediction model resulting in the intent prediction , “ bob intends to talk on his cell phone inside the parking garage .” block 460 indicates updating an intent prediction model based upon the establishment of an intent prediction , the event sequence information and the related profile . for example , bob &# 39 ; s intent prediction model may be updated based upon input data and the established intent prediction so as to facilitate improved future intent prediction and response . block 470 indicates updating a profile based upon the establishment of an intent prediction . for example , bob &# 39 ; s entity profile may be updated based upon input data and the established intent prediction so as to facilitate improved future intent prediction and response . the established intent prediction may be used , along with other data and information , to help establish an intent response as described in connection with fig5 . fig5 is a block diagram showing an example intent response process 500 . the process starts at block 510 upon receipt of intent prediction information along with a profile related to the intent prediction information . block 520 indicates applying profile information to an intent response model . for example , the ipr system may extract relevant data from car a &# 39 ; s entity profile and apply that data to an intent response model for car a . block 530 indicates applying intent prediction information to an intent response model . such an intent prediction may be generated by an intent prediction process such as that described in connection with fig4 . for example , the ipr system may apply the intent prediction , “ car a intends to park in the parking garage ” to the intent response model for car a . the ipr system may further apply the intent prediction , “ bob intends to talk on his cell phone inside the parking garage ” to the intent response model for bob . block 540 indicates applying sensor data to an intent response model . for example , the ipr system may sense data that indicates an alternative cellular - network compatible with bob &# 39 ; s cell phone is available within the parking garage . the system may further sense data that indicates the parking garage is full except for spots 34 and 42 on level 5 , and that only spot 42 is large enough to accommodate car a . the ipr system may further determine that additional data may be useful in establishing an intent response and calculate the value of such data . conditioned on a cost / benefit analysis or the like , the ipr system may locate appropriate sensors or data sources and retrieve the additional data . block 550 indicates establishing an intent response . for example , the ipr system may process car a &# 39 ; s intent response model along with the applied information and data , resulting in the establishment of the intent response , “ send text message to bob &# 39 ; s cell phone indicating that only spot 42 on level 5 can accommodate car a for parking ”. the ipr system may further process bob &# 39 ; s intent response model resulting in the intent response , “ switch bob &# 39 ; s cell phone to the garage network upon entry to the garage .” block 560 indicates updating an intent response model based upon the establishment of an intent response and a related profile . for example , bob &# 39 ; s intent response model may be updated based upon input data and the established intent response so as to facilitate improved future intent prediction and response . block 570 indicates updating a profile based upon the establishment of an intent response . for example , bob &# 39 ; s entity profile may be updated based upon input data and the established intent response so as to facilitate improved future intent prediction and response . upon establishment of an intent response , such a response is typically carried out by elements of the irp system . fig6 is a block diagram showing an example device or computing environment 600 in which the technology described herein above may be implemented . a suitable computing environment may be implemented with numerous general purpose or special purpose systems . examples of well known systems may include , but are not limited to , cell phones , personal data assistants (“ pda ”), personal computers (“ pc ”), hand - held or laptop devices , microprocessor - based systems , multiprocessor systems , servers , workstations , consumer electronic devices , set - top boxes , and the like . some devices , such as electronic badges , tags , sensors , implantable devices , or the like may be more or less complex than example computing environment 600 . computing environment 600 generally includes a general - purpose computing system in the form of a computing device 601 coupled to various peripheral devices 602 , 603 , 604 and the like . system 600 may couple to various input devices 603 , including voice recognition , touch pads , buttons , keyboards and / or pointing devices , such as a mouse or trackball , via one or more i / o interfaces 612 . the components of computing device 601 may include one or more processors ( including central processing units (“ cpu ”), graphics processing units (“ gpu ”), microprocessors (“ μp ”), and the like ) 607 , system memory 609 , and a system bus 608 that typically couples the various components . processor 607 typically processes or executes various computer - executable instructions to control the operation of computing device 601 and to communicate with other electronic and / or computing devices , systems or environment ( not shown ) via various communications connections such as a network connection 614 or the like . system bus 608 represents any number of several types of bus structures , including a memory bus or memory controller , a peripheral bus , a serial bus , an accelerated graphics port , a processor or local bus using any of a variety of bus architectures , and the like . system memory 609 may include computer readable media in the form of volatile memory , such as random access memory (“ ram ”), and / or non - volatile memory , such as read only