Abstract:
Determining a level of congruence between modality-event characteristics is disclosed. Information can be collected from an event input source via one or more information collection modalities. Modality-event characteristics can be determined from this information. A level of congruence between the modality-event characteristics can be determined to enable initiating a response based on the level of congruence. The level of congruence can be based on satisfying a rule related to congruence between modality-event characteristics, user profile information, etc. The level of congruence can be related to a probability that the several inputs collected for an event, collected by a plurality of modalities, embody characteristics that are associated with the event occurring according to determined notions embodied in the rule and profile. Determining the level of congruence can support assertions that each input, across differing modes of capturing said input, accords with the expected inputs for an event.

Description:
TECHNICAL FIELD 
       [0001]    The disclosed subject matter relates to an analysis of an event characteristic, e.g., an analysis of an event characteristic can be employed in discriminating between event inputs based on an incongruence related to the event characteristic. 
       BACKGROUND 
       [0002]    By way of brief background, conventional authentication of a user input to a device is “key and lock” type authentication, e.g., a user inputs a password (key) that results in an unlock of a functionality (lock) where the password is determined to match a password on file. More advanced conventional systems can use a multifactor authentication, e.g., two or more pieces of information can be checked (two keys are checked). In some typical multifactor systems, the second key is often a time sensitive code to which only the user is expected to have access. These technologies, however, do not address non-user sources of the keys and, as such, are susceptible to failure. More generally, where input(s) are treated as attributable to only one source, and therefore given a measure of trust, this measure of trust can be leveraged to circumvent typical security measures. As an example, a first user&#39;s spoken password can be recorded and then used by a second user to gain improper access premised on an assumption that only the first user will have the voice associated with the first user. This example can also be expanded to multifactor authentication, e.g., the second user can have the first user&#39;s mobile device and the recorded password, where the mobile device has the second key and the first key is spoofed with the recorded voice, the security measures are again defeated. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0003]      FIG. 1  is an illustration of an example system that facilitates determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. 
           [0004]      FIG. 2  is a depiction of an example system that facilitates determining a congruence of 1 to N event characteristics captured for an event source via 1 to N different modalities in accordance with aspects of the subject disclosure. 
           [0005]      FIG. 3  illustrates an example system that facilitates determining a congruence of event characteristics captured for an event source via different modalities and via a plurality of user equipments in accordance with aspects of the subject disclosure. 
           [0006]      FIG. 4  illustrates an example system that facilitates determining a congruence of event characteristics captured for an event via different modalities employing a remotely located analysis component in accordance with aspects of the subject disclosure. 
           [0007]      FIG. 5  illustrates an example of depicting determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. 
           [0008]      FIG. 6  illustrates an example method facilitating initiating a response based on determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. 
           [0009]      FIG. 7  depicts an example method facilitating determining a congruence of 1 to (N+1) event characteristics captured for an event source via 1 to (N+1) different modalities from a plurality of user equipments in accordance with aspects of the subject disclosure. 
           [0010]      FIG. 8  illustrates an example method facilitating determining a congruence of event characteristics captured for an event via different modalities based on remotely stored rules in accordance with aspects of the subject disclosure. 
           [0011]      FIG. 9  depicts an example schematic block diagram of a computing environment with which the disclosed subject matter can interact. 
           [0012]      FIG. 10  illustrates an example block diagram of a computing system operable to execute the disclosed systems and methods in accordance with an embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    The subject disclosure is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the subject disclosure. It may be evident, however, that the subject disclosure may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the subject disclosure. 
         [0014]    Conventional authentication of a user input to a device, as previously stated, is typically of a “key and lock” type. Keys, such as, user passwords, vocal patterns, fingerprints, iris scans, etc., can result in an unlock of a ‘lock’, e.g., a functionality, action, alert, system, etc., where the key is determined to match a record on file. More advanced conventional systems can use a multifactor authentication, e.g., two or more pieces of information can be checked (two keys are checked against stored values). These technologies, however, do not address the validity of the source of the key itself, e.g., is the password coming from the actual user associated with the password or, conversely, is the password being presented artificially, and thus these conventional systems can be susceptible to other types of failures, e.g., spoofing, etc., even where the password itself is correct. More generally, where input(s) are de facto treated as valid where correct rather than valid where correct and validated to a source, they can be leveraged to circumvent typical security measures. As an example, a true user&#39;s passcode can be recorded such as by a key logger, etc., and then used by a malicious user to gain access by sending the code electronically rather than physically entering it into a device via a keyboard, touchpad, etc. Where the passcode can be checked against other characteristics of the passcode entry event, e.g., is an actual touch detected on a keypad, touch screen, etc., is a face visible via an onboard camera, is the device in an orientation associated with normal passcode entry (e.g., not face down), can the true user&#39;s voice be detected in the background, etc., the validity of the passcode entry can be predicated on a congruence of these additional event characteristics and can improve the security associated with use of the example passcode. 
         [0015]    The present disclosure addresses analysis of event characteristics via additional modalities to determine a level of congruence that can facilitate validation of another characteristic of the event. In an aspect, information related to an event can be captured by a plurality of modalities. Characteristics of the event can be extracted from this information, e.g., modality-event characteristics. As an example, entry of text into an email can be associated with physical entry of the text via an input device, movement of the input device associated with the physical entry of the text (finger tapping can cause a device to jiggle), with the device being oriented so as to provide access to the input device (face up), the occasional sound of the user&#39;s native language (e.g., office conversation, the user speaking to someone, etc.), an orientation of the user and the device (the user can be in front of and facing the device to enter text) that can be captured by an imaging device, etc. The characteristics of these other aspects of the event can be analyzed to indicate a level of congruence. As an example, where the device is face down, a detected language is foreign, and the device is motionless, etc., it can be determined that the entry of text is not likely due to the user entering the text because there is a low level of congruence between the modality-event characteristics. As a further example, where the device is face up, a background voice attributable to the user&#39;s wife and child are detected, the device jiggles in correlation to text entry and is still in periods where text isn&#39;t being entered, and a front facing camera captures the user&#39;s face, there can be a high level of congruence and the text entry can be treated as likely attributable to the user. 
         [0016]    The present disclosure is not limited to determining congruencies for validating text entry and can be applied to nearly any form of input event. As an example, speaking into a corded or cordless microphone attached to a phone can be spoofed by using an antenna to induce currents in a microphone cord or can mimic the data sent from a wireless microphone and received by the phone. This type of event can be analyzed to check for electromagnetic radiation (EM) that has a sufficient level of correlation to the audio signal being received by the phone, e.g., if EM goes up when a spoken word is received and the EM radiation goes down when there is a pause, then there can be a lower level of congruence that can indicate that the audio information may not be from a true user. A further modality-event characteristic can be associated with the phone camera capturing the true user&#39;s face and mouth movements, e.g., where the mouth isn&#39;t moving in time to the audio information it can be less likely that the true user is generating the audio information, or where the another user&#39;s face (not the true user) is detected as prominently before the phone, e.g., by a front facing camera, then it is less likely to be congruent with the audio information and confidence in the source can be decreased. Nearly any event input can be associated with multiple modality-event characteristics that can be analyzed to determine a level of congruence and all such event inputs are to be considered within the scope of the present disclosure even where not expressly disclosed herein for the sake of clarity and brevity. 
