Patent Grant
8307029
A great deal of information is generated when people use electronic devices, such as when people use mobile phones and cable set-top boxes. Such information, such as location, applications used, social network, physical and online locations visited, to name a few, could be used to deliver useful services and information to end users, and provide commercial opportunities to advertisers and retailers. However, most of this information is effectively abandoned due to deficiencies in the way such information may be captured. For example, and with respect to a mobile phone, information is generally not gathered while the mobile phone is idle (i.e., not being used by a user). Other information, such as presence of others in the immediate vicinity, time and frequency of messages to other users, and activities of a user's social network are also not captured effectively.
This disclosure describes systems and methods for using data collected and stored by multiple devices on a network in order to improve the performance of the services provided via the network. In particular, the disclosure describes systems and methods delivering communications associated with delivery conditions in which the occurrence of the delivery condition is determined by monitoring information received from a plurality of sources via multiple communication channels. The message delivery systems allow messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. A message (which may be any data object including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) is delivered in accordance with delivery conditions based on any available data, including topical, spatial, temporal, and/or social data. Furthermore, because the systems coordinate delivery of messages via multiple communication channels and through multiple devices, the communication channel for delivery of a message may be dynamically determined based on the delivery conditions.
One aspect of the disclosure is a method for delivering messages that includes receiving a request to deliver a first message from a sender to a recipient, such request identifying at least one delivery condition. The method then identifies at least one real world entity (RWE) or information object (IO) associated with the at least one delivery condition. Data associated with each of the identified at least one RWE or IO is then retrieved and monitored for information indicating that the at least one delivery condition is met. The first message is delivered when the at least one delivery condition is met.
Another aspect of the disclosure is a computer-readable medium encoding instructions for performing a method for delivery of a message. The method includes detecting a first message from a sender for delivery to a recipient when a delivery condition is meet and monitoring data associated with at least one RWE related to the delivery condition. The first message is then delivered when the data associated with the at least one RWE indicates that the delivery condition is met. In situations where the delivery condition identifies a first RWE and a range of distances between the recipient and the first RWE, the method may further include identifying a second RWE that is a proxy for a current location of the recipient, periodically retrieving location data describing a current location of the second RWE and a current location of the first RWE and determining the distance between the current location of the second RWE to the current location of the first RWE. In situations where the delivery condition is detection that the recipient is at an event associated with an event location and an event time period the method may include identifying a mobile device that is a proxy for a current location of the recipient, the mobile device being the at least one RWE, retrieving location data describing a current location of the mobile device during the event time period, and determining a current distance between the current location of the mobile device and the event location. In situations where the delivery condition is detection that the recipient is at an event associated with an event location and an event time period, the method may include identifying a mobile device that is a proxy for a current location of the recipient, the mobile device being the at least one RWE, retrieving location data describing a current location of the mobile device during the event time period, and determining a current distance between the current location of the mobile device and the event location.
In yet another aspect, the disclosure describes a system that includes a data collection engine connected via at least one communication channel to a plurality of computing devices transmitting IOs over the at least one communication channel. The system further includes computer-readable media connected to the data collection engine storing at least one of social data, spatial data, temporal data and logical data associated with a plurality of RWEs including the plurality of computing devices, such social data, spatial data, temporal data and logical data collected by the data collection engine from the IOs transmitted by the plurality of computing devices. The system further includes a message delivery engine that, based on the detection of a request to deliver a message to a recipient when a delivery condition is met, identifies a first set of one or more of the plurality of RWEs as associated with the delivery condition and tests the data from the computer-readable medium for the identified one or more of the plurality of RWEs for occurrence of the delivery condition.
These and various other features as well as advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. Additional features are set forth in the description that follows and, in part, will be apparent from the description, or may be learned by practice of the described embodiments. The benefits and features will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
The following drawing figures, which form a part of this application, are illustrative of embodiments systems and methods described below and are not meant to limit the scope of the disclosure in any manner, which scope shall be based on the claims appended hereto.
This disclosure describes a communication network, referred herein as the “W4 Communications Network” or W4 COMN, that uses information related to the “Who, What, When and Where” of interactions with the network to provide improved services to the network's users. The W4 COMN is a collection of users, devices and processes that foster both synchronous and asynchronous communications between users and their proxies. It includes an instrumented network of sensors providing data recognition and collection in real-world environments about any subject, location, user or combination thereof.
As a communication network, the W4 COMN handles the routing/addressing, scheduling, filtering, prioritization, replying, forwarding, storing, deleting, privacy, transacting, triggering of a new message, propagating changes, transcoding and linking. Furthermore, these actions can be performed on any communication channel accessible by the W4 COMN.
The W4 COMN uses a data modeling strategy for creating profiles for not only users and locations but also any device on the network and any kind of user-defined data with user-specified conditions from a rich set of possibilities. Using Social, Spatial, Temporal and Logical data available about a specific user, topic or logical data object, every entity known to the W4 COMN can be mapped and represented against all other known entities and data objects in order to create both a micro graph for every entity as well as a global graph that interrelates all known entities against each other and their attributed relations.
In order to describe the operation of the W4 COMN, two elements upon which the W4 COMN is built must first be introduced, real-world entities and information objects. These distinction are made in order to enable correlations to be made from which relationships between electronic/logical objects and real objects can be determined. A real-world entity (RWE) refers to a person, device, location, or other physical thing known to the W4 COMN. Each RWE known to the W4 COMN is assigned or otherwise provided with a unique W4 identification number that absolutely identifies the RWE within the W4 COMN.
RWEs may interact with the network directly or through proxies, which may themselves be RWEs. Examples of RWEs that interact directly with the W4 COMN include any device such as a sensor, motor, or other piece of hardware that connects to the W4 COMN in order to receive or transmit data or control signals. Because the W4 COMN can be adapted to use any and all types of data communication, the devices that may be RWEs include all devices that can serve as network nodes or generate, request and/or consume data in a networked environment or that can be controlled via the network. Such devices include any kind of “dumb” device purpose-designed to interact with a network (e.g., cell phones, cable television set top boxes, fax machines, telephones, and radio frequency identification (RFID) tags, sensors, etc.). Typically, such devices are primarily hardware and their operations can not be considered separately from the physical device.
Examples of RWEs that must use proxies to interact with W4 COMN network include all non-electronic entities including physical entities, such as people, locations (e.g., states, cities, houses, buildings, airports, roads, etc.) and things (e.g., animals, pets, livestock, gardens, physical objects, cars, airplanes, works of art, etc.), and intangible entities such as business entities, legal entities, groups of people or sports teams. In addition, “smart” devices (e.g., computing devices such as smart phones, smart set top boxes, smart cars that support communication with other devices or networks, laptop computers, personal computers, server computers, satellites, etc.) are also considered RWEs that must use proxies to interact with the network. Smart devices are electronic devices that can execute software via an internal processor in order to interact with a network. For smart devices, it is actually the executing software application(s) that interact with the W4 COMN and serve as the devices' proxies.
The W4 COMN allows associations between RWEs to be determined and tracked. For example, a given user (an RWE) may be associated with any number and type of other RWEs including other people, cell phones, smart credit cards, personal data assistants, email and other communication service accounts, networked computers, smart appliances, set top boxes and receivers for cable television and other media services, and any other networked device. This association may be made explicitly by the user, such as when the RWE is installed into the W4 COMN. An example of this is the set up of a new cell phone, cable television service or email account in which a user explicitly identifies an RWE (e.g., the user's phone for the cell phone service, the user's set top box and/or a location for cable service, or a username and password for the online service) as being directly associated with the user. This explicit association may include the user identifying a specific relationship between the user and the RWE (e.g., this is my device, this is my home appliance, this person is my friend/father/son/etc., this device is shared between me and other users, etc.). RWEs may also be implicitly associated with a user based on a current situation. For example, a weather sensor on the W4 COMN may be implicitly associated with a user based on information indicating that the user lives or is passing near the sensor's location.
An information object (IO), on the other hand, is a logical object that stores, maintains, generates, serves as a source for or otherwise provides data for use by RWEs and/or the W4 COMN. IOs are distinct from RWEs in that IOs represent data, whereas RWEs may create or consume data (often by creating or consuming IOs) during their interaction with the W4 COMN. Examples of IOs include passive objects such as communication signals (e.g., digital and analog telephone signals, streaming media and interprocess communications), email messages, transaction records, virtual cards, event records (e.g., a data file identifying a time, possibly in combination with one or more RWEs such as users and locations, that may further be associated with a known topic/activity/significance such as a concert, rally, meeting, sporting event, etc.), recordings of phone calls, calendar entries, web pages, database entries, electronic media objects (e.g., media files containing songs, videos, pictures, images, audio messages, phone calls, etc.), electronic files and associated metadata.
In addition, IOs include any executing process or application that consumes or generates data such as an email communication application (such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaring application, a word processing application, an image editing application, a media player application, a weather monitoring application, a browser application and a web page server application. Such active IOs may or may not serve as a proxy for one or more RWEs. For example, voice communication software on a smart phone may serve as the proxy for both the smart phone and for the owner of the smart phone.
An IO in the W4 COMN may be provided a unique W4 identification number that absolutely identifies the IO within the W4 COMN. Although data in an IO may be revised by the act of an RWE, the IO remains a passive, logical data representation or data source and, thus, is not an RWE.
For every IO there are at least three classes of associated RWEs. The first is the RWE who owns or controls the IO, whether as the creator or a rights holder (e.g., an RWE with editing rights or use rights to the IO). The second is the RWE(s) that the IO relates to, for example by containing information about the RWE or that identifies the RWE. The third are any RWEs who then pay any attention (directly or through a proxy process) to the IO, in which “paying attention” refers to accessing the IO in order to obtain data from the IO for some purpose.
“Available data” and “W4 data” means data that exists in an IO in some form somewhere or data that can be collected as needed from a known IO or RWE such as a deployed sensor. “Sensor” means any source of W4 data including PCs, phones, portable PCs or other wireless devices, household devices, cars, appliances, security scanners, video surveillance, RFID tags in clothes, products and locations, online data or any other source of information about a real-world user/topic/thing (RWE) or logic-based agent/process/topic/thing (IO).
As mentioned above the proxy devices 104, 106, 108, 110 may be explicitly associated with the user 102. For example, one device 104 may be a smart phone connected by a cellular service provider to the network and another device 106 may be a smart vehicle that is connected to the network. Other devices may be implicitly associated with the user 102. For example, one device 108 may be a “dumb” weather sensor at a location matching the current location of the user's cell phone 104, and thus implicitly associated with the user 102 while the two RWEs 104, 108 are co-located. Another implicitly associated device 110 may be a sensor 110 for physical location 112 known to the W4 COMN. The location 112 is known, either explicitly (through a user-designated relationship, e.g., this is my home, place of employment, parent, etc.) or implicitly (the user 102 is often co-located with the RWE 112 as evidenced by data from the sensor 110 at that location 112), to be associated with the first user 102.
