WO2011067675A2 - Système de notification ciblé sur le comportement et l'attention et procédé d'utilisation associé - Google Patents

Système de notification ciblé sur le comportement et l'attention et procédé d'utilisation associé Download PDF

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Publication number
WO2011067675A2
WO2011067675A2 PCT/IB2010/003436 IB2010003436W WO2011067675A2 WO 2011067675 A2 WO2011067675 A2 WO 2011067675A2 IB 2010003436 W IB2010003436 W IB 2010003436W WO 2011067675 A2 WO2011067675 A2 WO 2011067675A2
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WO
WIPO (PCT)
Prior art keywords
user
information
activity
act
notification
Prior art date
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PCT/IB2010/003436
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English (en)
Other versions
WO2011067675A3 (fr
Inventor
Tara Rosenberger Shankar
Aurélien Guillou
Chaochi Chang
Adrian Budiu
Original Assignee
France Telecom
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by France Telecom filed Critical France Telecom
Priority to EP10816319A priority Critical patent/EP2507759A2/fr
Publication of WO2011067675A2 publication Critical patent/WO2011067675A2/fr
Publication of WO2011067675A3 publication Critical patent/WO2011067675A3/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
    • H04M15/80—Rating or billing plans; Tariff determination aspects
    • H04M15/8033—Rating or billing plans; Tariff determination aspects location-dependent, e.g. business or home
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/535—Tracking the activity of the user
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
    • H04M15/80—Rating or billing plans; Tariff determination aspects
    • H04M15/8083—Rating or billing plans; Tariff determination aspects involving reduced rates or discounts, e.g. time-of-day reductions or volume discounts
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
    • H04M15/80—Rating or billing plans; Tariff determination aspects
    • H04M15/8088—Rating or billing plans; Tariff determination aspects involving increased rates, e.g. spam messaging billing differentiation

Definitions

  • the present system relates to at least one of a system, method, device, computer program, user interface, and apparatus for targeting content to a user device and, more particularly, to a targeted ad content delivery system based upon a predicted behavior or attention shift of a user.
  • geographic based advertising systems push ads to a user upon detecting the user's presence in a certain location.
  • One such system is disclosed in, for example, in U.S. Patent 6,452,498, incorporated herein as if set out in its entirety, in which content is pushed to a mobile unit of a user when the mobile unit accesses an access point connected to a network. More specifically, when the access point detects the presence of the mobile unit, the access point sends a signal to a network indicting the presence of the mobile unit and information requested by the mobile unit. Based upon the signal received from the access point, the network communicates with information providers connected to the network and provides data to the mobile unit through the access point corresponding to the location of the mobile unit.
  • this system uses location data to send specific advertising data to the mobile unit of the user which may arrive at a time that is inconvenience for the user. Further,
  • a system and/or a method which can identify one or more times to deliver ads that are targeted a user's socio-cultural segment, and which can identify opportune moments in a user's routines and/or tasks structure and deliver ads in these opportune moments.
  • the BATMN system can direct advertising delivery at times when the user is predicted to have unstructured time (e.g., unhabitual periods of time), is switching from one routine activity to the next, and/or is in the process of switching attention focus and therefore is susceptible to respond to an advertisement, such as a just in time0 advertisement (e.g., an advertisement that has a shortened period of applicability, such as a quick order food coupon that is only redeemable for the next hour).
  • a just in time0 advertisement e.g., an advertisement that has a shortened period of applicability, such as a quick order food coupon that is only redeemable for the next hour.
  • the present system may transmit ads, such as ads targeted to a5 particular user device (targeted ad), such as a mobile station (MS), upon detecting that a switching event related to the user of the user device, has occurred.
  • ads such as ads targeted to a5 particular user device (targeted ad), such as a mobile station (MS)
  • the present system may identify times which are cognitively salient for transmitting and/or rendering stimuli such as, for example, ads to a user of, for example, an MS, a personal computer (PC), and the like.
  • the present system may determine, generate,0 transmit and/or render stimuli such as, for example, ads, notifications, updates, etc., to a user when the user's attention and/or cognitive function may be conducive to reception of the stimuli.
  • the present system discloses a cost efficient system and/or method to generate, transmit, and/or render stimuli.
  • a further advantage of the present system is that it may operate in a mobile and/or stationary user environment.
  • the present system may predict user activates (e.g., routines) and/or determine user attention shifts based on past detected user behavior so as to identify different types of advertising notification opportunities which may be available to the system.
  • the present system may be compatible with call management systems and may select, generate, transmit, and/or render, notification information based upon a predicted or determined switching event related to the user.
  • the present system may block incoming professional calls when the system detects that a user is engaged in an activity (e.g., is driving a vehicle) and/or may generate notification information such as call information (e.g., missed calls and/or current calls at the time of the switching event) and/or voice message information (as described herein, as the notification or a portion thereof) upon determining that a user is no longer engaged in the predicted activity such as at a time when a switching event is predicted or determined.
  • notification information such as call information (e.g., missed calls and/or current calls at the time of the switching event) and/or voice message information (as described herein, as the notification or a portion thereof) upon determining that a user is no longer engaged in the predicted activity such as at a time when a switching event is predicted or determined.
  • the present system provides a system, method, device, computer program, user interface, and apparatus for transmitting content to a user device, the method including acts of receiving user information from one or more sensors; obtaining user history information comprising one or more of historical location information and historical activity information; determining, based upon the user information and the history information, whether a switching event has occurred for a user of the user device; and delivering a notification if a switching event is determined to have occurred.
  • the method may include a further act of determining a confidence level of a determined switching event, wherein the act of delivering the notification includes an act of delivering the notification if a switching event is determined above a predetermined threshold confidence level.
  • the user information from the one or more sensors may include one or more of location information, acceleration information, pressure information, in call sensor information, light sensor information, and magnetic orientation information.
  • the method may include a further an act of updating the history information with the received user information.
  • a further act may include an act of determining a type of notification to send based on the determined switching event.
  • the type of notification may include one of a just in time advertisement, a hyper-local advertisement and a hyper-activity sensitive advertisement.
  • the act of determining may include acts of determining that a first activity is ending; predicting that a second activity will begin shortly; and identifying a period of time between the end of the first activity and before the beginning of the second activity, where the act of delivering the notification comprises an act of delivering the notification during the identified period.
  • a further act may include an act of requesting confirmation from the user within a user interface provided on the user device that a given portion of the user history information corresponds to a given activity.