         [0017]    Rules and profiles can also be employed in conjunction with the analysis of the modality-event characteristics. Rules can relate to broadly applicable conventions, for example, higher security inputs can be accorded higher levels of required congruence, e.g., a password entry can be invalidated without relatively higher levels of congruence as compared to validating a low security entry, e.g., text entry into a calendar application etc., which can be allowed with comparatively low levels of congruence. Profiles can be employed to personalize congruence determinations. As an example, a profile can require that all forms of valid event inputs be determined to have high congruence, low congruence, perfect congruence, etc., that one modality is given more weight than another modality, e.g., facial recognition congruence with an event input be more strongly weighted than an infrared/thermal imaging modality, etc., that a modality-event characteristic be ignored, e.g., that EM radiation modalities not be employed in congruence determination, etc., or nearly any other personalization. Moreover, profiles can comprise characteristics that can be associated with individual true users, for example, images of the user, images of family members, the sound/voices of a true user&#39;s office, user schedules, input device types/models/brands/identities, etc. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile as well as alteration of the general rules that are applied to the congruence determination via rules. 
         [0018]    In some embodiments, congruence determinations can be performed in a device receiving input. In some instances, the device receiving input can gather modality-event characteristic data from other device. In other instances, the input device can be enabled with other sensing equipment to allow collection of modality-event characteristics by the device itself. Modern smartphones, for example, are connected devices that embody numerous sensors, e.g., microphones, camera, tilt sensors, motion sensors, GPS, pressure sensors, touch sensors, fingerprint sensors, etc., and can also be connected to other devices, e.g., via a wired or wireless connection, such as IR sensors, remote cameras, proximity sensors, beacons, etc., that can all source modality-event characteristics to the smartphone contemporaneous with event input at the smartphone. Further, processing of the analysis can occur on the device receiving the event input and/or on other devices located locally or remotely from the event input receiving device. As an example, processing of the analysis can occur in the cloud, e.g., on a remotely located server connected to a device via at least a wired or wireless link, on a local device connected to an event input receiving device, e.g., on a desktop computer validating event input on a proximate tablet computer, or on the event input receiving device itself, e.g., on a smartphone receiving event input on the smartphone itself. Similarly, rules and/or profile information can be stored locally and/or remotely. 
         [0019]    To the accomplishment of the foregoing and related ends, the disclosed subject matter, then, comprises one or more of the features hereinafter more fully described. The following description and the annexed drawings set forth in detail certain illustrative aspects of the subject matter. However, these aspects are indicative of but a few of the various ways in which the principles of the subject matter can be employed. Other aspects, advantages and novel features of the disclosed subject matter will become apparent from the following detailed description when considered in conjunction with the provided drawings. 
         [0020]      FIG. 1  is an illustration of a system  100 , which facilitates determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. System  100  can comprise modality-event characteristic (MEC) analysis component (MECAC)  120 . MECAC  120  can receive MEC data  130  and can determine MEC congruence information (MECCI)  190 . MECAC  120  can receive MEC data  130  from one or more sources. MECAC  120  can, in an aspect, extract data related to a characteristic of an event input received via a modality and can analyze the significance of the characteristic in relation to other characteristics from other event input received by other modalities that are contemporaneous with the event. As an example, where an event input is a video signal captured with a first video modality, the characteristic can be a binary state associated with the mouth of a user. This can be analyzed in relation to another event input, for example, an audio event input captured by a microphone modality, wherein the characteristic under analysis is the periodic nature of loud and quiet periods in the audio data. This can be analyzed against a third event input captured by an accelerometer modality, wherein the characteristic is a time variant level of jiggle. These three characteristics can be associated with the same input device and can be contemporaneous. The analysis can then determine a level of congruence between the modality-event characteristics, e.g., how congruous is the mouth moving and sound level, how congruous is the mouth moving and the jitter, how congruous is the sound level and the jiggle, how congruous is the mouth moving, sound level, and jiggle, etc. From this example, where the true user is typing input, the jiggle and sound (tapping sound of a keyboard for example) can be congruous but neither can be congruous with the mouth movement as the true user is perhaps not speaking while typing (although he could be mouthing the words she is typing, which could cause the mouth movement to also be congruous). In another aspect of this example, where the user is doing speech-to-text input, the mouth movement and sound level can be congruous while the jiggle characteristic is not, perhaps the device is sitting on a table while the true user is dictating an email, for example. Of note, only three modality-event characteristics are illustrated in this example, but the disclosure contemplates nearly any number of MECs can be analyzed to determine congruence between one or more MECs. 
         [0021]    MEC data  130  can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can typically be extracted from nearly any information source contemporaneously associated with an event. Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a train in the background, the sound of a planes in the background, etc. It will be noted that numerous other examples are readily appreciated though they cannot all be explicitly state herein for the sake of brevity and clarity. 
         [0022]    The analysis of MEC data  130  by MECAC  120  can result in MECCI  190 . MECCI  190  can comprise information associated with congruence between MECs. As such, MECCI  190  can comprise congruence information for some, none, or all MECs associated with MEC data  130 . In an aspect, MECCI  190  can comprise information that relates to a level of congruence between MECs that can enable a response to be initiated. As such, where there is a high level of congruence between the MECs associated with an event, ‘no action’ can be an appropriate response, however, where congruence drops or was low, an alert can be initiated, a lockout can be initiated, tracking can be initiated, etc. Of note, congruence can change, such as over time, for an event. As an example, where a true user logs into an account with high MEC congruence, no action may be initiated, however, where the true user puts down the device without logging out and another user begins entry, the congruence can drop, for example the face may not be recognized, etc., which can cause an alarm to be initiated, for data associated with the drop in congruence to be quarantined, etc. Where, for example, a true user logs into a bank account on his mobile device but does not log out and then his mobile is stolen, a criminal trying to take money out of the bank account, because the true user is still logged in, can have his intentions frustrated where the congruence level has dropped. Other examples will be readily appreciated but are not explicitly recited for brevity, although all such examples are within the scope of the instant disclosure. 
         [0023]      FIG. 2  is a depiction of a system  200  that can facilitate determining a congruence of 1 to N event characteristics captured for an event source via 1 to N different modalities in accordance with aspects of the subject disclosure. System  200  can comprise MECAC  220 . MECAC  220  can receive MEC data  230  and can determine MECCI  290 . MECAC  220  can receive MEC data  230  from one or more sources. 
         [0024]    System  200  can comprise event input source  202 . Event input source can be proximate to user equipment (UE)  240 . UE  240  can receive an event input from event input source  202 , e.g., event input via 1 st  mode  210  to event input via N th  mode  218 . Event input source  202  can be detectable or observable by UE  240 , wherein the detection and observation ca be by way of event input via 1 st  mode  210  to event input via N th  mode  218 . In an aspect, event input source  202  can be any event that allows for detection or observation of, e.g., receiving of, event input via 1 st  mode  210  to event input via N th  mode  218 . As an example, event input source  202  can be a user entering a password, whereby event input via 1 st  mode  210  to event input via N th  mode  218  can comprise the password entry, an image of the user entering the password, audio captured from the area proximate to the user entering the password, EM radiation from the area proximate to the user entering the password, facial recognition information, motion information of the UE or of the area proximate to the user entering the password, etc. As another example, event input source  202  can be a user using near field communication (NFC) enabled credit card to pay for items or services, whereby input via 1 st  mode  210  to event input via N th  mode  218  can comprise NFC information, an image of the credit card user, audio captured from the area proximate to the user, EM radiation from the area proximate to the user, facial recognition information, motion information of the UE or of the area proximate to the user, or nearly any other information related to the event determined contribute to the scene, e.g., the event input source  202 . 