The user 102 may also be directly associated with other people, such as the person 140 shown, and then indirectly associated with other people 142, 144 through their associations as shown. Again, such associations may be explicit (e.g., the user 102 may have identified the associated person 140 as his/her father, or may have identified the person 140 as a member of the user's social network) or implicit (e.g., they share the same address).
Tracking the associations between people (and other RWEs as well) allows the creation of the concept of “intimacy”: Intimacy being a measure of the degree of association between two people or RWEs. For example, each degree of removal between RWEs may be considered a lower level of intimacy, and assigned lower intimacy score. Intimacy may be based solely on explicit social data or may be expanded to include all W4 data including spatial data and temporal data.
Each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144 of the W4 COMN may be associated with one or more IOs as shown. Continuing the examples discussed above,
Furthermore, those RWEs which can only interact with the W4 COMN through proxies, such as the people 102, 140, 142, 144, computing devices 104, 106 and location 112, may have one or more IOs 132, 134, 146, 148, 150 directly associated with them. An example includes IOs 132, 134 that contain contact and other RWE-specific information. For example, a person's IO 132, 146, 148, 150 may be a user profile containing email addresses, telephone numbers, physical addresses, user preferences, identification of devices and other RWEs associated with the user, records of the user's past interactions with other RWE's on the W4 COMN (e.g., transaction records, copies of messages, listings of time and location combinations recording the user's whereabouts in the past), the unique W4 COMN identifier for the location and/or any relationship information (e.g., explicit user-designations of the user's relationships with relatives, employers, co-workers, neighbors, service providers, etc.). Another example of a person's IO 132, 146, 148, 150 includes remote applications through which a person can communicate with the W4 COMN such as an account with a web-based email service such as Yahoo! Mail. The location's IO 134 may contain information such as the exact coordinates of the location, driving directions to the location, a classification of the location (residence, place of business, public, non-public, etc.), information about the services or products that can be obtained at the location, the unique W4 COMN identifier for the location, businesses located at the location, photographs of the location, etc.
In order to correlate RWEs and IOs to identify relationships, the W4 COMN makes extensive use of existing metadata and generates additional metadata where necessary. Metadata is loosely defined as data that describes data. For example, given an IO such as a music file, the core, primary or object data of the music file is the actual music data that is converted by a media player into audio that is heard by the listener. Metadata for the same music file may include data identifying the artist, song, etc., album art, and the format of the music data. This metadata may be stored as part of the music file or in one or more different IOs that are associated with the music file or both. In addition, W4 metadata for the same music file may include the owner of the music file and the rights the owner has in the music file. As another example, if the IO is a picture taken by an electronic camera, the picture may include in addition to the primary image data from which an image may be created on a display, metadata identifying when the picture was taken, where the camera was when the picture was taken, what camera took the picture, who, if anyone, is associated (e.g., designated as the camera's owner) with the camera, and who and what are the subjects of/in the picture. The W4 COMN uses all the available metadata in order to identify implicit and explicit associations between entities and data objects.
Some of items of metadata 206, 214, on the other hand, may identify relationships between the IO 202 and other RWEs and IOs. As illustrated, the IO 202 is associated by one item of metadata 206 with an RWE 220 that RWE 220 is further associated with two IOs 224, 226 and a second RWE 222 based on some information known to the W4 COMN. This part of
As this is just a conceptual model, it should be noted that some entities, sensors or data will naturally exist in multiple clouds either disparate in time or simultaneously. Additionally, some IOs and RWEs may be composites in that they combine elements from one or more clouds. Such composites may be classified or not as appropriate to facilitate the determination of associations between RWEs and IOs. For example, an event consisting of a location and time could be equally classified within the When cloud 306, the What cloud 308 and/or the Where cloud 304.
The W4 engine 310 is center of the W4 COMN's central intelligence for making all decisions in the W4 COMN. An “engine” as referred to herein is meant to describe a software, hardware or firmware (or combinations thereof) system, process or functionality that performs or facilitates the processes, features and/or functions described herein (with or without human interaction or augmentation). The W4 engine 310 controls all interactions between each layer of the W4 COMN and is responsible for executing any approved user or application objective enabled by W4 COMN operations or interoperating applications. In an embodiment, the W4 COMN is an open platform upon which anyone can write an application. To support this, it includes standard published APIs for requesting (among other things) synchronization, disambiguation, user or topic addressing, access rights, prioritization or other value-based ranking, smart scheduling, automation and topical, social, spatial or temporal alerts.
One function of the W4 COMN is to collect data concerning all communications and interactions conducted via the W4 COMN, which may include storing copies of IOs and information identifying all RWEs and other information related to the IOs (e.g., who, what, when, where information). Other data collected by the W4 COMN may include information about the status of any given RWE and IO at any given time, such as the location, operational state, monitored conditions (e.g., for an RWE that is a weather sensor, the current weather conditions being monitored or for an RWE that is a cell phone, its current location based on the cellular towers it is in contact with) and current status.
The W4 engine 310 is also responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN. The function of identifying RWEs associated with or implicated by IOs and actions performed by other RWEs is referred to as entity extraction. Entity extraction includes both simple actions, such as identifying the sender and receivers of a particular IO, and more complicated analyses of the data collected by and/or available to the W4 COMN, for example determining that a message listed the time and location of an upcoming event and associating that event with the sender and receiver(s) of the message based on the context of the message or determining that an RWE is stuck in a traffic jam based on a correlation of the RWE's location with the status of a co-located traffic monitor.
It should be noted that when performing entity extraction from an IO, the IO can be an opaque object with only W4 metadata related to the object (e.g., date of creation, owner, recipient, transmitting and receiving RWEs, type of IO, etc.), but no knowledge of the internals of the IO (i.e., the actual primary or object data contained within the object). Knowing the content of the IO does not prevent W4 data about the IO (or RWE) to be gathered. The content of the IO if known can also be used in entity extraction, if available, but regardless of the data available entity extraction is performed by the network based on the available data. Likewise, W4 data extracted around the object can be used to imply attributes about the object itself, while in other embodiments, full access to the IO is possible and RWEs can thus also be extracted by analyzing the content of the object, e.g. strings within an email are extracted and associated as RWEs to for use in determining the relationships between the sender, user, topic or other RWE or IO impacted by the object or process.
In an embodiment, the W4 engine 310 represents a group of applications executing on one or more computing devices that are nodes of the W4 COMN. For the purposes of this disclosure, a computing device is a device that includes a processor and memory for storing data and executing software (e.g., applications) that perform the functions described. Computing devices may be provided with operating systems that allow the execution of software applications in order to manipulate data.
In the embodiment shown, the W4 engine 310 may be one or a group of distributed computing devices, such as a general-purpose personal computers (PCs) or purpose built server computers, connected to the W4 COMN by suitable communication hardware and/or software. Such computing devices may be a single device or a group of devices acting together. Computing devices may be provided with any number of program modules and data files stored in a local or remote mass storage device and local memory (e.g., RAM) of the computing device. For example, as mentioned above, a computing device may include an operating system suitable for controlling the operation of a networked computer, such as the WINDOWS XP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.
Some RWEs may also be computing devices such as smart phones, web-enabled appliances, PCs, laptop computers, and personal data assistants (PDAs). Computing devices may be connected to one or more communications networks such as the Internet, a publicly switched telephone network, a cellular telephone network, a satellite communication network, a wired communication network such as a cable television or private area network. Computing devices may be connected any such network via a wired data connection or wireless connection such as a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephone connection.
Local data structures, including discrete IOs, may be stored on a mass storage device (not shown) that is connected to, or part of, any of the computing devices described herein including the W4 engine 310. For example, in an embodiment, the data backbone of the W4 COMN, discussed below, includes multiple mass storage devices that maintain the IOs, metadata and data necessary to determine relationships between RWEs and IOs as described herein. A mass storage device includes some form of computer-readable media and provides non-volatile storage of data and software for retrieval and later use by one or more computing devices. Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by a computing device.
By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.
The next layer is the data layer 406 in which the data produced by the sensor layer 402 is stored and cataloged. The data may be managed by either the network 404 of sensors or the network infrastructure 406 that is built on top of the instrumented network of users, devices, agents, locations, processes and sensors. The network infrastructure 408 is the core under-the-covers network infrastructure that includes the hardware and software necessary to receive that transmit data from the sensors, devices, etc. of the network 404. It further includes the processing and storage capability necessary to meaningfully categorize and track the data created by the network 404.
The next layer of the W4 COMN is the user profiling layer 410. This layer 410 may further be distributed between the network infrastructure 408 and user applications/processes 412 executing on the W4 engine or disparate user computing devices. In the user profiling layer 410 that functions as W4 COMN's user profiling layer 410. Personalization is enabled across any single or combination of communication channels and modes including email, IM, texting (SMS, etc.), photobloging, audio (e.g. telephone call), video (teleconferencing, live broadcast), games, data confidence processes, security, certification or any other W4 COMN process call for available data.
In one embodiment, the user profiling layer 410 is a logic-based layer above all sensors to which sensor data are sent in the rawest form to be mapped and placed into the W4 COMN data backbone 420. The data (collected and refined, related and deduplicated, synchronized and disambiguated) are then stored in one or a collection of related databases available to all processes of all applications approved on the W4 COMN. All Network-originating actions and communications are based upon the fields of the data backbone, and some of these actions are such that they themselves become records somewhere in the backbone, e.g. invoicing, while others, e.g. fraud detection, synchronization, disambiguation, can be done without an impact to profiles and models within the backbone.
Actions originating from anything other than the network, e.g., RWEs such as users, locations, proxies and processes, come from the applications layer 414 of the W4 COMN. Some applications may be developed by the W4 COMN operator and appear to be implemented as part of the communications infrastructure 408, e.g. email or calendar programs because of how closely the operate with the sensor processing and user profiling layer 410. The applications 412 also serve some role as a sensor in that they, through their actions, generate data back to the data layer 406 via the data backbone concerning any data created or available due to the applications execution.
The applications layer 414 also provides a personalized user interface (UI) based upon device, network, carrier as well as user-selected or security-based customizations. Any UI can operate within the W4 COMN if it is instrumented to provide data on user interactions or actions back to the network. This is a basic sensor function of any W4 COMN application/UI, and although the W4 COMN can interoperate with applications/UIs that are not instrumented, it is only in a delivery capacity and those applications/UIs would not be able to provide any data (let alone the rich data otherwise available from W4-enabled devices.)