  • a further act may include an act of requesting confirmation from the user within a user interface provided on the user device that the user information corresponds to a given activity.
  • the act of determining may include an act of comparing the user information to the history information to identify if the user information corresponds to a portion of the history information captured during an identified user activity.
  • the act of delivering may include acts of identifying at least one of a current user activity that corresponds to the user information and an activity that is predicted to follow a current user activity; and delivering a notification that is related to at least one of the current user activity and the activity that is predicted to follow the current user activity.
  • the act of delivering may include an act of delivering call information to the user device if the switching event is determined to have occurred.
  • the method may include an act of not delivering call information to the user when an activity is determined to be occurring, and delivering the not delivered call information if the switching event is determined to have occurred.
  • FIG. 1 show an illustrative activity graph depicting notification opportunities available based upon a sequential activity structure of a user in accordance with an embodiment of the present system
  • FIG. 2 shows a graph of a weekday profile for a "Regular worker" in accordance with the present system
  • FIG. 3 shows a sequence diagram of elements in accordance with an embodiment of the present system
  • FIG. 4 shows an activity recognition component diagram in accordance with an embodiment of the present system.
  • FIG. 5 shows a portion of a system in accordance with an embodiment of the present system.
  • an operative coupling may include one or more of a wired connection and/or a wireless connection between two or more devices that enables a one and/or two-way communication path between the devices and/or portions thereof.
  • an operative coupling may include a wired and/or wireless coupling to enable communication between a content server and one or more user devices.
  • a further operative coupling, in accordance with the present system may include one or more couplings between two or more user devices, such as via a network source, such as the content server, in accordance with an embodiment of the present system.
  • rendering and formatives thereof as utilized herein refer to providing content, such as digital media which may include, for example, stimuli such as ads, notifications, etc., such that it may be perceived by at least one user sense, such as a sense of sight and/or a sense of hearing.
  • the present system may render a user interface on a display device so that it may be seen and interacted with by a user.
  • the present system may render audio visual content on both of a device that renders audible output (e.g., a speaker, such as a loudspeaker) and a device that renders visual output (e.g., a display).
  • a device that renders audible output e.g., a speaker, such as a loudspeaker
  • a device that renders visual output e.g., a display
  • the term content and formatives thereof will be utilized and should be understood to include audio content, visual content, audio visual content, textual content and/or other content types, unless a particular content type is specifically intended
  • switching event and formative thereof is intended to refer to times when a user is predicted to have unstructured time (e.g., unhabitual periods of time), is switching from one routine activity to the next, and/or is in the process of switching attention focus.
  • a switching event in operation of the present system, is not always instantaneous from a cognitive perspective and may be measured in terms of a Reaction Time (RT) delay between activities.
  • RT Reaction Time
  • a switching event may include, for example, a shift in user activity such as, when, for example, when a user dismounts a bicycle, leaves a vehicle such as, for example, a train, plane, car, bus, etc.
  • the present system it is believed that during an attention-shift, a user's attention may be more likely to shift and/or consume notification information such advertisements, etc., than during other periods.
  • the present system is directed to detecting activities and times there between.
  • routine may refer to a recurrent similarity in user's behavior of time and/or location.
  • a routine may include recurrent behavior such as driving to work each morning, a physical exercise routine performed during the evening, etc.
  • any event that is regularly occurring for a user may be detectable by a user device, such as a mobile phone that has time keeping capability as well as one or more sensors that may be utilized for detection of the event, such as a routine activity.
  • the present system may determine routines using, for example, a statistical model such as, a Hidden Markov Model (HMM) which may produce a representation of the user's routine behavior over time and space with a confidence level having a range that is between 0 and 1 (e.g., (0.0-1.0)) and may be represented as a percentage of confidence (e.g., 0-100%).
  • HMM Hidden Markov Model
  • Each predicted activity and/or location may have a corresponding confidence level such that it is more likely (e.g., greater than 50%) or less likely (e.g., less than 50%) to be a given determined activity as described further herein.
  • the system, device(s), method, user interface, etc., described herein address problems in prior art systems.
  • a system, method, device, computer program, and interface for rendering a Ul for a users convenience may include one or more applications which are necessary to complete an assigned task.
  • a common interface device for a user interface such as a graphical user interface (GUI) is a mouse, trackball, keyboard, touch-sensitive display, etc.
  • GUI graphical user interface
  • a mouse may be moved by a user in a planar workspace to move a visual object, such as a cursor, depicted on a two-dimensional display surface in a direct mapping between the position of the user manipulation and the depicted position of the cursor. This is typically known as position control, where the motion of the depicted object directly correlates to motion of the user manipulation.
  • GUI an example of such a GUI in accordance with an embodiment of the present system is a GUI that may be provided by a computer program that may be user invoked, such as to enable a user to view and/or to select rendered content.
  • the GUI may present a typical Ul including a windowing environment and as such, may include menu items, pull-down menu items, pop-up windows, etc., that are
  • GUI 10 typical of those provided in a windowing environment, such as may be represented within a WindowsTM Operating System GUI as provided by Microsoft Corporation and/or an OS XTM Operating System GUI, such as provided on an iPhoneTM, MacBookTM, iMacTM, etc., as provided by Apple, Inc., and/or another operating system.
  • the objects and sections of the GUI may be navigated utilizing a user input device, such as a i s mouse, trackball, finger, and/or other suitable user input.
  • the user input may be utilized for making selections within the GUI such as by selection of menu items, window items, radio buttons, pop-up windows, for example, in response to a mouseover operation, and other common interaction paradigms as understood by a person of ordinary skill in the art.
  • Similar interfaces may be provided by a device having a touch sensitive screen that is operated on by an input device such as a finger of a user or other input device such as a stylus.
  • an input device such as a finger of a user or other input device such as a stylus.
  • a cursor may or may not be provided since location of selection is directly determined by the location of interaction with the touch sensitive screen.
  • GUI utilized for supporting touch sensitive inputs may be5 somewhat different than a GUI that is utilized for supporting, for example, a computer mouse input, however, for purposes of the present system, the operation is similar. Accordingly, for purposes of simplifying the foregoing description, the interaction discussed is intended to apply to either of these systems or others that may be suitably applied.
  • the present system may identify certain types of opportunities to serve highly targeted notification information (e.g., render notification information within a Ul of a user device) such as, for example, advertising information, at determined times (e.g., during a switching event) when a corresponding user is most able or likely to perceive 5 and/or consume the notification information.