         [0025]    UE  240  can, in an aspect, simply pass input via event input via 1 st  mode  210  to event input via N th  mode  218  as MEC data  230  to MECAC  220 . In another aspect, UE  240  can extract or determine MEC data  230  from input from event input via 1 st  mode  210  to event input via N th  mode  218  before enabling access to MEC data  230  by MECAC  220 . In some embodiments, UE  240  can comprise MECAC  220 . In other instances, MECAC  220  can be discrete and separate from UE  240  and located local to, or remote from, UE  240 . Event input via 1 st  mode  210  to event input via N th  mode  218  can comprise information about event input source  202  that can enable extraction or determination of characteristics of event input source  202 . As such, MEC data  230  can comprise information about the characteristics of event input source  202  with regard to the modality that is associated with the capture of the information leading to the characteristic. As an example, where the 1 st  mode is an image of a user, event input via 1 st  mode  210  can comprise information about the image that can facilitate extraction of characteristics about the image, e.g., facilitating facial recognition, iris pattern detection, hair color, eye color, physiological aspects of the user that can be derived from the image such as flared nostrils, flushed cheeks, bloodied lip, wearing makeup, the presence of glasses/contact lenses, etc. These characteristics can be embodied in MEC data  230  to facilitate MECAC  220  in determining a congruence of the characteristics across a plurality of modalities, e.g., via event input via 1 st  mode  210  to event input via N th  mode  218  presented as MEC  230  to MECAC  220 . 
         [0026]    In an aspect, MECAC  220  can extract data related to a characteristic of an event input received via a modality and can analyze the significance of the characteristic in relation to other characteristics from other event input received by other modalities that are contemporaneous with the event, e.g., event input via 1 st  mode  210  to event input via N th  mode  218 . As an example, where an event input is a video signal captured with a first video modality, the characteristic be a state associated with the eye movement of a user. This can be analyzed in relation to another event input, for example, an audio event input captured by a microphone modality, wherein the characteristic can be a determined level of tension in the voice based on vocal analysis. This can be analyzed against other event inputs from event input source  202 . These three characteristics can be associated with the same input device, e.g., UE  240 , and can be contemporaneous. The analysis can then determine a level of congruence between the modality-event characteristics, e.g., how congruous is the eye movement and vocal stress level, how congruous is the eye movement and the other characteristics, etc. From this example, where the true user is displays rapid eye movement and has a high level of vocal stress, it can be determined that the user is acting under duress, that the user is violating a social norm, etc. 
         [0027]    MEC data  230  can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can typically be extracted from nearly any information source contemporaneously associated with an event, e.g., event input via 1 st  mode  210  to event input via N th  mode  218 . Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a train in the background, the sound of a planes in the background, etc. It will be noted that numerous other examples are readily appreciated though they cannot all be explicitly state herein for the sake of brevity and clarity. 
         [0028]    The analysis of MEC data  230  by MECAC  220  can result in MECCI  290 . MECCI  290  can comprise information associated with congruence between MECs. As such, MECCI  290  can comprise congruence information for some, none, or all MECs associated with MEC data  230 . In an aspect, MECCI  290  can comprise information that relates to a level of congruence between MECs that can enable a response to be initiated. As such, where there is a low level of congruence between the MECs associated with an event, an action can be an appropriate response. Where, for example, MECCI  290  indicates a high level of congruence for rapid eye movement and high vocal stress for a customer in line at a bank, an alert can be sent to bank security regarding the ‘nervous’ customer. As another example, where MECCI  290  indicates a high level of congruence for rapid eye movement and high vocal stress while a user is online shopping via a mobile device, e.g., UE  240 , a customer service session can be initiated via the mobile device to aid the anxious user with their purchase. Other examples will be readily appreciated but are not explicitly recited for brevity, although all such examples are within the scope of the instant disclosure. Of note, congruence can change, such as over time, for an event. 
         [0029]      FIG. 3  illustrates a system  300  that facilitates determining a congruence of event characteristics captured for an event source via different modalities and via a plurality of user equipments in accordance with aspects of the subject disclosure. System  300  can comprise MECAC  320 . MECAC  320  can receive MEC data  330  and can determine MECCI  390 . MECAC  320  can receive MEC data  330  from one or more sources, e.g., UE  340 , UE  342 , etc. 
         [0030]    System  300  can comprise event input source  302 . Event input source can be proximate to UE  340 ,  342 , etc. UE  340  can receive an event input from event input source  302 , e.g., event input via 1 st  mode  310  to event input via N th  mode  318 . UE  342  can receive an event input from event input source  302 , e.g., event input via (N+1) th  mode  319 . Event input source  302  can be detectable or observable by UE  340 ,  342 , etc., wherein the detection and observation can be by way of event input via 1 st  mode  310  to event input via (N+1) th  mode  319 . In an aspect, event input source  302  can be any event that allows for detection or observation of, e.g., receiving of, event input via 1 st  mode  310  to event input via (N+1) th  mode  319 . As an example, event input source  302  can be a driver asking for directions via a vehicle navigation system, whereby event input via 1 st  mode  310  to event input via (N+1) th  mode  319  can comprise vocalization of an address, an image of the driver, an image of surrounding traffic, an image of any passengers, audio captured from the area proximate to the driver, facial recognition information, motion information of the UE, e.g., UE  340 ,  342 , the vehicle, etc. 
         [0031]    UE  340 ,  342 , etc., can, in an aspect, provide input via event input via 1st mode  310  to event input via (N+1) th  mode  319  in an unchanged form as MEC data  330 / 332  to MECAC  320 . In another aspect, UE  340 ,  342 , etc., can extract or determine MEC data  330  from input from event input via 1 st  mode  310  to event input via (N+1) th  mode  319  before enabling access to MEC data  330 / 332  by MECAC  320 . In some embodiments, UE  340 ,  342 , etc., can comprise MECAC  320 . In other instances, MECAC  320  can be discrete and separate from UE  340 ,  342 , etc., and located local to, or remote therefrom. Event input via 1 st  mode  310  to event input via (N+1) th  mode  319  can comprise information about event input source  302  that can enable extraction or determination of characteristics of event input source  302 . As such, MEC data  330 / 332  can comprise information about the characteristics of event input source  302  with regard to the modality that is associated with the capture of the information leading to the characteristic. These characteristics can be embodied in MEC data  330 / 332  to facilitate MECAC  320  in determining a congruence of the characteristics across a plurality of modalities, e.g., via event input via 1 st  mode  310  to event input via (N+1) th  mode  319  presented as MEC  330 / 332  to MECAC  320 . 
         [0032]    In an aspect, MECAC  320  can extract data related to a characteristic of an event input received via a modality and can analyze the significance of the characteristic in relation to other characteristics from other event input received by other modalities that are contemporaneous with the event, e.g., event input via 1 st  mode  310  to event input via (N+1) th  mode  319 . This can be analyzed against other event inputs from event input source  302 . These characteristics can be associated with the same event input source  302  via input device(s), e.g., UE  340 ,  342 , etc., and can be contemporaneous. The analysis can then determine a level of congruence between the modality-event characteristics. 
         [0033]    In an aspect, the inclusion of UE  342  can present several pathways for access to event input via (N+1) th  mode  319 . In a first embodiment, event input via (N+1) th  mode  319  can be simultaneously captured by UE  340  and UE  342 . In another embodiment, event input via (N+1) th  mode  319  can be captured by UE  342  and passed to UE  340  via path  319 A-C. Whereby UE  340  can then pass event input via (N+1) th  mode  319 , via path  319 C, to MECAC  320  as MEC data  330 . Furthermore, UE  342  can receive event input via (N+1) th  mode  319  and enable access to corresponding MEC data  332 . MEC data  332  can be accessed by MECAC  320  via path  332 A and/or from UE  340  via path  332 B as part of MEC data  330 , e.g., MEC data  332  can be incorporated into MEC data  330  when received by UE  340  from UE  342  via path  332 B. 