In the case of W4 COMN mobile devices, the UI can also be used to confirm or disambiguate incomplete W4 data in real-time, as well as correlation, triangulation and synchronization sensors for other nearby enabled or non-enabled devices. At some point, the network effects of enough enabled devices allow the network to gather complete or nearly complete data (sufficient for profiling and tracking) of a non-enabled device because of it's regular intersection and sensing by enabled devices in it's real-world location.
Above the applications layer 414 (and sometimes hosted within it) is the communications delivery network(s) 416. This can be operated by the W4 COMN operator or be independent third-party carrier service, but in either case it functions to deliver the data via synchronous or asynchronous communication. In every case, the communication delivery network 414 will be sending or receiving data (e.g., http or IP packets) on behalf of a specific application or network infrastructure 408 request.
The communication delivery layer 418 also has elements that act as sensors including W4 entity extraction from telephone calls, emails, blogs, etc. as well as specific user commands within the delivery network context, e.g., “save and prioritize this call” said before end of call may trigger a recording of the previous conversation to be saved and for the W4 entities within the conversation to analyzed and increased in weighting prioritization decisions in the personalization/user profiling layer 410.
In one embodiment the W4 engine connects, interoperates and instruments all network participants through a series of sub-engines that perform different operations in the entity extraction process. One such sub-engine is an attribution engine 504. The attribution engine 504 tracks the real-world ownership, control, publishing or other conditional rights of any RWE in any IO. Whenever a new IO is detected by the W4 engine 502, e.g., through creation or transmission of a new message, a new transaction record, a new image file, etc., ownership is assigned to the IO. The attribution engine 504 creates this ownership information and further allows this information to be determined for each IO known to the W4 COMN.
The W4 engine 502 further includes a correlation engine 506. The correlation engine 506 operates in two capacities: first, to identify associated RWEs and IOs and their relationships (such as by creating a combined graph of any combination of RWEs and IOs and their attributes, relationships and reputations within contexts or situations) and second, as a sensor analytics pre-processor for attention events from any internal or external source.
In one embodiment, the identification of associated RWEs and IOs function of the correlation engine 506 is done by graphing the available data. In this embodiment, a histogram of all RWEs and IOs is created, from which correlations based on the graph may be made. Graphing, or the act of creating a histogram, is a computer science method of identify a distribution of data in order to identify relevant information and make correlations between the data. In a more general mathematical sense, a histogram is simply a mapping mi that counts the number of observations that fall into various disjoint categories (known as bins), whereas the graph of a histogram is merely one way to represent a histogram. By selecting each IO, RWE, and other known parameters (e.g., times, dates, locations, etc.) as different bins and mapping the available data, relationships between RWEs, IOs and the other parameters can be identified.
In an embodiment, the W4 data are processed and analyzed using data models that treat data not as abstract signals stored in databases, but rather as IOs that represent RWEs that actually exist, have existed, or will exist in real space, real time, and are real people, objects, places, times, and/or events. As such, the data model for W4 IOs that represent W4 RWEs (Where/When/Who/What) will model not only the signals recorded from the RWEs or about the RWEs, but also represent these RWEs and their interactions in ways that model the affordances and constraints of entities and activities in the physical world. A notable aspect is the modeling of data about RWEs as embodied and situated in real world contexts so that the computation of similarity, clustering, distance, and inference take into account the states and actions of RWEs in the real world and the contexts and patterns of these states and actions.
For example, for temporal data the computation of temporal distance and similarity in a W4 data model cannot merely treat time as a linear function. The temporal distance and similarity between two times is dependent not only on the absolute linear temporal delta between them (e.g., the number of hours between “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 20, 7:00 pm Pacific Time”), but even more so is dependent on the context and activities that condition the significance of these times in the physical world and the other W4 RWEs (people, places, objects, and events) etc.) associated with them. For example, in terms of distance and similarity, “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 27, 4:00 pm Pacific Time” may be modeled as closer together in a W4 temporal data model than “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 20, 7:00 pm Pacific Time” because of the weekly meeting that happens every Tuesday at work at 4:00 pm vs. the dinner at home with family that happens at 7 pm on Tuesdays. Contextual and periodic patterns in time may be important to the modeling of temporal data in a W4 data model.
An even simpler temporal data modeling issue is to model the various periodic patterns of daily life such as day and night (and subperiods within them such as morning, noon, afternoon, evening, etc.) and the distinction between the workweek and the weekend. In addition, salient periods such as seasons of the year and salient events such as holidays also affect the modeling of temporal data to determine similarity and distance. Furthermore, the modeling of temporal data for IOs that represent RWEs should correlate temporal, spatial, and weather data to account for the physical condition of times at different points on the planet. Different latitudes have different amounts of daylight and even are opposite between the northern and southern hemispheres. Similar contextual and structural data modeling issues arise in modeling data from and about the RWEs for people, groups of people, objects, places, and events.
With appropriate data models for IOs that represent data from or about RWEs, a variety of machine learning techniques can be applied to analyze the W4 data. In an embodiment, W4 data may modeled as a “feature vector” in which the vector includes not only raw sensed data from or about W4 RWEs, but also higher order features that account for the contextual and periodic patterns of the states and action of W4 RWEs. Each of these features in the feature vector may have a numeric or symbolic value that can be compared for similarity to other numeric or symbolic values in a feature space. Each feature may also be modeled with an additional value from 0 to 1 (a certainty value) to represent the probability that the feature is true. By modeling W4 data about RWEs in ways that account for the affordances and constraints of their context and patterns in the physical world in features and higher order features with or without certainty values, this data (whether represented in feature vectors or by other data modeling techniques) can then be processed to determine similarity, difference, clustering, hierarchical and graph relationships, as well as inferential relationships among the features and feature vectors.
A wide variety of statistical and machine learning techniques can be applied to W4 data from simple histograms to Sparse Factor Analysis (SFA), Hidden Markov Models (HMMs), Support Vector Machines (SVMs), Bayesian Methods, etc. Such learning algorithms may be populated with data models that contain features and higher order features represent not just the “content” of the signals stored as IOs, e.g., the raw W4 data, but also model the contexts and patterns of the RWEs that exist, have existed, or will exist in the physical world from which these data have been captured.
As a pre-processor, the correlation engine 506 monitors the information provided by RWEs in order to determine if any conditions are identified that may trigger an action on the part of the W4 engine 502. For example, if a delivery condition has be associated with a message, when the correlation engine 506 determines that the condition is met, it can transmit the appropriate trigger information to the W4 engine 502 that triggers delivery of the message.
The attention engine 508 instruments all appropriate network nodes, clouds, users, applications or any combination thereof and includes close interaction with both the correlation engine 506 and the attribution engine 504.
The attention engine 608 includes a message intake and generation manager 610 as well as a message delivery manager 612 that work closely with both a message matching manager 614 and a real-time communications manager 616 to deliver and instrument all communications across the W4 COMN.
The attribution engine 604 works within the user profile manager 618 and in conjunction with all other modules to identify, process/verify and represent ownership and rights information related to RWEs, IOs and combinations thereof.
The correlation engine 606 dumps data from both of its channels (sensors and processes) into the same data backbone 620 which is organized and controlled by the W4 analytics manager 622 and includes both aggregated and individualized archived versions of data from all network operations including user logs 624, attention rank place logs 626, web indices and environmental logs 618, e-commerce and financial transaction information 630, search indexes and logs 632, sponsor content or conditionals, ad copy and any and all other data used in any W4 COMN process, IO or event. Because of the amount of data that the W4 COMN will potentially store, the data backbone 620 includes numerous database servers and datastores in communication with the W4 COMN to provide sufficient storage capacity.
As discussed above, the data collected by the W4 COMN includes spatial data, temporal data, RWE interaction data, IO content data (e.g., media data), and user data including explicitly-provided and deduced social and relationship data. Spatial data may be any data identifying a location associated with an RWE. For example, the spatial data may include any passively collected location data, such as cell tower data, global packet radio service (GPRS) data, global positioning service (GPS) data, WI-FI data, personal area network data, IP address data and data from other network access points, or actively collected location data, such as location data entered by the user.
Temporal data is time based data (e.g., time stamps) that relate to specific times and/or events associated with a user and/or the electronic device. For example, the temporal data may be passively collected time data (e.g., time data from a clock resident on the electronic device, or time data from a network clock), or the temporal data may be actively collected time data, such as time data entered by the user of the electronic device (e.g., a user maintained calendar).
The interaction data may be any data associated with user interaction of the electronic device, whether active or passive. Examples of interaction data include interpersonal communication data, media data, relationship data, transactional data and device interaction data, all of which are described in further detail below. Table 1, below, is a non-exhaustive list including examples of electronic data.
With respect to the interaction data, communications between any RWEs may generate communication data that is transferred via the W4 COMN. For example, the communication data may be any data associated with an incoming or outgoing short message service (SMS) message, email message, voice call (e.g., a cell phone call, a voice over IP call), or other type of interpersonal communication relative to an RWE, such as information regarding who is sending and receiving the communication(s). As described above, communication data may be correlated with, for example, temporal data to deduce information regarding frequency of communications, including concentrated communication patterns, which may indicate user activity information.
Logical and IO data refers to the data contained by an IO as well as data associated with the IO such as creation time, owner, associated RWEs, when the IO was last accessed, etc. If the IO is a media object, the term media data may be used. Media data may include any data relating to presentable media, such as audio data, visual data, and audiovisual data. For example, the audio data may be data relating to downloaded music, such as genre, artist, album and the like, and includes data regarding ringtones, ringbacks, media purchased, playlists, and media shared, to name a few. The visual data may be data relating to images and/or text received by the electronic device (e.g., via the Internet or other network). The visual data may be data relating to images and/or text sent from and/or captured at the electronic device. The audiovisual data may be data associated with any videos captured at, downloaded to, or otherwise associated with the electronic device. The media data includes media presented to the user via a network, such as use of the Internet, and includes data relating to text entered and/or received by the user using the network (e.g., search terms), and interaction with the network media, such as click data (e.g., advertisement banner clicks, bookmarks, click patterns and the like). Thus, the media data may include data relating to the user's RSS feeds, subscriptions, group memberships, game services, alerts, and the like. The media data also includes non-network activity, such as image capture and/or video capture using an electronic device, such as a mobile phone. The image data may include metadata added by the user, or other data associated with the image, such as, with respect to photos, location when the photos were taken, direction of the shot, content of the shot, and time of day, to name a few. As described in further detail below, media data may be used, for example, to deduce activities information or preferences information, such as cultural and/or buying preferences information.
