WO2013041345A1 - Traitement d'événements d'interaction basé sur un contexte - Google Patents

Traitement d'événements d'interaction basé sur un contexte Download PDF

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Publication number
WO2013041345A1
WO2013041345A1 PCT/EP2012/066980 EP2012066980W WO2013041345A1 WO 2013041345 A1 WO2013041345 A1 WO 2013041345A1 EP 2012066980 W EP2012066980 W EP 2012066980W WO 2013041345 A1 WO2013041345 A1 WO 2013041345A1
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WIPO (PCT)
Prior art keywords
interaction
interaction events
events
linked
relevant
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PCT/EP2012/066980
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English (en)
Inventor
Akhil MATHUR
Samik Datta
Anirban Majumder
Sreedal MENON
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Alcatel Lucent
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Publication of WO2013041345A1 publication Critical patent/WO2013041345A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/907—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/904—Browsing; Visualisation therefor

Definitions

  • the present subject matter relates to interaction events in a communication network and, particularly but not exclusively, to context-based processing of interaction events in the communication networks.
  • interactions taking place through a mobile phone are captured to create a library of events based on the interactions.
  • the interactions can include, for example, text messages, emails, phone calls, memos, reminders, and calendar updates.
  • the captured interactions are then displayed to the user on the mobile phone in a raw form.
  • the interactions are captured and the details of the interaction, such as sender and time, are displayed to the user in the same format as captured.
  • a large number of interactions take place through the mobile phone over a small period of time, say a week, and displaying a list of all the interactions for that period may overwhelm the user.
  • a method for context-based processing of interaction events in a communication network includes extracting one or more context features for each of a plurality of interaction events.
  • the context features can be extracted from interaction information associated with the plurality of interaction event.
  • linked interaction events are identified, based on a query parameter.
  • the query parameter can be based on one or more of the context feature.
  • relevant interaction events are determined from among the linked interaction events.
  • the relevant interaction events are rendered to depict the relevant interaction events as graphics. The rendering of the relevant interaction events can be based on the context features and the comparisons between the linked interaction events.
  • an interaction processing system for context- based processing of interaction events in a communication network.
  • the interaction processing system includes a processor and a memory coupled to the processor.
  • the memory includes an interaction identification module configured to identify linked interaction events from a plurality of interaction events based on a query parameter.
  • the query parameter can be based on at least one context feature associated with each of the plurality of interaction events.
  • the interaction identification module is further configured to select relevant interaction events from among the linked interaction events based on a relational score.
  • the relational score is computed for each comparison between the linked interaction events.
  • the interaction processing system further comprises a rendering module configured to obtain graphics for each of the relevant interaction events and render the relevant interaction events based on the obtained graphics.
  • a computer-readable medium having embodied thereon a computer program for executing a method for context-based processing of interaction events.
  • the method of the computer-readable medium includes obtaining interaction information associated with a plurality of interaction events and extracting one or more context features for each of the plurality of interaction events, from the interaction information.
  • linked interaction events are identified based on a query parameter.
  • the query parameter can be based on one or more of the context features.
  • relevant interaction events are determined from among the linked interaction events, based on comparisons between the linked interaction events. The comparison can include ascertaining a similarity between the linked interaction events being compared.
  • the relevant interaction events are rendered based on graphics associated with each of the relevant interaction events.
  • Figure 1 illustrates a network implementation of an interaction processing system for context-based processing of interaction events in a communication network, according to an embodiment of the present subject matter.
  • Figure 2 illustrates a visualization rendered by the interaction processing system, according to an embodiment of the present subject matter.
  • Figure 3 illustrates a method for context-based processing of interaction events in a communication network, according to an embodiment of the present subject matter.
  • the present subject matter relates to context-based processing of interaction events in a communication network.
  • interaction events such as mails, text messages, phone calls, taking place through a mobile phone are captured and displayed to a user in raw form, i.e., the metadata of the interaction
  • the sender, the recipients, and a time stamp associated with the interaction are displayed to the user.
  • conventional tools can be used to track and maintain a log of the interactions and events taking place through a computing device, such as a personal computer or a laptop.
  • Such events can include web pages visited and files accessed by the user on the computing device, along with the time stamp.
  • the content of the interaction such as the text in the mail or the message, or the content of the file accessed are not captured.