         [0034]    MEC data  330 ,  332 , etc., can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can typically be extracted from nearly any information source contemporaneously associated with an event, e.g., event input via 1 st  mode  310  to event input via (N+1) th  mode  319 . Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a train in the background, the sound of a planes in the background, etc. It will be noted that numerous other examples are readily appreciated though they cannot all be explicitly stated herein for the sake of brevity and clarity. 
         [0035]    The analysis of MEC data  330 ,  332 , etc., by MECAC  320  can result in MECCI  390 . MECCI  390  can comprise information associated with congruence between MECs. As such, MECCI  390  can comprise congruence information for some, none, or all MECs associated with MEC data  330 ,  332 , etc. In an aspect, MECCI  390  can comprise information that relates to a level of congruence between MECs that can enable a response to be initiated. As such, the level of congruence between the MECs associated with an event can be associated with a response determined to be appropriate. Of note, congruence can change, such as over time, for an event. 
         [0036]    MECAC  320  can, as illustrated, comprise characteristic extraction component  350 . Characteristic extraction component  350  can extract a value ascribed to characteristic based on MEC data  330 ,  332 , etc. As an example, MEC data  330  can comprise information related to an audio modality associated with event input source  302 . Characteristic extraction component  350 , in this example, can extract certain time-frequency relationships, amplitude-time relationships, Fourier or other transform information, etc., from the information related to the audio input to enable this characteristic to be analyzed for congruence with other characteristics. Further, MECAC  320  can comprise characteristic correlation component  360 . Characteristic correlation component  360  can determine a correlation between characteristics, e.g., characteristics extracted from MEC data  330 ,  332 , etc., via characteristic extraction component  350 . In an aspect this can allow for exclusion of uncorrelated characteristics in determining a level of congruence between characteristics. 
         [0037]    In an aspect, MECAC  320  can also comprise correlation rule component  370  and profile component  380 . Rules and profiles can also be employed in conjunction with the analysis of the modality-event characteristics. Correlation rule component  370  can facilitate access to one or more rule, wherein a rule relates to broadly applicable conventions related to determining a level of congruence. As an example, a rule can relate to determining similarity between waveforms, applying threshold values, defining normal rates of change in MEC values, etc. In an aspect, a rule can be stored by correlation rule component  370  or can be received by correlation rule component  370 , such as from a local or remotely located data store. 
         [0038]    Profiles, in comparison to rules, can be employed to personalize congruence determinations. Profile component  380  can enable access to a profile value comprised in one or more profiles. In an aspect, a profile can be stored by profile component  380  or can be received by profile component  380 , such as from a local or remotely located data store. A profile value, for example, can indicate weighting of modalities whereby some modalities can have a greater impact on MECCI  390  than other modalities, can indicate modalities that are to be ignored, modalities that are to always be used, etc., can designate different rankings of modalities for different UEs, etc. As an example, a profile can indicate that a video modality is to always be employed in determining the level of congruence but that the video source should be selected from the UE, e.g., selecting between UE  340 ,  342 , etc., that has the highest resolution video stream. Moreover, profiles can comprise characteristics that can be associated with individual true users, for example, images of the user, images of family members, the sound/voices of a true user&#39;s office, user schedules, input device types/models/brands/identities, etc. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile, via profile component  380 , as well as application of updateable general rules, via correlation rule component  370 , that can be applied to the congruence determination. 
         [0039]      FIG. 4  illustrates a system  400  that facilitates determining a congruence of event characteristics captured for an event via different modalities employing a remotely located analysis component in accordance with aspects of the subject disclosure. System  400  can comprise MECAC  420 . MECAC  420  can receive MEC data  430  and can determine MECCI  490 . MECAC  420  can receive MEC data  430  from one or more sources related to an event input source  402 . Event input source can be proximate to user equipment (UE)  440 . UE  440  can receive an event input from the event input source, e.g., event input via 1 st  mode  410  to event input via N th  mode  418 . An event input source can be detectable or observable by UE  440 , wherein the detection and observation ca be by way of event input via 1 st  mode  410  to event input via N th  mode  418 . 
         [0040]    UE  440  can, in an aspect, simply pass input via event input via 1 st  mode  410  to event input via N th  mode  418  to MECAC  420  as MEC data  430 . In another aspect, UE  440  can extract or determine MEC data  430  from input from event input via 1st mode  410  to event input via N th  mode  418  before enabling access to MEC data  430  by MECAC  420 . Event input via 1 st  mode  410  to event input via N th  mode  418  can comprise information about an event input source that can enable extraction or determination of characteristics of the event input source. As such, MEC data  430  can comprise information about the characteristics of the event input source via the modality that is associated with the capture of the information. These characteristics can be embodied in MEC data  430  to facilitate MECAC  420  in determining a congruence of the characteristics across a plurality of modalities, e.g., via event input via 1 st  mode  410  to event input via N th  mode  418 . Characteristics can be analyzed in relation to another characteristic associated with another modality for capturing information related to the event input. The analysis can then determine a level of congruence between the modality-event characteristics. 
         [0041]    MEC data  430  can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can typically be extracted from nearly any information source contemporaneously associated with an event, e.g., event input via 1 st  mode  410  to event input via N th  mode  418 . Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a train in the background, the sound of a planes in the background, etc. It will be noted that numerous other examples are readily appreciated though they cannot all be explicitly state herein for the sake of brevity and clarity. 
         [0042]    The analysis of MEC data  430  by MECAC  420  can result in MECCI  490 . MECCI  490  can comprise information associated with congruence between MECs. As such, MECCI  490  can comprise congruence information for some, none, or all MECs associated with MEC data  430 . In an aspect, MECCI  490  can comprise information that relates to a level of congruence between MECs that can enable a response to be initiated. Of note, congruence can change, such as over time, for an event. 
         [0043]    MECAC  420  can be communicatively coupled to a security component of UE  440 . As such, a response can be triggered by MECAC  420  based on a level of congruence between MECs. This can be in addition to facilitating access to MECCI  490  via MECAC  420 . As an example, a response can cause security component  446  to reject input associated with an event input to UE  440 , terminate or suspend access to data or resources of UE  440 , request additional validation, cause an alarm, etc. In another aspect, security component  446  can communicate information, via input/output (I/O) component  444  of UE  440  to external devices or systems (no illustrated), e.g., via communication framework  492 . 
         [0044]    UE  440  can comprise I/O component  444  that can facilitate communication between MECAC  420  and other devices or systems, both internal and external to UE  440 . In an aspect, MECCI  490  can be stored at data store  448 ,  494 , etc., shared with MECAC  422 , etc., or with another local or remotely located device, such as a remote server, etc., via communications framework  492  by way of I/O component  444 . In a further aspect, rule and/or profile information can be received at MECAC  420  via I/O component  444 , e.g., from data store  448 ,  494 , etc., or from other local or remote devices, etc., via communication framework  492 . 