  • notification information such as, for example, advertisements
  • the present system may select the notification information in accordance with a user's routine.
  • information related to a user's routine such as, for example, information related to a user's routine such as, for
  • a user's location, activity, schedule, and/or attention structure may be used to select notification information.
  • the present system may select and/or generate notification information such as advertisement information that may include local advertising with time-delimited offers (e.g., a restaurant has available seating and offers mobile users i s close by a buy-one-get-one-free offer to come and eat in the next 15 minutes).
  • advertisement information may include local advertising with time-delimited offers (e.g., a restaurant has available seating and offers mobile users i s close by a buy-one-get-one-free offer to come and eat in the next 15 minutes).
  • the present system may operate to deliver time-delimited offers in a manner that mobile users have a higher likelihood of focusing and/or acting on the advertisement, then for example, during a time when the user is otherwise engaged in an activity.
  • the present system may, for example, deliver mobile advertising information to an MS of a user in a helpful, simple, and/or convenient way which may coincide with natural perceptual-cognitive rhythms of the user. Further, just as importantly as determining when to deliver and/or render information, the present system may determine when not to deliver and/or render notification information to a5 user and can, thus, reduce and/or prevent information saturation and/or "tuning out" by the user. Accordingly, the present system may provide a filter for notification information such as, for example, advertisement information, which may identify particular content delivery opportunities which may occur when a user shifts attention and/or is between routines, activities, etc., and characterize these moments as non-0 routine or routine. The system may then determine a type of advertisement to be delivered or rendered based, for example, upon an identification of a routine or activity that is ending, had ended or is about to begin. For example, the present system may render advertisement information only during a switching event.
  • advertisement information such as, for example, advertisement information, which may identify particular content
  • the present 5 system may identify one or more NRMs, to deliver notification information which may include, advertisements, call information, etc., to an MS of a user.
  • the present system may determine opportune times to select, deliver, and/or render the content information. Further, the present system may select notification information which is most appropriate to the identified moment. With respect to the processing stages, they lo may be identified as: 1. an attention shift process which may include, for example, a process of monitoring the user's attention to identify particular moments of attentional shift of a user; 2.
  • an inverse routine process which may include, for example, a process of determining the user's routines in location and activity across time, to select one or more non-routine moments (NRMs) which correspond with non-routine times; a i s determination on suitable content to deliver during a given NRM; and delivery of the determined content.
  • NRMs non-routine moments
  • the present system may use these processes (e.g., detection of a switching event, such as an attentional shift; the inverse routine process, endfing of an activity, etc.) either alone or in combination thereof. Accordingly, the present system may0 precisely deliver targeted notification information such as, for example, targeted advertising, etc., to one or more users in a "cost efficient" manner. For example, with regard to non-routine times, the present system may identify when a user is in a non- routine moment (e.g., "free time") and deliver notification information such as, for example, advertising information to the user during this time.
  • a non- routine moment e.g., "free time”
  • the present5 system may also select and/or determine one or more classes of local mobile advertising designed for immediate uptake by a user which may be cost efficient (i.e., delivered at a time when the user is not involved in an activity as described herein.).
  • the system may match the user with time dependent (e.g., immediate, etc.) local business needs, such as0 restaurants with available seating, groceries that must be sold today, theme parks that are running at less than full capacity, etc., and transmit notification information in accordance with the match to an MS of the user.
  • the system may also render notification information, such as, for example, time-sensitive offers to, for example, one or more users (or groups of users) who are determined to have free time.
  • the system may select and/or render notification information such as, for example, half-price tickets for an impromptu showing of a blockbuster film to these users. Further, the system may receive information such as, for example, confirmation information from the users. Thus, the system may inform the user's that the tickets may be good only if fifty people show up or respond to the offer in, for example, the next ten minutes, and may await a confirmation from the user's. Then upon, receiving a confirmation that is greater than a threshold (e.g., fifty), the system may inform the user's and/or the movie theater of the determination.
  • a threshold e.g., fifty
  • the present system may identify switching events in a user's day such as described above, it may also identify advertising opportunities that may be, for example, location and/or activity specific, and may be independent of time (i.e., time insensitive). For example, upon determining that a user jogs regularly, the system may deliver notification information specific to this activity before and/or after the user's activity (e.g., at a time when the activity is terminating and before a subsequent activity begins.
  • this process may include any suitable method or methods which may determine an attentions! shift or perceptual-cognitive transition of a user.
  • the system may use real-time personal sensing mobile technology such as, real-time information from one or more sensors such as, location sensors or systems (e.g., global positiong system (GPS), wireless fidelity (WIFI), cell identification (CelllD), triangulation, etc.), acceleration sensors (e.g., accelerometers), magnetic sensors (e.g., magnetometers, compasses, etc.), eye blink or movement sensors, microphones, pressure sensors, and/or other sensors to identify an attention shift of a user.
  • the system may use any suitable psychological based method to determine an attention shift of a user.
  • the present system may use the sensor information to identify certain types of attention shifts which may coincide with changes in a user's determined activity.
  • the present system may use various sensor information to identify activity transitions corresponding with an attention shift which may occur when, for example, a user transitions from a sitting position to a standing position, gets off a bicycle, parks a car, exits or enters a car, etc. These activity transitions may be considered correlates of a switching event of the user.
  • the present system may also interface with other mobile and/or personal sensing technology so as to recognize a broad range of activity correlates of a switching event, and other more direct correlates of a switching event.
  • this process may predict a user's routine and/or non-routine activity with a certain level of confidence using any suitable method such as, for example, probabilistic techniques such as a Hidden Markov Model (HMM), through time, e.g., the time of the day, day of the week, day of the month, and calendar year.
  • Routine behaviors can be predicted with a history of personal sensing data such as the user's location, accelerometer, compass data, etc.
  • the user has demonstrated through previous behavior that there are times during the day and day of the week and calendar year when their behavior is routine. For example, on weekday mornings a user drives to work, goes out to lunch at noon, and drives home at 5 P.M. after work.