  • the contents of the interactions may be captured, the contents of two different events relating to the same topic or similar topics are not linked.
  • Such interactions when accumulated over a certain period of time, say a week or a month, may involve a large number of events that are captured and displayed. The user may be unable to use and access such large amounts of data at one time, and hence, the whole exercise of capturing the events may turn out to be futile. Additionally, when the log of the interactions is accessed by the user or a different entity, neither of them may be able to recognize and relate to the content of the interaction due to an absence of a context between the different interactions, and may not be able to link one event to another similar event belonging to the same topic.
  • the present subject matter relates to context-based processing of interaction events in a communication network.
  • the communication network includes an interaction processing system, which is capable of interacting with one or more user devices.
  • the interaction processing system can be a central server communicating with the user devices over the communication network.
  • the user devices can be understood as those devices through which the user achieves interaction events, such as text messages, emails, phone calls, and posts or photo uploads on social networking sites, and can include mobile terminals, laptops, personal computers, and tablets.
  • the interaction events can further include information describing the files accessed, such as audio files played, movies watched, and books read, on the user devices, communication logs, such as logs of emails, social networking site updates, and chats, the websites visited using the user device, and user environment details, such as location, time and motion information.
  • Each interaction event achieved through the user devices is tracked and logged.
  • the user devices can transmit interaction information to the interaction processing system.
  • the interaction information can include the interaction event itself, for example, the text message, email or an address of a website on which the interaction event is achieved.
  • the interaction information can include the content of the interaction event and the metadata of the interaction event, such as the sender and recipient, and the time stamp associated with the interaction event.
  • the interaction information is sent to the interaction processing system.
  • the interaction events can be monitored for a predetermined period of time, for example, a week, and the interaction information stored. Further, after the lapse of the predetermined period of time, the interaction information can be sent to the interaction processing system.
  • the interaction information received from the user devices can be indexed and stored to generate various visualizations of the interaction events.
  • the interaction information is processed to extract context features from the interaction information.
  • the context features can include features associated with users participating in the interaction event, location of occurrence of the event, and content of the interaction event.
  • linked interaction events are determined, for example, to establish a context between different interaction events. Further, from amongst the linked interaction events, relevant interaction events are identified.
  • the relevant interaction events can be understood as those interaction events which can be used for effectively depicting the interactions achieved by the users. In an example, the relevant interaction events can be identified based on one or more similar context features between the various interaction events or age of the interaction events, or both.
  • a query parameter can be utilized.
  • all the interaction events that include the query parameter in the extracted context features are identified as the linked interaction events.
  • the query parameter can include the rules on the basis of which it is determined whether the interaction events are linked or not.
  • the query parameter can be based on one or more context features extracted from the interaction information to determine the linked interaction events on the basis of the users, the location, or the content, or a combination thereof.
  • the query parameter can be based on an interaction event type to restrict the determination of linked interaction events from amongst the same kind of events.
  • the relevant interaction events are selected from amongst the linked interaction events.
  • each of linked interaction events are compared amongst themselves and for each comparison a relational score is computed.
  • the linked interaction events are classified as relevant interaction events.
  • the relational score is also based on a similarity between the two interaction events being matched based on the respective extracted context features.
  • the relational score can be determined based on a recentness of the interaction events. For example, the linked interaction events which are older than, say, a month, are not classified as relevant interaction events.
  • the relevant interaction events obtained based on the relational score have high degree of similarity and can be related to each other based on various context features, for example, apart from those included in the query parameter. Further, the relevant interaction events and the relation between the relevant interaction events can be used to render a visualization of the relevant interaction events.
  • the visualization can include the representation of a single interaction event.
  • the representation is rendered in the form of a graphic.
  • the graphics associated with the context features of an interaction event are obtained and used to render the representation of the interaction event.
  • the graphics can include images of a primary user, one or more secondary users, the location of the interaction events, and the content associated with the interaction event.