memory (“ rom ”) or flash memory (“ flash ”). a basic input / output system (“ bios ”) may be stored in non - volatile or the like . system memory 609 typically stores data , computer - executable instructions and / or program modules comprising computer - executable instructions that are immediately accessible to and / or presently operated on by one or more of the processors 607 . mass storage devices 604 and 610 may be coupled to computing device 601 or incorporated into computing device 601 via coupling to the system bus . such mass storage devices 604 and 610 may include non - volatile ram , a magnetic disk drive which reads from and / or writes to a removable , non - volatile magnetic disk ( e . g ., a “ floppy disk ”) 605 , and / or an optical disk drive that reads from and / or writes to a non - volatile optical disk such as a cd rom or dvd rom 606 . alternatively , a mass storage device , such as a hard disk , may include non - removable storage medium . other mass storage devices may include memory cards , memory sticks , tape storage devices , and the like . any number of computer programs , files , data structures , and the like may be stored on mass storage device 610 , other storage devices 604 , 605 , 606 and system memory 609 ( typically limited by available space ) including , by way of example and not limitation , operating systems , application programs , data files , directory structures , and computer - executable instructions . output devices , such as display device 602 , may be coupled to the computing device 601 via an interface , such as display adapter 611 . output device 602 may be a liquid crystal display (“ lcd ”). other types of output devices may include printers , audio outputs , voice outputs , tactile devices or other sensory output mechanisms , cathode ray tube (“ crt ”) displays , or the like . output devices may enable computing device 601 to interact with human operators or other machines or systems . a user may interface with computing environment 600 via any number of different input devices 603 such as voice recognition , touch pads , buttons , a keyboard , mouse , joystick , game pad , data port , and the like . these and other input devices may be coupled to processor 607 via input / output interfaces 612 which may be coupled to system bus 608 , and may be coupled by other interfaces and bus structures , such as a parallel port , game port , universal serial bus (“ usb ”), fire wire , infrared port , and the like . computing device 601 may operate in a networked environment via communications connections to one or more remote computing devices through one or more cellular networks , wireless networks , local area networks (“ lan ”), wide area networks (“ wan ”), storage area networks (“ san ”), the internet , radio links , optical links and the like . computing device 601 may be coupled to a network via network adapter 613 or the like , or , alternatively , via a modem , digital subscriber line (“ dsl ”) link , integrated services digital network (“ isdn ”) link , internet link , wireless link , or the like . communications connection 614 , such as a network connection , typically provides a coupling to communications media , such as a network . communications media typically provide computer - readable and computer - executable instructions , data structures , files , program modules and other data using a modulated data signal , such as a carrier wave or other transport mechanism . the term “ modulated data signal ” typically means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal . by way of example , and not limitation , communications media may include wired media , such as a wired network or direct - wired connection or the like , and wireless media , such as acoustic , radio frequency , infrared , or other wireless communications mechanisms . those skilled in the art will realize that storage devices utilized to provide computer - readable and computer - executable instructions and data can be distributed over a network . for example , a remote computer or storage device may store computer - readable and computer - executable instructions in the form of software applications and data . a local computer may access the remote computer or storage device via the network and download part or all of a software application or data and may execute any computer - executable instructions . alternatively , the local computer may download pieces of the software or data as needed , or distributively process the software by executing some of the instructions at the local computer and some at remote computers and / or devices . those skilled in the art will also realize that , by utilizing conventional techniques , all or portions of the software &# 39 ; s computer - executable instructions may be carried out by a dedicated electronic circuit such as a digital signal processor (“ dsp ”), programmable logic array (“ pla ”), discrete circuits , and the like . the term “ electronic apparatus ” may include computing devices or consumer electronic devices comprising any software , firmware or the like , or electronic devices or circuits comprising no software , firmware or the like . the term “ firmware ” typically refers to executable instructions , code or data maintained in an electronic device such as a rom . the term “ software ” generally refers to executable instructions , code , data , applications , programs , or the like maintained in or on any form of computer - readable media . the term “ computer - readable media ” typically refers to system memory , storage devices and their associated media , and the like . in view of the many possible embodiments to which the principles of the present invention and the forgoing examples may be applied , it should be recognized that the examples described herein are meant to be illustrative only and should not be taken as limiting the scope of the present invention . therefore , the invention as described herein contemplates all such embodiments as may come within the scope of the following claims and any equivalents thereto .