         [0045]      FIG. 5  is a diagram  500  that depicts determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. Diagram  500  illustrates UE  540  with a wired headphone/microphone attachment. Of note, it has been shown that EM radiation can be used to spoof vocal input to a microphone in this arrangement, e.g., an EM signal can be used to imitate user voice commands by inducing currents in the microphone wires for the headset attachment. At  502 A, a true user can vocalize into the microphone of the headset attached to  540 , e.g., via  510 A. UE  540  can comprise a MECAC, e.g., MECAC  520 , etc. In the case of the true user vocalizing, MEC data  530 A can comprise characteristic information  530 A 1  for a microphone modality, e.g., frequency or amplitude over time, etc., and EM characteristic  530 A 2 , e.g., relating an amount of EM radiation received by UE  540  contemporaneously with the microphone characteristic. Where the true user is vocalizing, EM radiation can be low and substantially different in character from the microphone characteristic. This can be associated with a level of congruence indicating that it is unlikely that EM radiation caused the detected microphone characteristic, e.g., that the microphone reflects a true vocal signal received by the microphone. In contrast, where antenna  502 B produces EM radiation that induces a microphone-like current detected by UE  540 , e.g., at  510 B, this can be associated with MEC  530 B. MEC  530 B can comprise a microphone characteristic and EM characteristic similar to  530 A, however, in the case where antenna  502 B is sending the EM radiation, EM characteristic  530 B 2  can be substantially non-zero. In some instances, EM characteristic  530 B 2  can be determined to be similar to microphone characteristic  530 B 1 . This can lead to a different determined level of congruence between  530 B 1  and  530 B 2  than was determined for  530 A 1  and  530 A 2 . In response to the similarity between  530 B 1  and  530 B 2 , it can be determined that  502 B can be attempting to spoof a vocal input such as  502 A. As such, MECCI data, e.g.,  590 , etc., can be employed to initiate a response to the determined level of congruence, e.g., in the ‘A case’ the level of congruence can result in allowing the vocal input, and in the ‘B case’ the different level of congruence can result in disallowing the possibly spoofed vocal input. 
         [0046]    In some embodiments, UE  540  can comprise MECAC  520 . In other embodiments, MECAC  520  can be located locally or remotely from UE  540 . MECAC  520  can determine MECCI  590  based on MECs, such as those contained in  530 A,  530 B, etc., e.g.,  530 A 1 ,  530 A 2 ,  530 B 1 ,  530 B 2 . Moreover, MECCI  590  can be further based on a rule and/or a profile, as disclosed herein. In an embodiment, MECCI  590  can be determined from a formula such as: f( 530   x   1 ,  530   x   2 , . . . ,  530   x N, rule, profile), where the notation  530   x   1  can be  530 A 1  in the ‘A case’ and  530 B 1  in the ‘B case’. Further, MECCI  590 , a rule, or profile data of a profile can be stored and communicated to/from data store  548 . In some embodiments, data store  548  can be comprised in UE  540 . In other embodiments, data store  548  can be located local to, or remote from, UE  540 . 
         [0047]    In view of the example system(s) described above, example method(s) that can be implemented in accordance with the disclosed subject matter can be better appreciated with reference to flowcharts in  FIG. 6 - FIG. 8 . For purposes of simplicity of explanation, example methods disclosed herein are presented and described as a series of acts; however, it is to be understood and appreciated that the claimed subject matter is not limited by the order of acts, as some acts may occur in different orders and/or concurrently with other acts from that shown and described herein. For example, one or more example methods disclosed herein could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, interaction diagram(s) may represent methods in accordance with the disclosed subject matter when disparate entities enact disparate portions of the methods. Furthermore, not all illustrated acts may be required to implement a described example method in accordance with the subject specification. Further yet, two or more of the disclosed example methods can be implemented in combination with each other, to accomplish one or more aspects herein described. It should be further appreciated that the example methods disclosed throughout the subject specification are capable of being stored on an article of manufacture (e.g., a computer-readable medium) to allow transporting and transferring such methods to computers for execution, and thus implementation, by a processor or for storage in a memory. 
         [0048]      FIG. 6  illustrates a method  600  that facilitates initiating a response based on determining a congruence of event characteristics captured for an event via different modalities in accordance with aspects of the subject disclosure. At  610 , method  600  can comprise receiving modality-event characteristic (MEC) data. MEC data can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can be extracted from nearly any information source contemporaneously associated with an event via a given modality. Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a people in the background, the sound of a traffic in the background, etc. It will be noted that numerous other examples can be readily raised though they cannot all be explicitly stated herein for the sake of brevity and clarity. 
         [0049]    MEC data can be related to an event input source that can be proximate to a user equipment (UE). A UE can receive an event input from an event input source, e.g., as an event input via an N th  mode, e.g.,  210 - 218 ,  310 - 319 ,  410 - 418 , etc. An event input source can be detectable or observable by UE, wherein the detection and observation ca be by way of the event input via the N th  mode, for example the event input via an N th  mode can comprise password entry, an image of a user, audio captured from an area proximate to a user, EM radiation from an area proximate to a user, facial recognition information, motion information of the UE or of the area proximate to the UE, etc. 
         [0050]    At  620 , method  600  can comprise determining MEC congruence information (MECCI) based on MEC data from  610 . A characteristic of an event input received via a modality and can be extracted from MEC data and be employed in an analysis of the significance of the characteristic in relation to other characteristics from other event inputs received by other modalities that are contemporaneous with the event. As an example, where an event input is a video signal captured with a first video modality, the characteristic be a state associated with the finger movement of a user. This can be analyzed in relation to another event input, for example, an amount of jiggle captured by an accelerometer modality. This can be analyzed against other event inputs from an event input source. These characteristics can be associated with the same input device, e.g., a UE, smartphone, tablet, wearable device, vehicle, keyboard, touchscreen, microphone, internet of tings (IOT) enabled device, etc. The analysis can then determine a level of congruence between the modality-event characteristics, e.g., how congruous is the finger movement and jiggle level, how congruous is the finger movement and the other characteristics, how congruous is the jiggle level and the other characteristics, etc. From this example, where a true user is tapping in a password on a touch screen mobile device, the finger movement can be highly congruous with the jiggle levels over time, which can be employed to assert that the true user is entering the password being received, e.g., the finger movement appear to match the shaking of the mobile device in time and it can be more likely that it is an actual entry of the password data than if the finger movements did not ‘match’ the jiggle of the device. As an example where the mobile is perfectly still, it is less likely the user is actually tapping the touchscreen to enter the password data. Where a third, fourth, etc., MEC is also analyzed for congruence, this can result in further refinement of the determined level of congruence between the characteristics and the entry of the password. For example, where the third MEC relates to the orientation of the device, where it is determined that the device is face down, and the fourth MEC relates to a front facing series of images, which are determined to be black (as would occur where a device is face down on a table), the level of congruence of the four MECs can be low, namely that even though there is finger movement and jiggle, these are offset by device orientation and an image of a table top so close to the device front that actually tap entry of a password is unlikely. The MEC congruence, e.g., MECCI, can be employed to initiate a response. 
         [0051]    At  630 , MECCI can be made available for access by other devices, systems, methods, etc. In an aspect, MECCI can be made available to a UE associated with receiving event information to enable the UE to respond to the input event based on the levels of congruence determined, e.g., different combinations of event inputs via different modalities, and MECs related thereto, can have different levels of congruence for each combination. By selecting a relevant combination of MECs, the associated determined level of congruence can be employed in determining a corresponding response. 
         [0052]    At  640 , method  600  can include, in response to the MECCI being determined to satisfy a rule related to a profile value, initiating a response condition. At this point, method  600  can end. Rules and profiles can also be employed in conjunction with the analysis of the modality-event characteristics. A rule can impart broadly applicable conventions to determining a level of congruence. As an example, a rule can relate to determining similarity between waveforms, applying threshold values, defining normal rates of change in MEC values, etc. Profiles, in comparison to rules, can be employed to personalize congruence determinations. A profile value can be comprised in one or more profiles. A profile value, for example, can indicate weighting of modalities whereby some modalities can have a greater impact on MECCI than other modalities, can indicate modalities that are to be ignored, modalities that are to always be used, etc., can designate different rankings of modalities for different UEs, etc. As an example, a profile can indicate that a video modality is to always be employed in determining the level of congruence but that multiple video sources should be selected when available. Moreover, profiles can comprise characteristics that can be associated with individual true users, for example, images of the user, images of family members, the sound/voices of a true user&#39;s office, user schedules, input device types/models/brands/identities, etc. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile, as well as application of updateable general rules, that can be applied to the congruence determination. 