The relationship data may include data relating to the relationships of an RWE or IO to another RWE or IO. For example, the relationship data may include user identity data, such as gender, age, race, name, social security number, photographs and other information associated with the user's identity. User identity information may also include e-mail addresses, login names and passwords. Relationship data may further include data identifying explicitly associated RWEs. For example, relationship data for a cell phone may indicate the user that owns the cell phone and the company that provides the service to the phone. As another example, relationship data for a smart car may identify the owner, a credit card associated with the owner for payment of electronic tolls, those users permitted to drive the car and the service station for the car.
Relationship data may also include social network data. Social network data includes data relating to any relationship that is explicitly defined by a user or other RWE, such as data relating to a user's friends, family, co-workers, business relations, and the like. Social network data may include, for example, data corresponding with a user-maintained electronic address book. Relationship data may be correlated with, for example, location data to deduce social network information, such as primary relationships (e.g., user-spouse, user-children and user-parent relationships) or other relationships (e.g., user-friends, user-co-worker, user-business associate relationships). Relationship data also may be utilized to deduce, for example, activities information.
The interaction data may also include transactional data. The transactional data may be any data associated with commercial transactions undertaken by or at the mobile electronic device, such as vendor information, financial institution information (e.g., bank information), financial account information (e.g., credit card information), merchandise information and costs/prices information, and purchase frequency information, to name a few. The transactional data may be utilized, for example, to deduce activities and preferences information. The transactional information may also be used to deduce types of devices and/or services the user owns and/or in which the user may have an interest.
The interaction data may also include device or other RWE interaction data. Such data includes both data generated by interactions between a user and a RWE on the W4 COMN and interactions between the RWE and the W4 COMN. RWE interaction data may be any data relating to an RWE's interaction with the electronic device not included in any of the above categories, such as habitual patterns associated with use of an electronic device data of other modules/applications, such as data regarding which applications are used on an electronic device and how often and when those applications are used. As described in further detail below, device interaction data may be correlated with other data to deduce information regarding user activities and patterns associated therewith. Table 2, below, is a non-exhaustive list including examples of interaction data.
Conditional Delivery of Messages on the W4 COMN
One notable aspect of the W4 COMN is the ability to use W4 data to allow users to tailor when and how messages are delivered to other users or their proxies. The information obtained about a W4 entity from any source or communication channel may be used as a basis for delivery conditions for any message delivered via the W4 COMN on any communication channel interoperating with the W4 COMN.
The delivery of messages is a network personal information management (PIM) operation that allows both explicit and implicit automation of W4 COMN circuits, processes and events by logic-based conditions including means for senders, receivers and delivery conditions to be expressed, weighted and prioritized in W4 analytical processes for testing delivery conditions or network environmental conditions. W4 message delivery includes user-, process- or system-generated messages targeted to an intersection of any topical, spatial, temporal, and/or social variables.
To continue the “Who, What, When, Where” conceptualization discussed above, W4 message delivery allows messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. Table 3, below, provides a matrix of some examples of different “Who, What, When, Where” combinations that could be used in W4 message delivery. The listings in Table 3 are not complete nor exhaustive, but are provided to give an idea of the plethora of different message delivery options provided by the W4 COMN.
The list provided in Table 3 is a very limited list of the possibilities for message delivery via the W4 COMN. It should be noted that the delivery conditions could be a simple condition, such as a time or detection that a designated RWE is at a location, or a more complex condition based on the occurrence of multiple conditions, either at the same time or in a specific order, such as deliver only on the day of a baseball game to a RWE near a specified location. In the baseball game example, the baseball game may be considered to be an event, with its own unique W4 identifier, that is associated with a location and a time period. For events such as sporting events, meetings, holidays, etc., one or more IOs may exist on the W4 COMN or an electronic calendar that are a proxy for the event from which the time, location, and other relevant data of the event may be obtained.
In a broad sense, W4 message delivery allows a message (which may be any IO including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) to be delivered in accordance with delivery conditions based on any combination of the available W4 data types, including topical, spatial, temporal, and/or social data. Furthermore, because the W4 COMN coordinates delivery of messages via multiple communication channels and through multiple devices and other RWEs, it allows the communication channel for delivery of a message to be dynamically determined upon detection that the delivery conditions are met. Examples include a social alarm clock, place-based messages, social proximity-based messages, and time-shifted message delivery, to name but a few applications of the W4 message delivery functionality.
Predetermined sets of W4 delivery conditions can be packaged and provided to users in common bundles, e.g., a Parent's Package, a Boss' Package, a Vehicle Maintenance Package, etc. These bundles may include predetermined message content, delivery conditions and delivery condition templates that allow the users to quickly construct delivery conditions for messages that will be easily and clearly interpreted by the W4 COMN's message delivery subsystems.
As discussed above, it should be understood that multiple RWEs and IOs may be associated with a single message as a sender. For example, a user may create and send an email message with delivery conditions using a laptop computer. The user is an RWE having a unique W4 identifier. In addition, the laptop is an RWE with its own unique W4 identifier. The email application on the laptop may be tracked as an IO with its own W4 identifier. In an embodiment, some or all of the user, laptop computer and email application may be considered a sender of the IO that is the email message. In this case, the user may be considered the originating sender and the laptop and email application proxies for the originating sender. The concept of proxies was discussed above and is particularly important here where it is anticipated that human actors, either as senders, recipients or entities tied to a delivery condition, will be known to the W4 COMN primarily through information obtained from their proxies (e.g., proxy RWEs, such as their smart phones, computing devices, sensors, smart vehicles, home phones, etc., and proxy IOs such as email accounts, communication software, credit card accounts, data objects containing data generated by a RWE, data objects containing data about an RWE or event, etc.).
In an embodiment, this determination of the senders of a message, including determining who the user, if any, is that should be considered the original sender may be performed by the attribution engine 504 as described above. It should also be noted that some messages may be sent by a process programmatically, e.g., automatically during the course of the execution of a program, so that there is not a human sender to be identified but rather only a sender IO. Alternatively, the attribution engine 504 may only identify the sending RWE that actually places the message into the W4 COMN and any other associated RWEs (e.g., proxies and/or originating sender) may be identified by the message intake manager 702 in conjunction with the correlation engine 506.
The message intake manager 702, upon receipt of a message with delivery conditions, identifies the recipients and the delivery conditions of the message as described below. This may include requesting that the correlation engine 506 correlate the channel-specific identifiers of the recipients with other W4 data in order to identify a target human recipient, if any, and any proxies for that recipient. In addition, the same information may need to be determined for RWEs identified in the delivery conditions.
It should be understood that any human or non-networked entity that is a sender, recipient or subject of a delivery condition of a message may be identified only by proxy RWEs or IOs. For example, an email may be sent by or directed to “john.smith@yahoo.com” or a telephone call may be directed to “(720)555-0505.” In both cases, the identifiers used to identify the human (i.e., “john.smith@yahoo.com” and “(720)555-0505”) are identifiers of proxies of the actual intended human recipient. Based on the W4 data known to the W4 COMN, these identifiers of proxies may be analyzed, e.g., by the correlation engine 506, in order to determine the unique W4 identifier of the RWE that is accessed by, represented by or working through the proxy RWE or proxy IO. For example, “john.smith@yahoo.com” and “(720)555-0505” may be communication channel-specific identifiers of RWEs or IOs that the W4 COMN is aware are proxies for a known human RWE (e.g., a user with the name John Smith) with a distinct unique W4 identifier.
The W4 engine 700 further includes the message delivery manager 704 that controls the delivery of messages. In an embodiment, the message delivery manager 704 logs the delivery conditions for a message and monitors the W4 data for occurrence of the delivery conditions. When/if the delivery conditions are met, the message delivery manager 704 then delivers the message to the recipient. This may include selecting a delivery route or communication channel and selecting the appropriate proxy RWE (if applicable) for delivery of the message, possibly including reformatting the message for the selected RWE. For example, for an email message that is to be delivered to a recipient when that recipient is at a specified location, the email message may be reformatted as a text for display via a cellular phone or vehicle-mounted display device that is one of the recipient's proxy devices and delivered to that device when it is determined that the device is at the specified location. Similarly, voicemails that are to be delivered to a recipient when a certain team wins a game may be reformatted and transmitted to whatever proxy device the recipient may be using at the time that the team wins the game. Thus, the voicemail may be delivered to a cell phone number, a voicemail inbox, an email inbox as an attachment to an email, or at a work telephone number depending on what the recipient is doing at the time the team wins the game.
In order to determine when delivery conditions are met, the message delivery manager 704 may utilize the correlation engine 506 to monitor the W4 data. Furthermore, a determination that a delivery condition has been met may be possible only through the graphing and identification of relationships based on the correlations between IOs and RWEs known to the W4 COMN. The relationships may be determined in response to a request to deliver a message, triggered by some other input, or may be automatically determined by the correlation engine on a periodic basis and stored for later use.
As described above, a foundational aspect of the W4 COMN that allows for conditional message delivery is the ongoing collection and maintenance of W4 data from the RWEs interacting with the network. In an embodiment, this collection and maintenance is an independent operation 899 of the W4 COMN and thus current W4 social, temporal, spatial and topical data are always available for use in testing of delivery conditions. In addition, part of this data collection operation 899 includes the determination of ownership and the association of different RWEs with different IOs as described above including prioritization among specific groups or sub-groups of RWEs and IOs. Therefore, each IO is owned/controlled by at least one RWE with a known, unique identifier on the W4 COMN and each IO may have many associations with other RWEs that are known to the W4 COMN.
In the embodiment shown, the method 800 is initiated when a message with a delivery condition is detected by the W4 COMN in a receive delivery request operation 802. Such a request may be generated by software on a computing device operated by a user, by an automated process or by a “dumb” device such as a cellular phone or a sensor. As discussed above, one or more RWEs may be identified as a sender of the message. Such identifications may be made from the data of the message, the source of the request or a combination of both. In addition, deductions may be made concerning a user that is the sender of the message based on W4 data for the known device or software senders of the message, as previously described.
The delivery request will further identify one or more recipients of the message. As discussed above, each recipient may be identified by a channel-specific identifier of a proxy RWE of the recipient. Thus, similar to the situation with senders, there may be multiple recipients associated with a message. For example, a recipient of an email may be identified as “bill.smith@yahoo.com”, which is an email address for a electronic mail account. Using the W4 data, it may be determined that the user associated that email address has multiple proxies on the W4 COMN including for example the email account identified by “bill.smith@yahoo.com”, a mobile telephone identified with a telephone number, a home telephone identified by a different telephone number, a toll payment transponder identified by a transponder identification number, a car identified by a license plate, an internet protocol (IP) address, a business telephone identified by a third telephone number, and a home address identified by one or more physical location coordinates or addresses. In an embodiment, requests to deliver a message to a recipient that is determined to be a proxy for a user (or other RWE such as a business or location) may be interpreted as requests to deliver the message to the user (or other RWE) that is accessible via the proxy as discussed below.