  • the system may compare each of the level of confidence for each of the predicted routine and/or non-routine activities of the user, with one or more predetermined thresholds to select one or more most-likely routine or non-routine activities of the user. Further, the one or more predetermined thresholds may change depending upon time, location, and behavior of the user. For example, if it 5 P.M. and the user is detected to be driving in the car, the system may determine with a high confidence level (> 50%) that the user is driving home and with prior info on the users driving habits, such as route followed, the present system may deliver a verbal notification (since the user is driving) of a coupon at a bakery along the route, that bread is available at a discount. However, in a case wherein it is 3 P.M. and the user is detected to be driving in the car, the system may not be able to determine with a high confidence level (> 50%) where the user is driving, and therefore, the present system may not deliver a notification or other content.
  • the inverse of a user's routine moments for example, those moments when a user is considered to have free, unstructured, and/or unhabitual times (NRM).
  • NRM free, unstructured, and/or unhabitual times
  • the system may determine a NRM occurs when, for example, a user is predicted with a high degree of confidence to do something different every weekend day (e.g., shop, fish, surf, boat, ski, etc.) and/or go to different location on every Friday night. Further, the system may determine a NRM occurs when, for example, it is determined that a user is on vacation and, thus, is not considered to be in the usual location. The system may determine that a user is on vacation or otherwise not performing a routine, activity, etc., by determining that a user is located somewhere other than previously detected "routine" locations.
  • the system may determine a "cost factor" for each NRM and may select notification information to be delivered during each NRMs. Because the user is highly susceptible to be influenced during a switching event, notification information such as advertisements rendered during NRMs may command a higher premium (e.g., price) than advertisements rendered at other times (e.g., routine times). Accordingly, the system may include a cost application which may compute costs and/or select advertisements or other notification information based upon a cost factor of an NRM. The system may determine cost factors based upon time, location, and or activities (e.g., on vacation, etc.) of a user.
  • cost factors based upon time, location, and or activities (e.g., on vacation, etc.) of a user.
  • the system may apply a higher cost factor to a time period that corresponds with a user on vacation in a tourist resort far from the user's home than to a time period in which a user is traveling to/from work since it is likely that the user is more susceptible to an advertisement, for example, that is delivered when the user is having a switching event while on vacation.
  • the present system may render targeted local mobile advertising at an opportune time for user consumption.
  • a detection of a switching event in accordance with the present system corresponds to a determination that a user has shifted focus from, for example, ended an activity.
  • the switching event is determined in accordance with the present system, for example based upon user sensor information and/or user history information.
  • the process may also use a statistic application (e.g., an HMM) to determine a confidence factor and associate this confidence factor with the results of the determination.
  • a statistic application e.g., an HMM
  • the detection of a switching event may be determined in real-time and may capture a user's activity structure to determine whether a user has a switching event.
  • notification information may be optionally delayed by a certain amount of time (e.g., 10 seconds, etc.) such that it may be rendered within preferred time period after the system detects the switching event. Accordingly, this may ensure that notification information may be rendered when a user is most cognitively able to perceive the message, understand its meaning, build connections and relations with other things the person knows, integrate the message into the users intentions and goals, and/or act upon the message.
  • the delay may correspond with a type of notification message, a type of triggering event, etc., a user's selection, the system's selection, etc.
  • the present system may select notification information and/or a time to deliver the selected notification information according to a type of switching event of a user. Further, the system may associate a reaction time "switch cost" to a moment (e.g., a time or time period) when it is determined that a user switches from one activity to another different activity.
  • the present system may employ methods that deliver notification information such as an advertisement during a switch cost moment which occurs when a user is between activities.
  • a user may be considered to between activities when, for example, it is determined that a user has ended an activity (e.g., driving to the user's office, etc.) and has not yet started another activity (e.g., beginning to work inside the office, etc.).
  • a switch cost moment may be an ideal time to deliver and/or render notification information as it is during this time that the notification information may be integrated with a user's activity sequence (e.g., breakfast, diving kids to school, driving to work, working, picking kids up from school, etc.).
  • the present system may use one or more moments when it is determined that a user's attention can serially or simultaneously interact with notification information rendered on an MS in an efficient manner, such as between detected activities. Accordingly, the present system is based on the recognition that there are one or more times that a user id more likely to consume and/or interact with the notification information rendered one an MS of the user.
  • the suitable time for delivery of content is based on a recognition that users perform tasks generally in a serial manner, such as a sequence of activities such as writing an email response, editing a spreadsheet, submitting travel expenses, etc. Accordingly, the present system may determine an opportune time to render notification information that corresponds with a time when the user is susceptible to the content. The system may then select corresponding notification information to render for the user's convenience.
  • Cognitive theories of executive control of attention such as, for example, "bottleneck" theory of attention and performance are known in the art and are disclosed by, for example, Broadbent (1958). More particularly, Broadbent discloses how a person attends serially or simultaneously to phenomenon in the person's environment.
  • the present system incorporates sensor- based activity recognition. It is further envisioned that the present system may be compatible with sensing technologies which may be developed in the future such that the present system may recognize various types of attentional shifts of a user thus expanding a range of events to which present system may respond.
  • FIG. 1 show an illustrative activity graph 100 depicting notification opportunities available based upon a sequential activity structure of a user in accordance with an embodiment of the present system.
  • the activity structure shown in graph 100 illustrates detected activities in accordance with the present system.
  • the present system operates based on an understanding that during times of changing from one activity to another, there is a cognitive switch cost, such as switch costs 108, 110 depicted in graph 100, which is a time when the user is more susceptible to delivered content, then for example, at time when the user is engaged in an activity.
  • the present system may detect switching events which occur, for example, when a user shifts from one activity to another activity.
  • the graph 100 depicts three sequential activities: Activity A 102, riding bicycle to a friend's house; Activity B 104, greeting the friend; and Activity C 106, playing video games.
  • Activity A 102 may include subtasks of: "Reading directions...,” “Avoiding cars...,” “Observing traffic.”
  • the present system may deliver notification information, such as for example, advertisements, during the switch costs 108-1 10 which are a correlate of a switching even which may be detected to occur between activities 102-106. Accordingly, the system may determine a time period (e.g., the switch costs 108) which occurs after a first activity (e.g., after activity 102) has ended and/or before another activity (e.g., activity 104) has begun. Then the system may send and/or render notification information during this time period between activities when the user is more susceptible to the notification information.
  • a time period e.g., the switch costs 108 which occurs after a first activity (e.g., after activity 102) has ended and/or before another activity (e.g., activity 104) has begun. Then the system may send and/or render notification information during this time period between activities when the user is more susceptible to the notification information.