  • the visualization can also include graphics depicting the various interaction events related to the representation based on various context features. Since, the various relevant interaction events can have common context features, the representation can be used to depict the relevant interaction events, related to each other through one or more context features. For example, the representation can include those interaction events which are related to each other on the basis of the location of the interaction events, i.e., the interaction events have occurred at the same location. Accordingly, the user can select one of the graphics in the representation to access all the interaction events related to that graphic. For example, the user can select the image of the primary user to obtain all the interaction events linked with the primary user, the linked interaction events being determined based in the same manner as described above. Further, in an implementation, the various representations can be provided on a time-line in the visualization to depict the various interaction events in a week-by-week, or day- by-day, or hour-by-hour manner.
  • the functionality related to the rendering the representation and the overall visualization is described with reference to the interaction processing system, it will be understood that one or more of the user devices can be configured to achieve the same functionality.
  • the functionality of rendering can be provided on a cloud-based server which can provide the rendered representation as a web-service.
  • various users can remotely access and view the rendered representation and the visualization of the interaction events.
  • the identification of the relation among the various interaction events based on the query parameter contextually associates one interaction event to other interaction events.
  • Another user accessing the visualization, or a user of the user devices accessing the representations after a long period is able to easily recognize the context of the various interaction events in the representation.
  • the visualization of the interaction events can be can be easily shared with various other users over the communication network.
  • the relevant interaction events achieved by the user can be summarized in the form of an engaging visualization, say, in the form of a comic strip, and shared with the users on a social networking portal. Hence, the experience of sharing and viewing the interaction events of oneself and of other users is enhanced and is made convenient.
  • FIG. 1 illustrates a network environment 100 implementing an interaction processing system 102, according to an embodiment of the present subject matter.
  • the interaction processing system 102 is configured to process various interaction events achieved by a user, on the basis of context associated with each interaction event.
  • the interaction processing system 102 is connected to and interacts with a plurality of user devices 104-1 , 104-2 ...104-N, collectively referred to as the user devices 104 and individually referred to as a user device 104.
  • the interaction processing system 102 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, and a network server.
  • the user devices 104 can include, without limitation, desktop computers, hand-held devices, laptops or other portable computers, tablet personal computers, network computers, mobile phones, multi-media enabled phones, and smart phones.
  • the interaction processing system 102 and the user devices 104 can communicate with each other over a communication network 106.
  • the communication network 106 may be a wireless or a wired network, or a combination thereof.
  • the communication network 106 can be implemented as a computer network, as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and such.
  • the network 106 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), to communicate with each other.
  • HTTP Hypertext Transfer Protocol
  • TCP/IP Transmission Control Protocol/Internet Protocol
  • WAP Wireless Application Protocol
  • the network communication 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices.
  • the communication network 106 can be implemented as a telecommunication network.
  • the communication network 106 can be a collection of individual networks, interconnected with each other and functioning as a single large network (e.g., the internet or an intranet).
  • GSM Global System for Mobile Communication
  • UMTS Universal Mobile Telecommunications System
  • PCS Personal Communications Service
  • TDMA Time Division Multiple Access
  • CDMA Code Division Multiple Access
  • NTN Next Generation Network
  • PSTN Public Switched Telephone Network
  • ISDN Integrated Services Digital Network
  • the communication network 106 includes various network entities, such as gateways, routers; however, such details have been omitted for the sake of brevity.
  • the interaction processing system 102 can use General Packet Radio Service (GPRS) or Bluetooth for communicating with the user devices 104.
  • GPRS General Packet Radio Service
  • Bluetooth Bluetooth
  • the communication network can be implemented as a combination of a computer network as well as a telecommunication network.
  • the interaction processing system 102 includes processors) 108 coupled to a memory 1 10.
  • the interaction processing system 102 further includes interface(s) 1 12, for example, to facilitate communication with the user devices 104.
  • the interface(s) 1 12 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. Further, the interface(s) 1 12 enables the interaction processing system 102 to communicate with other devices, such as web servers and external repositories.
  • the interface(s) 1 12 can also facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.
  • the interface(s) 1 12 may include one or more ports.
  • the processor(s) 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.
  • the processors) 108 are configured to fetch and execute computer-readable instructions stored in the memory 1 10.
  • the memory 1 10 can include any computer-readable medium known in the art including, for example, volatile memory such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. Further, the memory 1 10 includes module(s) 1 14 and data 1 16.
  • the module(s) 1 14 include, for example, an extraction module 1 18, an interaction identification module 120, a rendering module 122, and other module(s) 124.
  • the other module(s) 124 may include programs or coded instructions that supplement applications or functions performed by the interaction processing system 102.