In an embodiment, the delivery condition may be part of the delivery request or may be included in an IO (e.g., as data or metadata) that constitutes the message. In such a situation, the address string or information is associated with the IO that is to be delivered. Such address string or address information may also be a part of the IO to be delivered. A request may also occur upon detection of an address string, such as for example, a user entering an address string into a field in an email composition screen or speaking an address string into a microphone on a device.
In an embodiment, the delivery conditions may be designated by the sender of the message, which may be an RWE, typically a user, or an IO, such as a process executing on a computing device. Any suitable way of selecting and associating the delivery conditions with the message may be used as long as the W4 COMN can identify the resulting data that embodies the delivery conditions. For example, for an email message the delivery conditions may be entered by the user into a delivery options interface provided by the email application, such delivery conditions then being stored as metadata of the message: this metadata then is decoded by the W4 COMN to identify the delivery conditions. For a telephone call, a message delivery system could use a voice or keypad data entry system to allow the caller to assign or select delivery conditions to the voice message from a audible menu. Delivery conditions may also be automatically generated and added to messages, for example by an application such as a parental control application that sends messages upon the detection certain activities or content. Other methods of associating delivery conditions with a message are possible and any suitable method may be used with the embodiments of the systems and methods described herein.
In the method 800, the RWEs and IOs may be identified in the delivery conditions by any identifier, unique or non-unique, communication channel-specific or global, as long as the identifier can be resolved by the W4 COMN to its intended target RWE or IO. Resolving channel-specific identifiers can be done by correlating the channel-specific identifier with other W4 data. Non-unique identifiers (e.g., identifiers such as “Mom”, “father”, “Debby”, “Bob”, “Starbucks”) may have to be disambiguated based on the W4 data known about the sender and the message to be delivered and any suitable disambiguation method may be utilized for this purpose.
In the embodiment, the message with delivery conditions may be considered to be detected when it is received from the sender(s), although the reader will understand that the message may not have actually been sent at the time of receipt of the delivery conditions. It is anticipated that under most circumstances that any attributable sender will already be known to the W4 COMN and provided with a unique W4 identifier as well as at least one communication channel-specific address (which is another form of unique identifier).
As mentioned above, the receive delivery request operation 802 may include receiving an actual IO (e.g., message file or snippet of text) from an RWE or an IO such as a email software being executed by an RWE, the IO to be transmitted as directed by the address string or pointer to an IO at another address or location. The IO may contain data such as the text or contents of the communication as well as additional information in the form of metadata. The data contained may be evaluated in order to identify the delivery conditions, additional RWEs associated with the message (e.g., people listed in text of a message but that are neither the sender nor a recipient), other IOs (e.g., hyperlinks to IOs, attachments, etc.) contained in the message, and any topics discussed in the message.
The receive delivery request operation 802 may be considered to occur at any point in the delivery chain within the W4 COMN, e.g., by any one of the engines used to conduct IO intake, routing or delivery. For example, depending on how the implementers of the W4 COMN choose to implement the network functions, a message may be received and initially analyzed and information routed to the correlation engine and addressing engine by any one of the message intake and generation manager, user profile manager, message delivery manager or any other engine or manager in the W4 COMN's communication delivery chain.
After detection of delivery conditions associated with a message, the delivery conditions are analyzed in a delivery condition identification operation 804. The delivery condition identification operation 804 includes identifying each RWE in the delivery condition and what the actual delivery conditions are with respect to the RWEs. This may require parsing a string containing the delivery conditions or some other analysis of the data or metadata for the message. For example, an IO may be emailed by a sender addressed to a recipient (identified by an email address) with the delivery condition that the message should only be delivered when the recipient is at/with a specified RWE (e.g., another person, a location such as a park, or a business such as a grocery store, Laundromat, a coffee shop, etc.). In this example, the delivery condition identification operation 804 will identify the recipient, the specified RWE and a maximum distance or range of distances between the two that indicates the delivery condition is met (which may or may not be explicitly provided in the delivery condition).
As discussed above, the recipients and any RWEs in the delivery conditions may be proxies for users or other RWEs. The identification operation 804 includes determining whether the recipient and delivery condition RWEs are proxies or the actual target of the message. In addition if a specified RWE is a proxy, the identification operation 804 further includes identifying any RWEs that may be used as proxies for the specified RWE and for any RWEs for which the specified RWE is, itself, a proxy.
For example, given the relationships described in
The identification operation 804 may distinguish between proxies based on the data available for the proxy and the delivery condition when identifying the proxies to be used to determine if a delivery condition is met. For example, a work email account for a user may be a proxy for the user, but if no current location data may be derived from user's use of the email account (e.g., the email account may be accessed from multiple devices and/or from multiple or any location), the work email account may not be identified as a proxy for the location of the user. If, however, the device the user uses to access the work email account at any given time can be identified and its location can be determined (e.g., a public computer on a network that the user uses to access his email account), then the device could be used as a proxy of the location of the user by virtue of its current relationship with the user's email account, even though the device may never have been used by the user before.
In an embodiment, the W4 engine may assume that for any identified RWE that is explicitly designated as a proxy for another RWE, that the other RWE is the intended entity and substitute it for identified RWE. For example, if an IO is emailed by a sender addressed to a recipient (identified by an email address) with the delivery condition that the message should only be delivered when the recipient is at/with a specified RWE, the IO may be delivered when it is determined that the recipient's cell phone (e.g., a proxy for the recipient, but not necessarily a proxy for the recipient's email account) is close enough to the specified RWE's cell phone (e.g., a proxy for the recipient) and the IO may also be delivered when it is determined that both the specified RWE and the recipient RWE are attending the same meeting/event based on message traffic, financial data (e.g., confirmation of event ticket purchase or detection of concurrent sales made at same location) or smart calendar entries.
The identification of proxies, as discussed above, may be explicit (e.g., designated as proxies by their associated user or RWE) or implicitly determined based on an analysis of W4 data. As discussed above, such W4 data may have been collected from messages, communications and IOs previously obtained or handled by the W4 COMN via many different communication channels and systems, including email and text-based communication channels as well as any communication channels that include audio data including channels that support telephone, voice over internet protocol (VOIP), and video communications such as video chat.
To determine implicit proxies, the W4 data may be graphed in order to determine what RWEs are related and how and from this information make probabilistic assumptions about the nature of the relationships between RWEs. In an embodiment, correlations are made for and between each of the RWEs known to the W4 COMN based on the social data, spatial data, temporal data and logical data associated with each RWE. In one sense, the graphing operation may be considered a form of comparing the retrieved social data, spatial data, temporal data and logical data for all RWEs to identify relationships between RWEs and other contextual similarities.
It should be noted that the determination of implicit proxies may be performed each time a delivery condition is tested. This allows for the dynamic determination of the appropriate proxy for any RWE at any time. For example, during the week a corporate car or corporate cell phone may be considered a good proxy for the location of a recipient; but during the weekend a personal cell phone or personal car may be considered a better proxy for the recipient than the work cell phone.
After the RWEs, including any proxy RWEs, that may be used to confirm the occurrence of the delivery conditions are identified, the delivery condition identification operation 804 then identifies one or more data sources for the data necessary to test the delivery conditions. For example, if the delivery condition has a location requirement, data sources for the current location of the recipient and the specified RWEs will be identified. If the delivery condition has a temporal requirement, a system clock or the local time for the recipient and the specified RWEs may be identified. If the delivery condition has a status or state-related condition (e.g., a condition based on some identified current sensor readings or other condition such as current traffic conditions, current weather conditions, current speed, what an RWE is currently doing, current weather forecast, occurrence of a defined event, etc.), the appropriate data source or sources are identified that contain the current information necessary to test these conditions so that the message can be delivered upon determination that the current state of the RWE matches the specified state identified by the delivery condition.
For example, if the recipient is a user with a cell phone, the cell phone may be identified as the proxy for the current location of the recipient. The identification operation 804 will then identify the location of the data source from which the current location of the cell phone may be obtained. Such a data source, for example, may be maintained by the cellular service provider and accessible through their network. Alternatively, the cell phone may be provided with a GPS locator and the current location may be accessible from the cell phone itself If the specified RWE is a business, a proxy RWE may not be needed but a proxy IO that contains information about the business including its current location(s) may be identified.
The delivery condition identification operation 804, as mentioned above, further includes identifying what constitutes the delivery condition(s) being met. For example, if the delivery condition requires an RWE be at a location, a range of distances between the RWE and identified location that, as far as the W4 COMN is concerned, will be treated as being “at” the location. Such a range may be predetermined by the operators of the W4 COMN (e.g., “at” is defined as within 10 meters), provided or otherwise selected by the sender as part of the delivery conditions or dynamically determined based on the data available and the precision of the location data or the application package or requirements.
If the delivery condition has a temporal requirement, a location may need to be identified if the sender designated only a relative time (e.g., Thursday at 6:00 am or Christmas) as opposed to an absolute time (Thursday, Nov. 8, 2007, 6:00 am Mountain Standard Time). The W4 COMN may, unless otherwise specified, assume all times are local to the sender or the recipient and, when future times are designated imprecisely, the sender may be prompted for more information or the next possible match may be used.
If the delivery condition has a status or state-related condition, the appropriate condition is quantified so that it can be tested using the data from identified data source or sources. Again, the exact condition may have been specified by the sender (e.g., “deliver if winds greater than 40 mph detected at home”) or the condition may need to be determined from a less specific designations (e.g., “deliver if high winds detected at home”). For example, a delivery condition based on “heavy traffic” at a location will identify what constitutes heavy traffic in terms of the metrics monitored by and accessible from the identified traffic sensor. Thus, a threshold or range of values that will be considered to be “heavy traffic” when detected may be identified or a definition retrieved. As another example, the delivery condition “when the recipient is playing computer games” may be determined by identifying what software constitutes computer games and determining if the software is active at any particular moment. Furthermore, a delivery condition's quantification may not be the same for every instance of the delivery condition, but rather may be independently determined based on the current context and W4 data.