  • the system can minimize or entirely prevent interruption of a user while the user is performing an activity. Further, by providing the notification information between activities, the notification information may be efficiently consumed by the user such that it may have a maximum effect upon the user. Moreover, by rendering the notification information during a period between activities, the system may reduce or prevent interruption of the user during an activity such as, for example, driving, operating equipment, work, etc, which can enhance a user's convenience and/or safety.
  • the present system may use sensor information such as may be provided by external and/or internal sensors 120, such as based within the MS of the user and/or based on external sensors that are operative to provide detection information in accordance with an embodiment of the present system.
  • the sensor 120 information may include, for example, mobile device embedded personal sensors (e.g., a GPS sensor, accelerometer, etc.), and system sensing applications (e.g., remote location applications such as is may be provided by mobile service providers.).
  • the system may also receive signals from one or more remotely located sensors such as, for example, proximity sensors (e.g., radio frequency identification (RFID) proximity sensors, etc.) and use this information to predict whether a switching event is occurring, whether the user is engaged in/terminating an activity, etc.
  • RFID radio frequency identification
  • the detection of periods between activities, shown as switch costs 108, 1 10, may be based, at least in part, upon received sensor information (e.g., barometric information, location information, acceleration information, in-call sensor information, ambient light information, compass information, etc.), and/or a user's interaction with an MS. Accordingly, the system may use the sensor information and a data classifier model to determine or recognize certain user activities (e.g. conversation, sitting, walking, cycling, driving, etc.) that the user is engaged in or that have just ended. Thus, upon detecting that the user is going to switch from one activity to another activity or other detections of a switching event, the system may render notification information.
  • sensor information e.g., barometric information, location information, acceleration information, in-call sensor information, ambient light information, compass information, etc.
  • the system may use the sensor information and a data classifier model to determine or recognize certain user activities (e.g. conversation, sitting, walking, cycling, driving, etc.) that the user is engaged in or that
  • the system may select (e.g., from a memory such as a database, etc.), generate, transmit and/or render notification information in response to the switching event and may be generated and/or selected in accordance with the activity that the user is detected to have ended, is currently engaged in, and is predicted to next engage in.
  • the system may also determine whether the activities are recognized with a high confidence status (e.g. >75% correct) by, for example, comparing statistical information related to the detected activity (e.g., determined by the system) with a threshold value (e.g., 0.75). Accordingly, if the statistical information related to the detected activity is greater than or equal to the threshold value, then the system may have detected the activity with a high confidence.
  • a high confidence status e.g. >75% correct
  • the system may determine statistical information related to the detected activity in accordance with sensor information. For example, the present system by accumulating sensor information that confirms the detected activity, the system may determine with a higher confidence that an activity is occurring. For example, in a case where the user has a detected activity of driving home after work, detection of a different route than the historical route for the activity, may result in a low confidence ( ⁇ 50%) that the user is engaged in the activity.
  • the present system may be trained to a user such that the user's activities may be recognized by the system.
  • a data classifier model may include training information specific to the user (or class of users (e.g., walkers, runners, skiers, bikers, business executives, etc.).
  • 5 data classifier model may include information which may easily identify a user who is walking such as, for example, acceleration information in one or more axes (e.g., x, y, and/or z axes).
  • the training information may be stored in a local and/or remote memory and may be updated by the system such that it reflects current user information (e.g., schedules, activities, etc.) of a user.
  • a predicted activity change may be predicted before the user begins the predicted activity change, such as when a user is bicycling (e.g., a detected activity) and is approaching a place wherein the user has historically ended the activity.
  • the system may deliver notification information at predicted moments of transition when a user is transitions from one activity to another activity.
  • the system may predict this transition ahead of time, for example in an off-line process (e.g., by analyzing historical information, such as before a recurrence of the activity), and may further refine the prediction in real-time based on the activity sensing from the mobile device.
  • the system may use schedule0 information (e.g., 8:00-8:30 am commute to work; 8:30-9:930 conference at work) and/or historical information (e.g., user arrives at work at 8:30 each morning), to predict that a user will change activities at about 8:30 am.
  • the system may use sensor information (e.g., accelerometer information, compass information, location information, etc.), to predict that the user is about to enter work when, for example, the user is5 determined to walk away from his commuting vehicle (e.g., car, bus, train, plane, ferry, etc.) and is in proximity (e.g., 100 feet determined using location information provided by the sensors) to the user's place (or places) of work.
  • the present system may operate in an online and/or offline environment.
  • a routine may be considered a recurrent similarity in a user's activity and/or location, dependent upon, or independent of, time and/or location. For example, the user may leave his house by car at 8:30 am each morning, but go to a different final destination at different historical times. This routine may be considered as time dependent and location independent. 5
  • the user may also have routine in location but not in activity, for example, the user may commute to the office at a different time each day during a 24 hour period, and/or go to work at the office intermittently (e.g., 2 days a week), etc. Further, the user may have a routine in both activity and location as well; for example, a user goes to the Highland Avenue Grocery at 9:00 am every Monday. Therefore, the system may match a current
  • a process for providing notification information such as advertisements during the user's inverse routine behavior (e.g., non-habitual behavior) will now be described.
  • the present system also provides a process for filtering out advertising i s delivery during a user's activities.
  • Information such as a time history of a user's location, and activities within the user's locations, may be stored as user history information and may be used to algorithmically determine days of the week and/or times of the day when a user has time periods in which the user is unusually open to new experiences and offers (e.g., advertisements, etc.) and therefore is determined to be0 open to be influenced by notification information rendered to the user. Additionally, the user may welcome certain information during these time periods.
  • time periods may be referred to as “highly influence-able time” or highly influence-able moments (HIMs) and may extend throughout the switching event (e.g., for the entire period between an activity) or in accordance with an embodiment of the present system, may5 be limited to a given time after detection that an activity has ended (e.g., if a period between activities is predicted to last 10 minutes, the notification may be delivered two minutes thereafter to reach a user at a time when cognitively, the first activity has ended and prior to when the user cognitively is ready to engage in a next activity.
  • HIMs highly influence-able moments
  • the present system may use predictive modeling techniques such as, for0 example, clustering, Markov models, etc., to predict, within a certain level of confidence, a user's established activities. Conversely, at times when an activity cannot be predicted (i.e., non-routine times) with a high degree of confidence, the present system will refrain from render notifications.
  • predictive modeling techniques such as, for0 example, clustering, Markov models, etc.