         [0053]      FIG. 7  illustrates a method  700  that facilitates determining a congruence of 1 to (N+1) event characteristics captured for an event source via 1 to (N+1) different modalities from a plurality of user equipments in accordance with aspects of the subject disclosure. At  710 , method  700  can comprise receiving 1 to N event inputs for an event via 1 to N modalities via a first UE. At  720 , method  700  can comprise receiving, by a second UE, an (N+1) th  event via a (N+1) th  modalities for the event. A UE can receive an event input from an event input source, e.g., as an event input via a 1 st  modality, 2 nd  modality, . . . , N th  modality, (N+1) th  modality, etc., e.g.,  210 - 218 ,  310 - 319 ,  410 - 418 , etc. An event input source can be detectable or observable by a first UE, second UE, etc., wherein the detection and observation can be by way of the event input via the 1 st  modality, 2 nd  modality, . . . , N th  modality, (N+1) th  modality, etc. 
         [0054]    At  730 , MEC data can be received from the first UE, second UE, etc., based on the 1 to (N+1) event inputs received by the UEs at  710  and  720 . MEC data can be related to an event input source that can be proximate to the first UE, the second UE, etc. The 1 to (N+1) event inputs can inherently or explicitly comprise MEC data, e.g., the MEC data can comprise data related to a MEC of each modality-event associated with the event input source. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can be extracted from nearly any information source contemporaneously associated with an event via a given modality. Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a people in the background, the sound of a traffic in the background, etc. It will be noted that numerous other examples can be readily raised though they cannot all be explicitly stated herein for the sake of brevity and clarity. 
         [0055]    At  740 , method  700  can comprise determining MECCI based on MEC data from  730 . A characteristic of an event input received via a modality and can be determined from MEC data and be employed in an analysis of the significance of the characteristic in relation to other characteristics from other event inputs received by other modalities that are contemporaneous with the event. These can be analyzed against other event inputs from an event input source. These characteristics can be associated with the one or more input devices associated with an event input source, e.g., a UE, smartphone, tablet, wearable device, vehicle, keyboard, touchscreen, microphone, internet of things (TOT) enabled device, etc., associated with an event input source. The analysis can then determine a level of congruence between the modality-event characteristics. The determined level of MEC congruence, e.g., comprised in MECCI, can be employed to initiate a response. 
         [0056]    At  750 , method  700  can comprise enabling access to MECCI, e.g., MECCI can be made available for access by other devices, systems, methods, etc. In an aspect, MECCI can be made available to one or more UEs associated with receiving event information, e.g., the first UE at  710 , the second UE at  720 , etc., to enable a UE to respond to an input event based on the levels of congruence determined, e.g., different combinations of event inputs via different modalities, and MECs related thereto, can have different levels of congruence for each combination. By selecting a relevant combination of MECs, the associated determined level of congruence can be employed in determining a corresponding response. 
         [0057]    At  760 , method  700  can include, in response to the MECCI being determined to satisfy a rule related to a profile condition, initiating a response condition. At this point, method  700  can end. Rules and profiles can also be employed in conjunction with the analysis of the modality-event characteristics. A rule can impart broadly applicable conventions to determining a level of congruence. As an example, a rule can relate to determining similarity between waveforms, applying threshold values, defining normal rates of change in MEC values, etc. Profiles, in comparison to rules, can be employed to personalize congruence determinations. A profile value can be comprised in one or more profiles. A profile value, for example, can indicate weighting of modalities whereby some modalities can have a greater impact on MECCI than other modalities, can indicate modalities that are to be ignored, modalities that are to always be used, etc., can designate different rankings of modalities for different UEs, etc. Moreover, profiles can comprise characteristics that can be associated with individual true users, for example, images of the user, images of family members, the sound/voices of a true user&#39;s office, user schedules, input device types/models/brands/identities, etc. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile, as well as application of updateable general rules, that can be applied to the congruence determination. 
         [0058]      FIG. 8  illustrates a method  800  that facilitates determining a congruence of event characteristics captured for an event via different modalities based on remotely stored rules in accordance with aspects of the subject disclosure. At  810 , method  800  can comprise receiving MEC data. MEC data can comprise data related to a MEC. As disclosed herein, a MEC can be a characteristic associated with an event and modality. As such, a MEC can be extracted via a given modality from nearly any information source contemporaneously associated with an event input source. Modalities can include video, images, audio, EM, motion, tilt, proximity, orientation, direction, pressure, temperature, capacitance, resistance, chemical composition, etc., or even information itself, e.g., brand, model, make, manufacturer, source identification, encryption type, identified language or dialect, etc. Moreover, different characteristics can be captured for any given modality, for an example image, color saturation, facial recognition, fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., for an example audio input, volume, frequency, spoken language/dialect, sound pressure, the sound of a people in the background, the sound of a traffic in the background, etc. It will be noted that numerous other examples can be readily raised though they cannot all be explicitly stated herein for the sake of brevity and clarity. MEC data can be related to an event input source that can be proximate to a UE. A UE can receive an event input from an event input source, e.g., as an event input via an N th  mode, e.g.,  210 - 218 ,  310 - 319 ,  410 - 418 , etc. An event input source can be detectable or observable by UE, wherein the detection and observation ca be by way of the event input via the N th  mode. 
         [0059]    At  820 , Method  800  can comprise receiving a congruence rule from a data store. The data store can store congruence rules including the congruence rule received at  820 . Rules and profiles can also be employed in conjunction with the analysis of the modality-event characteristics. A rule can impart broadly applicable conventions to determining a level of congruence. As an example, a rule can relate to determining similarity between waveforms, applying threshold values, defining normal rates of change in MEC values, etc. Profiles, in comparison to rules, can be employed to personalize congruence determinations. A profile value can be comprised in one or more profiles. A profile value, for example, can indicate weighting of modalities whereby some modalities can have a greater or lesser impact on MECCI than other modalities, can indicate modalities that are to be ignored, modalities that are to always be used, etc., can designate different rankings of modalities for different UEs, etc. Moreover, profiles can comprise characteristics that can be associated with individual true users, for example, images of the user, images of family members, the sound/voices of a true user&#39;s office, user schedules, input device types/models/brands/identities, etc. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile, as well as application of updateable general rules, that can be applied to the congruence determination. 
         [0060]    Method  800 , at  830 , can comprise determining MECCI based on the MEC data, from  810 , in response to determining that the MEC data satisfies the congruence rule from  820 . As an example, where the congruence rule relates to determining a level of similarity between waveform data, where the MEC data satisfies this rule, e.g., waveforms of the MEC data are sufficiently similar (or dissimilar), then the MEC data can be employed in determining the MECCI. In an aspect, determining MECCI based on MEC data from  810  can include determine a characteristic of an event input received via a modality that can be extracted from MEC data and be employed in an analysis of the significance of the characteristic in relation to other characteristics from other event inputs received by other modalities that are contemporaneous with the event. These can be analyzed in relation to other event inputs that satisfy a rule, per  820 , and can be analyzed against other event inputs from an event input source. These characteristics can be associated with the same input source, e.g., a UE, smartphone, tablet, wearable device, vehicle, keyboard, touchscreen, microphone, internet of tings (IOT) enabled device, etc., contemporaneously observing an event input source. The analysis can then determine a level of congruence between the modality-event characteristics. The MEC congruence, e.g., MECCI, can be employed to initiate a response. 