If the identification operation 804 can not adequately identify any of the parameters, RWEs and IOs described above or identify the delivery conditions to the extent sufficient to determine when a delivery condition has been met, the sender may be notified and asked for additional clarifying information. For example, in such a situation the W4 COMN may respond by prompting the sender of the message with a question such as, “By ‘deliver to debby at the grocery store’, do you mean your wife Deborah or your sister Deborah?” The prompt may include information derived from previous communications or other W4 data to assist the sender in confirming the proper identification of the recipient and delivery condition.
In addition, in an embodiment the delivery conditions may be very specifically designated by the sender when the message was created. For example, a sender may direct the W4 COMN to deliver a message only to the recipient via the recipient's cell phone, when the recipient's car and cell phone both are detected near a specified location at the same time so that when the sender can be certain that the message will not be delivered when, for instance, the recipient cycles by the gas station with the recipient's cell phone or when the sender drives by the location in the recipient's vehicle. This allows the sender to be very specific in defining delivery conditions, if that is what is desired or necessary for proper delivery of the message the way the sender intended.
After the delivery condition(s) and recipient(s) have been identified as described above, the delivery conditions are tested in a testing operation 806. This will include inspecting the necessary data sources and comparison of the various data elements (e.g., current location, temperature, state, etc.) as needed to determine if the delivery condition is met. In an embodiment, testing may include retrieving or requesting data from the identified data sources. The testing operation 806 may require only simple comparisons, e.g., comparing two values, such as current locations, to determine if the values are within a specified range. The testing operation 806 may be more complicated such as requiring complex calculations or simultaneous testing of multiple conditions related to a plethora of different RWEs and may include gathering and analyzing data from external sources.
Based on the results of the testing operation 806, a determination operation 808 determines if the delivery condition(s) are met or not. If the conditions are not met, then the delivery conditions are retested by repeating the testing operation 806. By such repeated testing the W4 data is monitored for occurrence of the delivery conditions. The retesting may be done periodically on a fixed schedule or dynamically in response to external conditions such as the receipt of new data on the W4 backbone related to RWEs implicated by the delivery conditions. The retesting may be done in perpetuity until the conditions are met or for a predetermined maximum time period specified by the sender or the W4 COMN. If a message cannot be delivered before the maximum time period is reached, the sender may be notified that the message was not delivered due to the delivery conditions not being met, e.g., “Message not delivered because Debby did not visit the grocery store within the specified period.”
If the determination operation 808 determines that the delivery conditions are met, the message is then transmitted to the recipient(s) in a delivery operation 810. In an embodiment, if the identified recipient is a proxy RWE, the message may be delivered to the proxy RWE regardless of the conditions identified that met the delivery condition. For example, an email may be sent to an identified email address when the cell phone of the user associated with that email address is detected at the delivery condition location. However, in this situation it is possible that the user may not receive the email until later if the cell phone is not email-enabled.
Alternatively, the method may select a communication channel and RWE to deliver the message to based on the conditions that triggered the delivery. For example, if a message is an email and the delivery condition is the recipient being at a location, upon detection that the recipient's cell phone is at the location the message may be reformatted for the recipient's cell phone, such as into an IM or SMS message, and transmitted to the cell phone via the cellular communication network servicing the cell phone. In such an embodiment, not only the identified proxy RWE of a recipient but all other proxies for the recipient (or other RWE) are considered possible delivery routes to the recipient. This allows the W4 message delivery system to select the most appropriate delivery route/communication channel/proxy combination when finally delivering the message, regardless of what delivery route/communication channel/proxy was initially identified as the recipient or used by the sender to create and send the message to the W4 COMN.
Such a dynamic message delivery system allows the delivery of message by alternative but effective means. For example, based on W4 data the cell phone of a user's best friend may be selected as a proxy for the user (as well as for the best friend) and a message to the user may be automatically delivered to the best friend's cell phone when delivery conditions are met and it is determined that the user and the best friend's cell phone are co-located. Under these circumstances the message delivery method can quickly and effectively deliver messages by communication channels and to devices that are completely unknown to the sender. As another example, when traveling, email messages for a user may be automatically delivered to coworkers with the user if the user does not have email access.
In addition, in an embodiment the delivery channel and RWE may be specifically designated by the sender when the message was created. For example, a sender may direct the W4 COMN to deliver a message only to the recipient via the car, when the car is detected near a gas station so that when the recipient cycles by the gas station with the recipient's cell phone delivery of the message is not triggered. Alternatively, the sender may identify that the W4 COMN dynamically select the communication route and recipient proxy device so that the message is delivered to the recipient in the manner that is most likely to get the message to the intended recipient user (as opposed to proxy for the user) upon occurrence of the delivery conditions.
Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, may be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible. Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that may be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter.
Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently.
While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications may be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure. For example, in an embodiment the methods and systems could be used to initiate two-way communications via audio or video connections between RWEs upon the occurrence of a delivery condition, e.g., a call between two cell phones could be made automatically upon the detection of a delivery condition. As another example, the delivery method could be used to create a virtual soundtracks for workouts based on delivering and playing different songs based on the current location, current speed, etc. of the exercising user. As another example, the delivery method could be used to create immersive reality games that provide feedback to players based on detection of different conditions caused by the players actions. In such an embodiment, devices may be created specifically to act as sensors and data inputs for use in game play, allowing for delivery conditions to be tailored to any action, e.g., striking a target with an infrared gun within a certain period of time. Numerous other changes may be made that will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the invention disclosed and as defined in the appended claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5446891 | Kaplan et al. | Aug 1995 | A |
| 5493692 | Theimer et al. | Feb 1996 | A |
| 5583763 | Atcheson et al. | Dec 1996 | A |
| 5651068 | Klemba et al. | Jul 1997 | A |
| 5761662 | Dasan | Jun 1998 | A |
| 5764906 | Edelstein et al. | Jun 1998 | A |
| 5781879 | Arnold et al. | Jul 1998 | A |
| 5784365 | Ikeda | Jul 1998 | A |
| 5794210 | Goldhaber et al. | Aug 1998 | A |
| 5802510 | Jones | Sep 1998 | A |