  • detection of user activities may be based, at least in part, upon history information which may include a user's time-stamped history of location (e.g. GPS/WIFI/CelllD data, etc.) and activities (e.g. recognition of activities constructed from raw accelerometer data, elicited confirmation from the user, etc.). Times of the day, day of the week, day of the month, month of the year are examples of times when routines may be predicted.
  • history information may include a user's time-stamped history of location (e.g. GPS/WIFI/CelllD data, etc.) and activities (e.g. recognition of activities constructed from raw accelerometer data, elicited confirmation from the user, etc.). Times of the day, day of the week, day of the month, month of the year are examples of times when routines may be predicted.
  • Routines may be modeled using, for example, a statistical technique such as an HMM using history information, such as, for example, location and activity data of the device or of the user (which may be useful when the user has multiple devices (e.g., BlackberryTM, IPhoneTM, etc.) which the user may use at different times (e.g., BlackberryTM at work during week, IPhoneTM on non-working weekends).
  • the system may build and update (e.g., continuously at set times, etc.) an HMM model for each user so that it is in accordance with present location and activity data of the user.
  • the system may obtain user information from, for example, a user's MS, a user's personal computer (PC), a user's Internet or web activity (e.g., an activity of for example, a user's e-mail service, a user's social networking service (e.g., Twitter, Facebook, etc.).
  • the system may use the user information to model (e.g., mathematically) the user's activities.
  • a change over time in a user's activities is may be modeled by the system with a temporal decay factor that may bias the model towards newer data.
  • the HMM may update data collection as a background process by the system on, for example, a device of the user (e.g., the MS, etc.).
  • a user may be solicited for confirmation of an activity.
  • the MS may solicit the user to confirm that they have just finished riding their bike, to aid in a confidence level of a future prediction or determination of the activity.
  • the system may identify an activity and/or non-activity moments, for determining when to deliver advertising.
  • FIGs. 2 through 4 are exemplary embodiments of use cases for delivering advertising information to a user according to an embodiment of the present system. Three exemplary user profiles are illustrated below in FIGs.
  • FIGs. 2-4 demonstrate a process for determining highly influence-able moments of a user and delivering different types of advertising opportunities for the respective user.
  • a visualization of a typical working day including a time-based representation of a user's activities and associated confidence levels (e.g., 0-100 percent corresponding with a range of 0.0 - 1.0) from the HMM process according to the present system are shown.
  • FIGs. 2-4 it is assumed that each user has a different profile type however, it is also envisioned that two or more user's may share a profile type or parts thereof. For example, a group of users such as, for example, teachers who work in the same school, may have a similar schedule during working hours.
  • the system may determine that one or more time periods for this group may be shared and grouped together as a single profile type for a certain period of time (e.g., weekdays from 8:30 am - 3:00 pm). This may be useful for cold starts, and non-typical periods such as, for example, school holiday periods, etc., when it may be desirable to determine a non-typical period (i.e., school holiday) for one or more users. Further, the system may include default and other predefined user profile types such that a user may be easily recognized and so that cold starts, which may occur when insufficient data is available for a user, may be prevented.
  • FIG. 2 shows a graph 200 of a weekday profile for a "Regular worker" in accordance with the present system.
  • the user is determined to be a factory worker that travels to a factory around the same time every weekday and reports to work by 7:30 am. The user then typically dines at the same restaurant at about the same time for lunch every weekday and leaves work at about 5:00 pm each weekday. In the evenings, the user often spends time with friends watching sport at home or at a local pub.
  • This information corresponds with a model generated by the system for the user and saved in the user history information. For the sake of clarity, a 24 hour period is shown. However, other time periods are also envisioned.
  • user history information corresponding with the user is loaded by the system.
  • the user history information may include one or more of day, date, time, location, activity information, and sensor information (e.g., call information, velocity information, etc.).
  • the system may then use the history information and current sensor information to determine a predicted user location and/or activity information 202.
  • the predicted user location and activity information 202 may also include corresponding confidence values 206 (shown as a % of confidence) which are determined by the system as a confidence level for a detected activity.
  • the system may compare the confidence values with a threshold value to determine whether the predicted current user location and activity information can be predicted with confidence (i.e., at a level which is greater than or equal to a threshold value). Using this method the system may accurately predict activities during a user's daily schedule or any other selected time period (e.g., weekly, etc.) by for example, detecting recurrent behavior along with sensor and/or user confirmation of an activity.
  • the system may determine activities over the course of time (e.g., such as a day, etc.) for each user based upon the user history information, the predicted user location and activity information 202.
  • the detected activities may correspond with a location (at work, at restaurant, at home, etc.) and time.
  • a non-routine periods (e.g., period 210) may correspond with highly influence-able time periods. During these periods, a notification 220 may be delivered to a user device for rendering by the user.
  • the activities and/or the non activity periods may be determined using any suitable modeling technique (e.g., an HMM model, etc.) in accordance with statistical info, may predict a confidence factor (i.e., probability) for each predicted location and/or activity for the user for a user over the course of time.
  • a confidence factor i.e., probability
  • notification information e.g., advertisements, etc.
  • the user may historically not engage in a regular occurring activity, which could be interpreted in accordance with an embodiment of the present system as a non-routine/non-activity period. Therefore, in accordance with an embodiment of the present system, notification information (e.g., advertisements, etc.) may be selected for the user (or type of user, e.g., regular worker) which are not time-sensitive during transitions between identified activities (e.g. driving to the factory, working at the factory), advertisements that are relevant to particular types of activities could be delivered before and after those activities (e.g. back support brace ads for people who lift a lot, and orthopedic shoes for people who stand a lot), and local advertisements that are time sensitive (e.g. 50% off Budweiser at a pub other than a pub where the user has a detected activity) may be delivered to the user device on weekday evenings.
  • notification information e.g., advertisements, etc.
  • the present system may transmit and/or render different types of notification information to a user interface (Ul) such as, for example, Ul of an MS, a PC, etc, based on a detected activity.
  • a user interface such as, for example, Ul of an MS, a PC, etc
  • an ending of one activity may be detected in real time.
  • the system may determine based on the activity detection (and/or based on a predicted next activity) an appropriately matched advertisement notification.
  • the notification may be sent prior to or while the user is orienting to a new activity, preferably during the "switch cost" cognitive delay, or very soon thereafter.
  • notification information such as, for example, advertisements that may be selected and/or generated by the system of a user are described below.