         [0061]    At  840 , MECCI can be made available for access by other devices, systems, methods, etc. In an aspect, MECCI can be made available to a UE associated with receiving event information to enable the UE to respond to the input event based on the levels of congruence determined, e.g., different combinations of event inputs via different modalities, and MECs related thereto, can have different levels of congruence for each combination. By selecting a relevant combination of MECs, the associated determined level of congruence can be employed in determining a corresponding response. 
         [0062]    At  850 , method  800  can include, receiving profile information from the data store, wherein the data store stores a profile associated with a user identity and includes a profile comprising the profile information. At  860 , in response to the MECCI being determined to satisfy a rule related to the profile information from  850 , method  800  can comprise initiating a response condition. At this point, method  800  can end. As previously disclosed, a profile can also be employed in conjunction with the analysis of the modality-event characteristics. Profiles, in comparison to congruence rules, can be employed to personalize congruence determinations. A profile value can be comprised in one or more profiles. As such, where a plurality of modality-event characteristics are checked, there can be individualization of the congruence determination based on the user profile, as well as application of updateable general rules, that can be applied to the congruence determination. 
         [0063]      FIG. 9  is a schematic block diagram of a computing environment  900  with which the disclosed subject matter can interact. The system  900  comprises one or more remote component(s)  910 . The remote component(s)  910  can be hardware and/or software (e.g., threads, processes, computing devices). In some embodiments, remote component(s)  910  can comprise servers, personal servers, wireless telecommunication network devices, etc. As an example, remote component(s)  910  can be UEs  240 ,  340 ,  342 , etc., MECAC  422 , etc., a remote server, etc. 
         [0064]    The system  900  also comprises one or more local component(s)  920 . The local component(s)  920  can be hardware and/or software (e.g., threads, processes, computing devices). In some embodiments, local component(s)  920  can comprise, for example, MECAC  220 ,  320 ,  420 , etc., UE  440 , etc. 
         [0065]    One possible communication between a remote component(s)  910  and a local component(s)  920  can be in the form of a data packet adapted to be transmitted between two or more computer processes. Another possible communication between a remote component(s)  910  and a local component(s)  920  can be in the form of circuit-switched data adapted to be transmitted between two or more computer processes in radio time slots. The system  900  comprises a communication framework  940  that can be employed to facilitate communications between the remote component(s)  910  and the local component(s)  920 , and can comprise an air interface, e.g., Uu interface of a UMTS network. Remote component(s)  910  can be operably connected to one or more remote data store(s)  950 , such as a hard drive, solid state drive, SIM card, device memory, etc., that can be employed to store information on the remote component(s)  910  side of communication framework  940 . Similarly, local component(s)  920  can be operably connected to one or more local data store(s)  930 , that can be employed to store information on the local component(s)  920  side of communication framework  940 . 
         [0066]    In order to provide a context for the various aspects of the disclosed subject matter,  FIG. 10 , and the following discussion, are intended to provide a brief, general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented. While the subject matter has been described above in the general context of computer-executable instructions of a computer program that runs on a computer and/or computers, those skilled in the art will recognize that the disclosed subject matter also can be implemented in combination with other program modules. Generally, program modules comprise routines, programs, components, data structures, etc. that performs particular tasks and/or implement particular abstract data types. 
         [0067]    In the subject specification, terms such as “store,” “storage,” “data store,” “data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It is noted that the memory components described herein can be either volatile memory or nonvolatile memory, or can comprise both volatile and nonvolatile memory, by way of illustration, and not limitation, volatile memory  1020  (see below), non-volatile memory  1022  (see below), disk storage  1024  (see below), and memory storage  1046  (see below). Further, nonvolatile memory can be included in read only memory, programmable read only memory, electrically programmable read only memory, electrically erasable read only memory, or flash memory. Volatile memory can comprise random access memory, which acts as external cache memory. By way of illustration and not limitation, random access memory is available in many forms such as synchronous random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, Synchlink dynamic random access memory, and direct Rambus random access memory. Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory. 
         [0068]    Moreover, it is noted that the disclosed subject matter can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as personal computers, hand-held computing devices (e.g., personal digital assistant, phone, watch, tablet computers, netbook computers, . . . ), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network; however, some if not all aspects of the subject disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
         [0069]      FIG. 10  illustrates a block diagram of a computing system  1000  operable to execute the disclosed systems and methods in accordance with an embodiment. Computer  1012 , which can be, for example, MECAC  120 ,  220 ,  320 ,  420 ,  422 ,  520 , etc., UE  240 ,  340 ,  342 ,  440 , etc., comprises a processing unit  1014 , a system memory  1016 , and a system bus  1018 . System bus  1018  couples system components comprising, but not limited to, system memory  1016  to processing unit  1014 . Processing unit  1014  can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as processing unit  1014 . 
         [0070]    System bus  1018  can be any of several types of bus structure(s) comprising a memory bus or a memory controller, a peripheral bus or an external bus, and/or a local bus using any variety of available bus architectures comprising, but not limited to, industrial standard architecture, micro-channel architecture, extended industrial standard architecture, intelligent drive electronics, video electronics standards association local bus, peripheral component interconnect, card bus, universal serial bus, advanced graphics port, personal computer memory card international association bus, Firewire (Institute of Electrical and Electronics Engineers 1194), and small computer systems interface. 
         [0071]    System memory  1016  can comprise volatile memory  1020  and nonvolatile memory  1022 . A basic input/output system, containing routines to transfer information between elements within computer  1012 , such as during start-up, can be stored in nonvolatile memory  1022 . By way of illustration, and not limitation, nonvolatile memory  1022  can comprise read only memory, programmable read only memory, electrically programmable read only memory, electrically erasable read only memory, or flash memory. Volatile memory  1020  comprises read only memory, which acts as external cache memory. By way of illustration and not limitation, read only memory is available in many forms such as synchronous random access memory, dynamic read only memory, synchronous dynamic read only memory, double data rate synchronous dynamic read only memory, enhanced synchronous dynamic read only memory, Synchlink dynamic read only memory, Rambus direct read only memory, direct Rambus dynamic read only memory, and Rambus dynamic read only memory. 
         [0072]    Computer  1012  can also comprise removable/non-removable, volatile/non-volatile computer storage media.  FIG. 10  illustrates, for example, disk storage  1024 . Disk storage  1024  comprises, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, flash memory card, or memory stick. In addition, disk storage  1024  can comprise storage media separately or in combination with other storage media comprising, but not limited to, an optical disk drive such as a compact disk read only memory device, compact disk recordable drive, compact disk rewritable drive or a digital versatile disk read only memory. To facilitate connection of the disk storage devices  1024  to system bus  1018 , a removable or non-removable interface is typically used, such as interface  1026 . 
         [0073]    Computing devices typically comprise a variety of media, which can comprise computer-readable storage media or communications media, which two terms are used herein differently from one another as follows. 
         [0074]    Computer-readable storage media can be any available storage media that can be accessed by the computer and comprises both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data, or unstructured data. Computer-readable storage media can comprise, but are not limited to, read only memory, programmable read only memory, electrically programmable read only memory, electrically erasable read only memory, flash memory or other memory technology, compact disk read only memory, digital versatile disk or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible media which can be used to store desired information. In this regard, the term “tangible” herein as may be applied to storage, memory or computer-readable media, is to be understood to exclude only propagating intangible signals per se as a modifier and does not relinquish coverage of all standard storage, memory or computer-readable media that are not only propagating intangible signals per se. In an aspect, tangible media can comprise non-transitory media wherein the term “non-transitory” herein as may be applied to storage, memory or computer-readable media, is to be understood to exclude only propagating transitory signals per se as a modifier and does not relinquish coverage of all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. As such, for example, a computer-readable medium can comprise executable instructions stored thereon that, in response to execution, cause a system comprising a processor to perform operations, comprising: receiving trigger information a remote device, e.g., a UE, and in response, generating communication augmentation information that can be accessed via an air interface or other wireless interface by one or more service interface components or other UEs to enable context sensitive communication augmentation. 