| 5835087 | Herz | Nov 1998 | A |
| 5903848 | Takahashi | May 1999 | A |
| 5920854 | Kirsch et al. | Jul 1999 | A |
| 6014638 | Burge et al. | Jan 2000 | A |
| 6021403 | Horvitz et al. | Feb 2000 | A |
| 6047234 | Cherveny et al. | Apr 2000 | A |
| 6098065 | Skillen et al. | Aug 2000 | A |
| 6112181 | Shear et al. | Aug 2000 | A |
| 6157924 | Austin | Dec 2000 | A |
| 6169992 | Beall et al. | Jan 2001 | B1 |
| 6212552 | Biliris et al. | Apr 2001 | B1 |
| 6266667 | Olsson | Jul 2001 | B1 |
| 6314365 | Smith | Nov 2001 | B1 |
| 6314399 | Deligne et al. | Nov 2001 | B1 |
| 6324519 | Eldering | Nov 2001 | B1 |
| 6327590 | Chidlovskii et al. | Dec 2001 | B1 |
| 6446065 | Nishioka et al. | Sep 2002 | B1 |
| 6490698 | Horvitz et al. | Dec 2002 | B1 |
| 6502033 | Phuyal | Dec 2002 | B1 |
| 6523172 | Martinez-Guerra et al. | Feb 2003 | B1 |
| 6571279 | Herz et al. | May 2003 | B1 |
| 6601012 | Horvitz et al. | Jul 2003 | B1 |
| 6662195 | Langseth et al. | Dec 2003 | B1 |
| 6665640 | Bennett et al. | Dec 2003 | B1 |
| 6694316 | Langseth et al. | Feb 2004 | B1 |
| 6701311 | Biebesheimer et al. | Mar 2004 | B2 |
| 6701315 | Austin | Mar 2004 | B1 |
| 6708203 | Makar et al. | Mar 2004 | B1 |
| 6731940 | Nagendran | May 2004 | B1 |
| 6741980 | Langseth et al. | May 2004 | B1 |
| 6757661 | Blaser et al. | Jun 2004 | B1 |
| 6773344 | Gabai et al. | Aug 2004 | B1 |
| 6781920 | Bates et al. | Aug 2004 | B2 |
| 6785670 | Chiang et al. | Aug 2004 | B1 |
| 6789073 | Lunenfeld | Sep 2004 | B1 |
| 6813501 | Kinnunen et al. | Nov 2004 | B2 |
| 6816850 | Culliss | Nov 2004 | B2 |
| 6829333 | Frazier | Dec 2004 | B1 |
| 6834195 | Brandenberg et al. | Dec 2004 | B2 |
| 6842761 | Diamond et al. | Jan 2005 | B2 |
| 6845370 | Burkey et al. | Jan 2005 | B2 |
| 6850252 | Hoffberg | Feb 2005 | B1 |
| 6853913 | Cherveny et al. | Feb 2005 | B2 |
| 6853982 | Smith et al. | Feb 2005 | B2 |
| 6882977 | Miller | Apr 2005 | B1 |
| 6904160 | Burgess | Jun 2005 | B2 |
| 6931254 | Egner et al. | Aug 2005 | B1 |
| 6961660 | Underbrink et al. | Nov 2005 | B2 |
| 6961731 | Holbrook | Nov 2005 | B2 |
| 6985839 | Motamedi et al. | Jan 2006 | B1 |
| 7010492 | Bassett et al. | Mar 2006 | B1 |
| 7027801 | Hall et al. | Apr 2006 | B1 |
| 7058508 | Combs et al. | Jun 2006 | B2 |
| 7058626 | Pan et al. | Jun 2006 | B1 |
| 7062510 | Eldering | Jun 2006 | B1 |
| 7065345 | Carlton et al. | Jun 2006 | B2 |
| 7065483 | Decary et al. | Jun 2006 | B2 |
| 7069308 | Abrams | Jun 2006 | B2 |
| 7073129 | Robarts et al. | Jul 2006 | B1 |
| 7110776 | Sambin | Sep 2006 | B2 |
| 7143091 | Charnock et al. | Nov 2006 | B2 |
| 7149696 | Shimizu et al. | Dec 2006 | B2 |
| 7181438 | Szabo | Feb 2007 | B1 |
| 7185286 | Zondervan | Feb 2007 | B2 |
| 7194512 | Creemer et al. | Mar 2007 | B1 |
| 7203597 | Sato et al. | Apr 2007 | B2 |
| 7209915 | Taboada et al. | Apr 2007 | B1 |
| 7219013 | Young et al. | May 2007 | B1 |
| 7236969 | Skillen et al. | Jun 2007 | B1 |
| 7254581 | Johnson et al. | Aug 2007 | B2 |
| 7257570 | Riise et al. | Aug 2007 | B2 |
| 7305445 | Singh et al. | Dec 2007 | B2 |
| 7320025 | Steinberg et al. | Jan 2008 | B1 |
| 7343364 | Bram et al. | Mar 2008 | B2 |
| 7395507 | Robarts et al. | Jul 2008 | B2 |
| 7404084 | Fransdonk | Jul 2008 | B2 |
| 7437312 | Bhatia et al. | Oct 2008 | B2 |
| 7451102 | Nowak | Nov 2008 | B2 |
| 7461168 | Wan | Dec 2008 | B1 |
| 7496548 | Ershov | Feb 2009 | B1 |
| 7522995 | Nortrup | Apr 2009 | B2 |
| 7529811 | Thompson | May 2009 | B2 |
| 7562122 | Oliver et al. | Jul 2009 | B2 |
| 7577665 | Rameer et al. | Aug 2009 | B2 |
| 7584215 | Saari et al. | Sep 2009 | B2 |
| 7624104 | Berkhin et al. | Nov 2009 | B2 |
| 7624146 | Brogne et al. | Nov 2009 | B1 |
| 7634465 | Sareen et al. | Dec 2009 | B2 |
| 7657907 | Fennan et al. | Feb 2010 | B2 |
| 7681147 | Richardson-Bunbury et al. | Mar 2010 | B2 |
| 7725492 | Sittig et al. | May 2010 | B2 |
| 7729901 | Richardson-Bunbury et al. | Jun 2010 | B2 |
| 7769740 | Martinez | Aug 2010 | B2 |
| 7769745 | Mor Naaman | Aug 2010 | B2 |
| 7783622 | Vandermolen et al. | Aug 2010 | B1 |
| 7792040 | Nair | Sep 2010 | B2 |
| 7802724 | Nohr | Sep 2010 | B1 |
| 7822871 | Stolorz et al. | Oct 2010 | B2 |
| 7831586 | Reitter et al. | Nov 2010 | B2 |
| 7865308 | Athsani | Jan 2011 | B2 |
| 7925708 | Davis | Apr 2011 | B2 |
| 20010013009 | Greening et al. | Aug 2001 | A1 |
| 20010035880 | Musatov et al. | Nov 2001 | A1 |
| 20010047384 | Croy | Nov 2001 | A1 |
| 20010052058 | Ohran | Dec 2001 | A1 |
| 20020014742 | Conte et al. | Feb 2002 | A1 |
| 20020019849 | Tuvey et al. | Feb 2002 | A1 |
| 20020019857 | Harjanto | Feb 2002 | A1 |
| 20020023091 | Silberberg et al. | Feb 2002 | A1 |
| 20020023230 | Bolnick et al. | Feb 2002 | A1 |
| 20020035605 | McDowell et al. | Mar 2002 | A1 |
| 20020049968 | Wilson et al. | Apr 2002 | A1 |
| 20020052786 | Kim et al. | May 2002 | A1 |
| 20020052875 | Smith et al. | May 2002 | A1 |
| 20020054089 | Nicholas | May 2002 | A1 |
| 20020065844 | Robinson et al. | May 2002 | A1 |
| 20020069218 | Sull et al. | Jun 2002 | A1 |
| 20020099695 | Abajian et al. | Jul 2002 | A1 |
| 20020103870 | Shouji | Aug 2002 | A1 |
| 20020111956 | Yeo et al. | Aug 2002 | A1 |
| 20020112035 | Carey | Aug 2002 | A1 |
| 20020133400 | Terry et al. | Sep 2002 | A1 |
| 20020138331 | Hosea et al. | Sep 2002 | A1 |
| 20020152267 | Lennon | Oct 2002 | A1 |
| 20020169840 | Sheldon et al. | Nov 2002 | A1 |
| 20020173971 | Stirpe et al. | Nov 2002 | A1 |
| 20020178161 | Brezin et al. | Nov 2002 | A1 |
| 20020198786 | Tripp et al. | Dec 2002 | A1 |
| 20030008661 | Joyce et al. | Jan 2003 | A1 |
| 20030009367 | Morrison | Jan 2003 | A1 |
| 20030009495 | Adjaoute | Jan 2003 | A1 |
| 20030027558 | Eisinger | Feb 2003 | A1 |
| 20030032409 | Hutcheson et al. | Feb 2003 | A1 |
| 20030033331 | Sena et al. | Feb 2003 | A1 |
| 20030033394 | Stine et al. | Feb 2003 | A1 |
| 20030065762 | Stolorz et al. | Apr 2003 | A1 |
| 20030069877 | Grefenstette et al. | Apr 2003 | A1 |
| 20030069880 | Harrison et al. | Apr 2003 | A1 |
| 20030078978 | Lardin et al. | Apr 2003 | A1 |
| 20030080992 | Haines | May 2003 | A1 |
| 20030126250 | Jhanji | Jul 2003 | A1 |
| 20030149574 | Rudman | Aug 2003 | A1 |
| 20030154293 | Zmolek | Aug 2003 | A1 |
| 20030165241 | Fransdonk | Sep 2003 | A1 |
| 20030191816 | Landress et al. | Oct 2003 | A1 |
| 20040010492 | Zhao et al. | Jan 2004 | A1 |
| 20040015588 | Cotte | Jan 2004 | A1 |
| 20040030798 | Andersson et al. | Feb 2004 | A1 |
| 20040034752 | Ohran | Feb 2004 | A1 |
| 20040043758 | Sorvari et al. | Mar 2004 | A1 |
| 20040044736 | Austin-Lane et al. | Mar 2004 | A1 |
| 20040070602 | Kobuya et al. | Apr 2004 | A1 |
| 20040139025 | Coleman | Jul 2004 | A1 |
| 20040139047 | Rechsteiner | Jul 2004 | A1 |
| 20040148341 | Cotte | Jul 2004 | A1 |
| 20040152477 | Wu et al. | Aug 2004 | A1 |
| 20040183829 | Kontny et al. | Sep 2004 | A1 |
| 20040201683 | Murashita et al. | Oct 2004 | A1 |
| 20040203851 | Vetro et al. | Oct 2004 | A1 |
| 20040203909 | Koster | Oct 2004 | A1 |
| 20040209602 | Joyce et al. | Oct 2004 | A1 |
| 20040243623 | Ozer et al. | Dec 2004 | A1 |
| 20040260804 | Grabarnik et al. | Dec 2004 | A1 |
| 20040267880 | Patiejunas | Dec 2004 | A1 |
| 20050005242 | Hoyle | Jan 2005 | A1 |
| 20050015451 | Sheldon et al. | Jan 2005 | A1 |
| 20050015599 | Wang et al. | Jan 2005 | A1 |
| 20050050027 | Yeh | Mar 2005 | A1 |
| 20050050043 | Pyhalammi et al. | Mar 2005 | A1 |
| 20050055321 | Fratkina | Mar 2005 | A1 |
| 20050060381 | Huynh et al. | Mar 2005 | A1 |
| 20050065950 | Chaganti et al. | Mar 2005 | A1 |
| 20050065980 | Hyatt et al. | Mar 2005 | A1 |
| 20050076060 | Finn et al. | Apr 2005 | A1 |
| 20050086187 | Grosser et al. | Apr 2005 | A1 |
| 20050105552 | Osterling | May 2005 | A1 |
| 20050108213 | Riise et al. | May 2005 | A1 |
| 20050120006 | Nye | Jun 2005 | A1 |
| 20050131727 | Sezan et al. | Jun 2005 | A1 |
| 20050149397 | Morgenstern et al. | Jul 2005 | A1 |
| 20050151849 | Fitzhugh et al. | Jul 2005 | A1 |
| 20050159220 | Wilson et al. | Jul 2005 | A1 |
| 20050159970 | Buyukkokten et al. | Jul 2005 | A1 |
| 20050160080 | Dawson | Jul 2005 | A1 |
| 20050165699 | Hahn-Carlson | Jul 2005 | A1 |
| 20050166240 | Kim | Jul 2005 | A1 |
| 20050171955 | Hull et al. | Aug 2005 | A1 |
| 20050177385 | Hull et al. | Aug 2005 | A1 |
| 20050182824 | Cotte | Aug 2005 | A1 |
| 20050183110 | Anderson | Aug 2005 | A1 |
| 20050187786 | Tsai | Aug 2005 | A1 |
| 20050192025 | Kaplan | Sep 2005 | A1 |
| 20050203801 | Morgenstern et al. | Sep 2005 | A1 |
| 20050216295 | Abrahamsohn | Sep 2005 | A1 |
| 20050216300 | Appelman et al. | Sep 2005 | A1 |