  • a user usually takes a morning coffee break from her work routine at 10 A.M. at Jammin' Java every weekday morning, yet the users current location/time/activity can not be determined with a high confidence (e.g., > 50%), the present system will not deliver a notification.
  • the present system may transmit notification information to the user's MS.
  • the notification information selected may include information such as, for example, current and forecast weather, the latest news, new emails and new voicemails for the user's convenience since the user is predicted to have a non-activity period (e.g., sitting an drinking coffee) following the bicycling activity.
  • the notification information may also include advertisement information such as, for example, an ad from Jammin' Java - "You've reached another customer milestone with 10 purchases! Have a free cappuccino on us today!
  • the notification information may also include other location specific ads from stores in the area, and may be based on the user's predicted interests and/or needs.
  • the notification information may further include information that is deemed to be relevant to the user's determined activity such as, for example, advertisement information related to bicycling (e.g., bicycle equipment advertisements, highly rated local bike paths, safety issues, etc.).
  • the notification sent to the user may be targeted, local (e.g., within the immediate neighborhood or along the route back to the office), hyper-location sensitive (e.g., directly related to the Jammin' Java location), hyper-activity sensitive (e.g., related to biking, walking, drinking coffee, etc.), but not time sensitive as user is predicted to be between activities and the predicted next activity is a return to work which is known from historical information, to always occur fifteen minutes later at 10:30.
  • hyper-location sensitive e.g., directly related to the Jammin' Java location
  • hyper-activity sensitive e.g., related to biking, walking, drinking coffee, etc.
  • the user may have historical behavior patterns including remaining in the local area around home and engaging in one or more activities. For example, the user may drive a car to a deli, park the car, and walk to the deli. Upon detecting that the user is walking from the parked car, the system may send notification information to the user's MS.
  • the notification information may include an ad from the deli and may include a coupon (e.g., 50% off on all grocery fruits).
  • the recognized activities include driving and walking.
  • the activities are not identified with confidence because the activities are not routine (e.g., are not previously identified activities and locations).
  • a notification may be sent that is targeted, local, hyper-location sensitive, and time sensitive (as the user has predicted free, flexible time right now).
  • FIG. 3 shows a sequence diagram 300 of elements including sensor management 310, activity portion 320 and device applications 330 in accordance with an embodiment of the present system.
  • the sensor management 310 may include location/change in location detection 312 and activity recognition 314 which includes historical information 316 of past detected activities or other behaviors that may be determined to be activities as more sensor data is correlated.
  • the location detection 312 may include information related to a current location and activity including associated confidence levels which together with location and activity history are utilized by the activity portion 320.
  • the activity portion includes an activity management system 322 and switching event detection 324.
  • the activity management system 322 may utilize user feedback from, for example, the device application 330, to determine/confirm that an activity has occurred including a confidence level to facilitate a determination that information provided by the sensor management correlates to the determined/confirmed activity.
  • the activity management system 322 upon detection/prediction of an activity, leads to switching event detection 324 (e.g., a time between detected/predicted activities).
  • the switching event detection 324 may be supplemented by user feedback to confirm a switching event.
  • the prediction component e.g., activity management system 322
  • the prediction component may be based on an HMM solution which provides calculated results (e.g., activity based routines, location based activities, activity + location based routines), along with a confidence value.
  • Other opportunities may be provided for the user to provide feedback to the present system that may be used to adapt the system to the user's preferences and activities.
  • the switching event detection may result in generation of a notification, the contents of which may be determined based on an advertisement database 336, that may be local to the user device and/or may be located on an advertisement server.
  • Other device applications 330 may include a mobile application, such as arranged to produce a Ul in accordance with the present system, and a call management function 334, in a case wherein the user device is a mobile phone.
  • the call management function 334 may, for example, select, generate, transmit, and/or render notification information such as call and/or message notification information such as ring tones, incoming messages, etc. at a time when a switching event is detected.
  • the present system may be compatible with call management systems and may select, generate, transmit, and/or render, notification information based upon a predicted or determined switching event related to the user. Accordingly, for example, the present system may block incoming professional calls when the system detects that a user is engaged in an activity (e.g., is driving a vehicle) and/or may generate notification information such as call information (e.g., missed calls and/or current calls at the time of the switching event) and/or voice message information (as described herein, as the notification or a portion thereof) upon determining that a user is no longer engaged in the predicted activity such as at a time when a switching event is predicted or determined.
  • call information e.g., missed calls and/or current calls at the time of the switching event
  • voice message information as described herein, as the notification or a portion thereof
  • the system components may include components related to activity recognition which in accordance with an embodiment of the present system may be implemented directly on a user device, such as a mobile device.
  • a user device such as a mobile device.
  • particular portions of the present system may be processed on the user device or on a remote device depending on the devices embedded sensor capabilities (e.g., embedded GPS, accelerometer, available memory, etc.). For example, certain activities may be identified using raw data from the user's speed calculated from a GPS and/or from the accelerometer sensor of the device (e.g., using basic thresholds or machine learning techniques) or may be determined by a remote device monitoring cell tower transition activity.
  • embedded sensor capabilities e.g., embedded GPS, accelerometer, available memory, etc.
  • certain activities may be identified using raw data from the user's speed calculated from a GPS and/or from the accelerometer sensor of the device (e.g., using basic thresholds or machine learning techniques) or may be determined by a remote device monitoring cell tower transition activity.
  • FIG. 4 shows an activity recognition component diagram 400 in accordance with an embodiment of the present system.
  • the present system may utilize raw sensor data 410, including data from a GPS 412 that may produce location data, an accelerometer 414 that provides acceleration data and/or other sensors 416, all of which may be updated periodically, such as every second or portion thereof.
  • Raw data from the sensors may be stored as a sensor data history 420.
  • minimum, maximum, mean and standard deviations may be calculated for purposes of determining thresholds and confidence levels for activity recognition.
  • the raw sensor data together with the calculated data 424 may be utilized for activity recognition 430 including data processing 432 to identify/reject data as corresponding to an activity.
  • feature extraction 434 may be performed to identify features of the recognized activity. Examples of features of an activity may include location data, average and real-time speeds, acceleration data, low-band basic frequencies, low-band energy values, peaks / s in the signal.
  • the activities may be classified 436 to produce a list of recognized activities with confidence level values, such as: ⁇ walking (75%), cycling (50%), driving (15%) ⁇ .
  • a decision tree 440 may aid in activity detection including a regeneration of the decision tree 450 which may be performed periodically (e.g., weekly) to account for changes in activities or behavior related to activities.