         [0075]    Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and comprises any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media comprise wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
         [0076]    It can be noted that  FIG. 10  describes software that acts as an intermediary between users and computer resources described in suitable operating environment  1000 . Such software comprises an operating system  1028 . Operating system  1028 , which can be stored on disk storage  1024 , acts to control and allocate resources of computer system  1012 . System applications  1030  take advantage of the management of resources by operating system  1028  through program modules  1032  and program data  1034  stored either in system memory  1016  or on disk storage  1024 . It is to be noted that the disclosed subject matter can be implemented with various operating systems or combinations of operating systems. 
         [0077]    A user can enter commands or information into computer  1012  through input device(s)  1036 . In some embodiments, a user interface can allow entry of user preference information, etc., and can be embodied in a touch sensitive display panel, a mouse input GUI, a command line controlled interface, etc., allowing a user to interact with computer  1012 . As an example, UE  240 ,  340 ,  342 ,  440 , etc., can receive touch, motion, audio, visual, or other types of input. Input devices  1036  comprise, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, cell phone, smartphone, tablet computer, etc. These and other input devices connect to processing unit  1014  through system bus  1018  by way of interface port(s)  1038 . Interface port(s)  1038  comprise, for example, a serial port, a parallel port, a game port, a universal serial bus, an infrared port, a Bluetooth port, an IP port, or a logical port associated with a wireless service, etc. Output device(s)  1040  use some of the same type of ports as input device(s)  1036 . 
         [0078]    Thus, for example, a universal serial busport can be used to provide input to computer  1012  and to output information from computer  1012  to an output device  1040 . Output adapter  1042  is provided to illustrate that there are some output devices  1040  like monitors, speakers, and printers, among other output devices  1040 , which use special adapters. Output adapters  1042  comprise, by way of illustration and not limitation, video and sound cards that provide means of connection between output device  1040  and system bus  1018 . It should be noted that other devices and/or systems of devices provide both input and output capabilities such as remote computer(s)  1044 . 
         [0079]    Computer  1012  can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s)  1044 . Remote computer(s)  1044  can be a personal computer, a server, a router, a network PC, cloud storage, a cloud service, code executing in a cloud-computing environment, a workstation, a microprocessor based appliance, a peer device, or other common network node and the like, and typically comprises many or all of the elements described relative to computer  1012 . 
         [0080]    For purposes of brevity, only a memory storage device  1046  is illustrated with remote computer(s)  1044 . Remote computer(s)  1044  is logically connected to computer  1012  through a network interface  1048  and then physically connected by way of communication connection  1050 . Network interface  1048  encompasses wire and/or wireless communication networks such as local area networks and wide area networks. Local area network technologies comprise fiber distributed data interface, copper distributed data interface, Ethernet, Token Ring and the like. Wide area network technologies comprise, but are not limited to, point-to-point links, circuit-switching networks like integrated services digital networks and variations thereon, packet switching networks, and digital subscriber lines. As noted below, wireless technologies may be used in addition to or in place of the foregoing. 
         [0081]    Communication connection(s)  1050  refer(s) to hardware/software employed to connect network interface  1048  to bus  1018 . While communication connection  1050  is shown for illustrative clarity inside computer  1012 , it can also be external to computer  1012 . The hardware/software for connection to network interface  1048  can comprise, for example, internal and external technologies such as modems, comprising regular telephone grade modems, cable modems and digital subscriber line modems, integrated services digital network adapters, and Ethernet cards. 
         [0082]    The above description of illustrated embodiments of the subject disclosure, comprising what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as those skilled in the relevant art can recognize. 
         [0083]    In this regard, while the disclosed subject matter has been described in connection with various embodiments and corresponding Figures, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below. 
         [0084]    As it employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit, a digital signal processor, a field programmable gate array, a programmable logic controller, a complex programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor may also be implemented as a combination of computing processing units. 
         [0085]    As used in this application, the terms “component,” “system,” “platform,” “layer,” “selector,” “interface,” and the like are intended to refer to a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components. 
         [0086]    In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in the subject specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
         [0087]    Further, the term “include” is intended to be employed as an open or inclusive term, rather than a closed or exclusive term. The term “include” can be substituted with the term “comprising” and is to be treated with similar scope, unless otherwise explicitly used otherwise. As an example, “a basket of fruit including an apple” is to be treated with the same breadth of scope as, “a basket of fruit comprising an apple.” 
         [0088]    Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,” “subscriber station,” “subscriber equipment,” “access terminal,” “terminal,” “handset,” and similar terminology, refer to a wireless device utilized by a subscriber or user of a wireless communication service to receive or convey data, control, voice, video, sound, gaming, or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably in the subject specification and related drawings. Likewise, the terms “access point,” “base station,” “Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home access point,” and the like, are utilized interchangeably in the subject application, and refer to a wireless network component or appliance that serves and receives data, control, voice, video, sound, gaming, or substantially any data-stream or signaling-stream to and from a set of subscriber stations or provider enabled devices. Data and signaling streams can comprise packetized or frame-based flows. 
         [0089]    Additionally, the terms “core-network”, “core”, “core carrier network”, “carrier-side”, or similar terms can refer to components of a telecommunications network that typically provides some or all of aggregation, authentication, call control and switching, charging, service invocation, or gateways. Aggregation can refer to the highest level of aggregation in a service provider network wherein the next level in the hierarchy under the core nodes is the distribution networks and then the edge networks. UEs do not normally connect directly to the core networks of a large service provider but can be routed to the core by way of a switch or radio access network. Authentication can refer to determinations regarding whether the user requesting a service from the telecom network is authorized to do so within this network or not. Call control and switching can refer determinations related to the future course of a call stream across carrier equipment based on the call signal processing. Charging can be related to the collation and processing of charging data generated by various network nodes. Two common types of charging mechanisms found in present day networks can be prepaid charging and postpaid charging. Service invocation can occur based on some explicit action (e.g. call transfer) or implicitly (e.g., call waiting). It is to be noted that service “execution” may or may not be a core network functionality as third party network/nodes may take part in actual service execution. A gateway can be present in the core network to access other networks. Gateway functionality can be dependent on the type of the interface with another network. 
         [0090]    Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,” “prosumer,” “agent,” and the like are employed interchangeably throughout the subject specification, unless context warrants particular distinction(s) among the terms. It should be appreciated that such terms can refer to human entities or automated components (e.g., supported through artificial intelligence, as through a capacity to make inferences based on complex mathematical formalisms), that can provide simulated vision, sound recognition and so forth. 
         [0091]    Aspects, features, or advantages of the subject matter can be exploited in substantially any, or any, wired, broadcast, wireless telecommunication, radio technology or network, or combinations thereof. Non-limiting examples of such technologies or networks comprise broadcast technologies (e.g., sub-Hertz, extremely low frequency, very low frequency, low frequency, medium frequency, high frequency, very high frequency, ultra-high frequency, super-high frequency, terahertz broadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g., Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi; worldwide interoperability for microwave access; enhanced general packet radio service; third generation partnership project, long term evolution; third generation partnership project universal mobile telecommunications system; third generation partnership project  2 , ultra mobile broadband; high speed packet access; high speed downlink packet access; high speed uplink packet access; enhanced data rates for global system for mobile communication evolution radio access network; universal mobile telecommunications system terrestrial radio access network; or long term evolution advanced. 
         [0092]    What has been described above includes examples of systems and methods illustrative of the disclosed subject matter. It is, of course, not possible to describe every combination of components or methods herein. One of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.