| 20050219375 | Hasegawa et al. | Oct 2005 | A1 |
| 20050234781 | Morgenstern et al. | Oct 2005 | A1 |
| 20050273510 | Schuh | Dec 2005 | A1 |
| 20060020631 | Cheong Wan et al. | Jan 2006 | A1 |
| 20060026013 | Kraft | Feb 2006 | A1 |
| 20060026067 | Nicholas et al. | Feb 2006 | A1 |
| 20060031108 | Oran | Feb 2006 | A1 |
| 20060040719 | Plimi | Feb 2006 | A1 |
| 20060047563 | Wardell | Mar 2006 | A1 |
| 20060047615 | Ravin | Mar 2006 | A1 |
| 20060053058 | Hotchkiss et al. | Mar 2006 | A1 |
| 20060069612 | Hurt et al. | Mar 2006 | A1 |
| 20060069616 | Bau | Mar 2006 | A1 |
| 20060069749 | Herz et al. | Mar 2006 | A1 |
| 20060074853 | Liu et al. | Apr 2006 | A1 |
| 20060085392 | Wang et al. | Apr 2006 | A1 |
| 20060085419 | Rosen | Apr 2006 | A1 |
| 20060089876 | Boys | Apr 2006 | A1 |
| 20060116924 | Angles et al. | Jun 2006 | A1 |
| 20060123053 | Scannell, Jr. | Jun 2006 | A1 |
| 20060129313 | Becker | Jun 2006 | A1 |
| 20060129605 | Doshi | Jun 2006 | A1 |
| 20060161894 | Oustiougov et al. | Jul 2006 | A1 |
| 20060168591 | Hunsinger et al. | Jul 2006 | A1 |
| 20060173838 | Garg et al. | Aug 2006 | A1 |
| 20060173985 | Moore | Aug 2006 | A1 |
| 20060178822 | Lee | Aug 2006 | A1 |
| 20060184508 | Fuselier et al. | Aug 2006 | A1 |
| 20060184579 | Mills | Aug 2006 | A1 |
| 20060212330 | Savilampi | Sep 2006 | A1 |
| 20060212401 | Amerally et al. | Sep 2006 | A1 |
| 20060227945 | Runge et al. | Oct 2006 | A1 |
| 20060235816 | Yang et al. | Oct 2006 | A1 |
| 20060236257 | Othmer et al. | Oct 2006 | A1 |
| 20060242139 | Butterfield et al. | Oct 2006 | A1 |
| 20060242178 | Butterfield et al. | Oct 2006 | A1 |
| 20060242259 | Vallath et al. | Oct 2006 | A1 |
| 20060258368 | Granito et al. | Nov 2006 | A1 |
| 20060282455 | Lee | Dec 2006 | A1 |
| 20070013560 | Casey | Jan 2007 | A1 |
| 20070015519 | Casey | Jan 2007 | A1 |
| 20070043766 | Nicholas et al. | Feb 2007 | A1 |
| 20070067104 | Mays | Mar 2007 | A1 |
| 20070067267 | Ives | Mar 2007 | A1 |
| 20070072591 | McGary et al. | Mar 2007 | A1 |
| 20070073583 | Grouf et al. | Mar 2007 | A1 |
| 20070073641 | Perry et al. | Mar 2007 | A1 |
| 20070086061 | Robbins | Apr 2007 | A1 |
| 20070087756 | Hoffberg | Apr 2007 | A1 |
| 20070088852 | Levkovitz | Apr 2007 | A1 |
| 20070100956 | Kumar | May 2007 | A1 |
| 20070112762 | Brubaker | May 2007 | A1 |
| 20070121843 | Atazky et al. | May 2007 | A1 |
| 20070130137 | Oliver et al. | Jun 2007 | A1 |
| 20070136048 | Richardson-Bunbury et al. | Jun 2007 | A1 |
| 20070136235 | Hess | Jun 2007 | A1 |
| 20070136256 | Kapur et al. | Jun 2007 | A1 |
| 20070136689 | Richardson-Bunbury et al. | Jun 2007 | A1 |
| 20070143345 | Jones et al. | Jun 2007 | A1 |
| 20070150168 | Balcom et al. | Jun 2007 | A1 |
| 20070150359 | Lim et al. | Jun 2007 | A1 |
| 20070155411 | Morrison | Jul 2007 | A1 |
| 20070161382 | Melinger et al. | Jul 2007 | A1 |
| 20070162569 | Robinson et al. | Jul 2007 | A1 |
| 20070162850 | Adler et al. | Jul 2007 | A1 |
| 20070168430 | Brun et al. | Jul 2007 | A1 |
| 20070173266 | Barnes | Jul 2007 | A1 |
| 20070179792 | Kramer | Aug 2007 | A1 |
| 20070185599 | Robinson et al. | Aug 2007 | A1 |
| 20070192299 | Zuckerberg et al. | Aug 2007 | A1 |
| 20070198506 | Attaran Rezaei et al. | Aug 2007 | A1 |
| 20070198563 | Apparao et al. | Aug 2007 | A1 |
| 20070203591 | Bowerman | Aug 2007 | A1 |
| 20070219708 | Brasche et al. | Sep 2007 | A1 |
| 20070233585 | Ben Simon et al. | Oct 2007 | A1 |
| 20070239348 | Cheung | Oct 2007 | A1 |
| 20070239517 | Chung et al. | Oct 2007 | A1 |
| 20070259653 | Tang et al. | Nov 2007 | A1 |
| 20070260508 | Barry et al. | Nov 2007 | A1 |
| 20070260604 | Haeuser et al. | Nov 2007 | A1 |
| 20070271297 | Jaffe et al. | Nov 2007 | A1 |
| 20070271340 | Goodman et al. | Nov 2007 | A1 |
| 20070273758 | Mendoza et al. | Nov 2007 | A1 |
| 20070276940 | Abraham et al. | Nov 2007 | A1 |
| 20070282621 | Altman et al. | Dec 2007 | A1 |
| 20070282675 | Varghese | Dec 2007 | A1 |
| 20070288278 | Alexander et al. | Dec 2007 | A1 |
| 20080005313 | Flake et al. | Jan 2008 | A1 |
| 20080005651 | Grefenstette et al. | Jan 2008 | A1 |
| 20080010206 | Coleman | Jan 2008 | A1 |
| 20080021957 | Medved et al. | Jan 2008 | A1 |
| 20080026804 | Baray et al. | Jan 2008 | A1 |
| 20080028031 | Bailey et al. | Jan 2008 | A1 |
| 20080040283 | Morris | Feb 2008 | A1 |
| 20080046298 | Ben-Yehuda et al. | Feb 2008 | A1 |
| 20080070588 | Morin | Mar 2008 | A1 |
| 20080070697 | Robinson et al. | Mar 2008 | A1 |
| 20080086261 | Robinson et al. | Apr 2008 | A1 |
| 20080086356 | Glassman et al. | Apr 2008 | A1 |
| 20080086431 | Robinson et al. | Apr 2008 | A1 |
| 20080086458 | Robinson et al. | Apr 2008 | A1 |
| 20080091796 | Story et al. | Apr 2008 | A1 |
| 20080096664 | Baray et al. | Apr 2008 | A1 |
| 20080102911 | Campbell et al. | May 2008 | A1 |
| 20080104061 | Rezaei | May 2008 | A1 |
| 20080104227 | Birnie et al. | May 2008 | A1 |
| 20080109761 | Stambaugh | May 2008 | A1 |
| 20080109843 | Ullah | May 2008 | A1 |
| 20080114751 | Cramer et al. | May 2008 | A1 |
| 20080120183 | Park | May 2008 | A1 |
| 20080120308 | Martinez et al. | May 2008 | A1 |
| 20080120390 | Robinson et al. | May 2008 | A1 |
| 20080120690 | Norlander et al. | May 2008 | A1 |
| 20080133750 | Grabarnik et al. | Jun 2008 | A1 |
| 20080147655 | Sinha et al. | Jun 2008 | A1 |
| 20080147743 | Taylor et al. | Jun 2008 | A1 |
| 20080148175 | Naaman et al. | Jun 2008 | A1 |
| 20080154720 | Gounares | Jun 2008 | A1 |
| 20080163284 | Martinez et al. | Jul 2008 | A1 |
| 20080172632 | Stambaugh | Jul 2008 | A1 |
| 20080177706 | Yuen | Jul 2008 | A1 |
| 20080270579 | Herz et al. | Oct 2008 | A1 |
| 20080285886 | Allen | Nov 2008 | A1 |
| 20080301250 | Hardy et al. | Dec 2008 | A1 |
| 20080320001 | Gaddam | Dec 2008 | A1 |
| 20090005987 | Vengroff et al. | Jan 2009 | A1 |
| 20090006336 | Forstall et al. | Jan 2009 | A1 |
| 20090012934 | Yerigan | Jan 2009 | A1 |
| 20090012965 | Franken | Jan 2009 | A1 |
| 20090043844 | Zimmet et al. | Feb 2009 | A1 |
| 20090044132 | Combel et al. | Feb 2009 | A1 |
| 20090063254 | Paul et al. | Mar 2009 | A1 |
| 20090070186 | Buiten et al. | Mar 2009 | A1 |
| 20090073191 | Smith et al. | Mar 2009 | A1 |
| 20090076889 | Jhanji | Mar 2009 | A1 |
| 20090100052 | Stern et al. | Apr 2009 | A1 |
| 20090106356 | Brase et al. | Apr 2009 | A1 |
| 20090125517 | Krishnaswamy et al. | May 2009 | A1 |
| 20090132941 | Pilskalns et al. | May 2009 | A1 |
| 20090144141 | Dominowska et al. | Jun 2009 | A1 |
| 20090150501 | Davis et al. | Jun 2009 | A1 |
| 20090150507 | Davis et al. | Jun 2009 | A1 |
| 20090165051 | Armaly | Jun 2009 | A1 |
| 20090171939 | Athsani et al. | Jul 2009 | A1 |
| 20090177603 | Honisch | Jul 2009 | A1 |
| 20090187637 | Wu et al. | Jul 2009 | A1 |
| 20090204484 | Johnson | Aug 2009 | A1 |
| 20090204672 | Jetha et al. | Aug 2009 | A1 |
| 20090204676 | Parkinson et al. | Aug 2009 | A1 |
| 20090216606 | Coffman et al. | Aug 2009 | A1 |
| 20090222302 | Higgins | Sep 2009 | A1 |
| 20090222303 | Higgins | Sep 2009 | A1 |
| 20090234814 | Boerries et al. | Sep 2009 | A1 |
| 20090234909 | Strandeil et al. | Sep 2009 | A1 |
| 20090249482 | Sarathy | Oct 2009 | A1 |
| 20090265431 | Janie et al. | Oct 2009 | A1 |
| 20090281997 | Jain | Nov 2009 | A1 |
| 20090299837 | Steelberg et al. | Dec 2009 | A1 |
| 20090313546 | Katpelly et al. | Dec 2009 | A1 |
| 20090320047 | Khan et al. | Dec 2009 | A1 |
| 20090323519 | Pun | Dec 2009 | A1 |
| 20090328087 | Higgins et al. | Dec 2009 | A1 |
| 20100002635 | Eklund | Jan 2010 | A1 |
| 20100014444 | Ghanadan et al. | Jan 2010 | A1 |
| 20100063993 | Higgins et al. | Mar 2010 | A1 |
| 20100070368 | Choi et al. | Mar 2010 | A1 |
| 20100118025 | Smith et al. | May 2010 | A1 |
| 20100125563 | Nair et al. | May 2010 | A1 |
| 20100125569 | Nair et al. | May 2010 | A1 |
| 20100125604 | Martinez et al. | May 2010 | A1 |
| 20100125605 | Nair et al. | May 2010 | A1 |
| 20100185642 | Higgins et al. | Jul 2010 | A1 |
| Number | Date | Country |
|---|---|---|
| 1362302 | Nov 2003 | EP |
| 2002312559 | Oct 2002 | JP |
| 1020000036897 | Jul 2000 | KR |
| 1020000054319 | Sep 2000 | KR |
| 10-2000-0064105 | Nov 2000 | KR |
| 1020030049173 | Jun 2003 | KR |
| 10-0801662 | Feb 2005 | KR |
| 1020060043333 | May 2006 | KR |
| 102007034094 | Mar 2007 | KR |
| 1020070073180 | Jul 2007 | KR |
| 1020080048802 | Jun 2008 | KR |
| W02006116196 | Nov 2006 | WO |
| WO 2007022137 | Feb 2007 | WO |
| WO 2007027453 | Mar 2007 | WO |
| WO 2007070358 | Jun 2007 | WO |
| WO2007113546 | Oct 2007 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20090150489 A1 | Jun 2009 | US |