  • a routines management system in accordance with the present system may identify routines during the hours of the day, days of the week, days of the month and so on, for example, in an offline process. Some activities that are detected may be location specific activities (e.g., you have been detected to be at the office between 9am-12pm every week day), may be routine activities (e.g., you have been determined to be driving between 8 am-8:30am every morning), and routines in activity and location (e.g., you have been determined to be sitting in a chair at your workplace). In accordance with an embodiment of the present system, such identified routines may be associated/determined with confidence level values generated from probabilistic models, such as Hidden Markov Models (HMM).
  • HMM Hidden Markov Models
  • Such confidence level values may be between 0 and 1 , or 0% and 100%. For example, a given activity may be detected with a 75% confidence level, whereas another activity may be detected with an 80% confidence level. If the confidence value of a determined activity exceeds the required threshold of the implemented system, the system may use the identified activity, location etc., in the system to determine which kind of notification (e.g., advertisement) to deliver to the user in real-time.
  • a given activity may be detected with a 75% confidence level, whereas another activity may be detected with an 80% confidence level.
  • FIG. 5 shows a portion of a system 500 in accordance with an embodiment of the present system.
  • a portion of the present system may include a user device 590 including a processor 510 operationally coupled to a memory 520, a display 530 and a user input device 570.
  • the memory 520 may be any type of device for storing application data as well as other data related to the described operation.
  • the application data and other data are received by the processor 510 for configuring (e.g., programming) the processor 510 to perform operation acts in accordance with the present system.
  • the processor 510 so configured becomes a special purpose machine particularly suited for performing in accordance with the present system.
  • the operation acts may include identifying activities, confidence levels, receiving notifications, providing, and/or rendering of notifications, etc.
  • the user input 570 may include a keyboard, mouse, trackball or other device, including touch sensitive displays, which may be stand alone or be a part of a system, such as part of a personal computer, personal digital assistant, mobile phone, set top box, television or other device for communicating with the processor 510 via any operable link.
  • the user input device 570 may be operable for interacting with the processor 510 including enabling interaction within a Ul as described herein.
  • the processor 510, the memory 520, display 530 and/or user input device 570 may all or partly be a portion of a computer system or other device such as a user and/or server device as described herein.
  • the methods of the present system are particularly suited to be carried out by a computer software program, such program containing modules corresponding to one or more of the individual steps or acts described and/or envisioned by the present system.
  • a computer software program such program containing modules corresponding to one or more of the individual steps or acts described and/or envisioned by the present system.
  • Such program may of course be embodied in a computer-readable medium, such as an integrated chip, a peripheral device or memory, such as the memory 520 or other memory coupled to the processor 510.
  • the program and/or program portions contained in the memory 520 configure the processor 510 to implement the methods, operational acts, and functions disclosed herein.
  • the memories may be distributed, for example between the user device and/or servers, or local, and the processor 510, where additional processors may be provided, may also be distributed or may be singular.
  • the memories may be implemented as electrical, magnetic or optical memory, or any combination of these or other types of storage devices.
  • the term "memory" should be construed broadly enough to encompass any information able to be read from or written to an address in an addressable space accessible by the processor 510. With this definition, information accessible through a network is still within the memory, for instance, because the processor 510 may retrieve the information from the network for operation in accordance with the present system.
  • the processor 510 is operable for providing control signals and/or performing operations in response to input signals from the user input device 570 as well as in response to other devices of a network and executing instructions stored in the memory 520.
  • the processor 510 may be an application-specific or general-use integrated circuit(s). Further, the processor 510 may be a dedicated processor for performing in accordance with the present system or may be a general-purpose processor wherein only one of many functions operates for performing in accordance with the present system.
  • the processor 510 may operate utilizing a program portion, multiple program segments, or may be a hardware device utilizing a dedicated or multi-purpose integrated circuit.
  • the present system may dynamically push targeted advertising based upon predicted and/or real-time activity of a user.
  • the present system may incorporate wired and/or wireless communication methods and may provide a user with a personalized environment. Further benefits of the present system include low cost and scalability.
  • the present system may push targeted advertising to user device (UD) when an unstructured time period is detected and/or when it is determined that a user has switched activities. In accordance with the present system, these times are determined to be optimal for a user to consume the targeted advertising.
  • UD user device
  • the present system may complement existing location-based targeted advertising techniques and improve existing advertising methods by selecting, generating, sending, and/or rendering advertisement messages at individually determined and cognitively salient moments which correspond with determined routines tasks of a user.
  • the present system may be utilized to determine whether user has switched activities and may push targeted content which corresponds with a user's location and/or user profile information such as a predicted behavior of a user.
  • the present system may be provided in a form of a content rendering device, such as a mobile station.
  • a further embodiment of the present system may provide a Ul that operates as a browser extension, such as a rendered browser toolbar, that can render content such as notification information on a Ul of a user device.
  • any of the disclosed elements may be comprised of hardware portions (e.g., including discrete and integrated electronic circuitry), software portions (e.g., computer programming), and any combination thereof;
  • f) hardware portions may be comprised of one or both of analog and digital portions
  • any of the disclosed devices or portions thereof may be combined together or separated into further portions unless specifically stated otherwise;
  • the term "plurality of an element includes two or more of the claimed element, and does not imply any particular range of number of elements; that is, a plurality of elements may be as few as two elements, and may include an immeasurable number of elements.

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Abstract

La présente invention concerne un système, un procédé, un dispositif, un programme informatique, une interface utilisateur et un appareil permettant de transmettre un contenu à un dispositif utilisateur. Le procédé consiste à recevoir des informations d'un utilisateur à partir d'un ou de plusieurs capteurs; obtenir des informations d'historique de l'utilisateur comprenant une ou plusieurs informations historiques de position et une ou plusieurs informations historiques d'activité; déterminer, sur la base des informations de l'utilisateur et des informations d'historique, si un événement de commutation est survenu pour un utilisateur du dispositif utilisateur; et fournir une notification s'il a été déterminé qu'un événement de commutation est survenu.
PCT/IB2010/003436 2009-12-01 2010-11-30 Système de notification ciblé sur le comportement et l'attention et procédé d'utilisation associé WO2011067675A2 (fr)

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EP10816319A EP2507759A2 (fr) 2009-12-01 2010-11-30 Système de notification ciblé sur le comportement et l'attention et procédé d'utilisation associé

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