  • the data 1 16 can include interaction information data 126, linked interaction data 128, representation data 130, and other data 132.
  • the other data 132 may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 1 14.
  • representation data 130 is shown internal to the interaction processing system 102, it may be understood that the representation data 130 can reside in an external repository (not shown in the figure), which is coupled to the interaction processing system 102.
  • the interaction processing system 102 may communicate with the external repository through the interface(s) 1 12 to obtain information from the representation data 130.
  • the interaction processing system 102 is configured to track interaction events achieved through the user devices 104, and represent the interaction events in the form of a visualization.
  • the interaction events can include, for example, text messages, emails, phone calls, blogs and photo uploads on a social networking website, files accessed, videos watched, websites visited, audio files played, movies watched, and documents or books read.
  • the interaction events can also include physical interactions, such as locations visited.
  • the user devices 104 can be understood to be operated by a single user carrying out the various interaction events.
  • the extraction module 118 can be configured to monitor and track the interaction events being achieved through the user devices 104 to obtain interaction information associated with the interaction events.
  • the interaction information can include the interaction event, for example, the email sent or received, an address of the webpage browsed, or information relating to document accessed.
  • the interaction information includes the content of the interaction event as well as the metadata, such as sender and recipient, of the interaction event.
  • the extraction module 118 periodically obtains the interaction information associated with the all interaction events achieved through the user devices 104, say, the interaction events achieved in a day or in a week.
  • the extraction module 118 may track each interaction event occurring through the user devices 104 and obtains the interaction information from the user devices 104.
  • the user device 104 such as the user device 104-1 , can include an interaction capture module 134.
  • the interaction capture module 134 similar to the extraction module 118, can track the interaction events on the user device 104 and provide the interaction information associated with the interaction event to the extraction module 1 18 on the interaction processing system.
  • the interaction capture module 134 is shown as implemented on the user device 104-1 in Figure 1 , it will be understood that the interaction capture module 134 can be independently implemented on each of the user devices 104.
  • the extraction module 118 can also obtain other information such as that relating to an environment of each user device 104 in the interaction information.
  • each user device 104 can transmit information relating to the location or geographical coordinates of the user device 104, say, to determine the location visited by the user, ambient light level, and acceleration values to determine whether the user device 104 is static or not, say, in case a gaming application is being accessed on the user device 104.
  • the extraction module 118 can further extract one or more context features from the interaction information.
  • the extraction module 118 obtains the context features for that interaction event.
  • the context features can be information for establishing a context of the interaction event and can include, for example, names of one or more users participating in the interaction event, a location of the occurrence of the interaction event, and a content of the interaction event.
  • the users can include a primary user, say, a sender of text messages or emails, or a user writing a post on a social networking site, and can include one or more secondary users, say, those commenting on a post on a social networking website or recipients of the text messages or emails.
  • the location can be obtained based on the geographical coordinates of the user device 104 transmitted in the interaction information.
  • the content can include information, such as subject or a topic in an interaction event or a theme of a book or a movie accessed on the user device 104, and a type of interaction event, say, a text message or an email.
  • the content can also include a sentiment parameter associated with the interaction event, which is determined by, say, processing a digital photograph in the interaction event for emotion analysis, identifying a smiley in a message, or identifying a tone of a message based on the words used.
  • the sentiment parameters associated with the event can be determined based on the audio files accessed, video files played, and the books and web pages visited.
  • the extraction module 118 can implement machine learning and data mining techniques to extract and obtain the context features associated with the interaction events from the interaction information.
  • the extraction module 118 can be configured to classify the features in one of users, location, and content categories described above, and store the extracted context features in the interaction information data 126.
  • relevant interaction events are determined from among the various interaction events tracked on the user devices 104.
  • the relevant interaction events can be understood as those interaction events, which are considered for visualizing the interactions.
  • the interaction identification module 120 can be configured to follow a two step approach. In said implementation, the interaction identification module 120 can first identify linked interaction events from among the various interaction events occurring on the user devices 104, and subsequently, select the relevant interaction events from the linked interaction events.
  • the interaction identification module 120 receives a query parameter.
  • the query parameter can include a context feature, say, a user or a location or a keyword associated with the content, based on which the related or linked interaction events can be identified.
  • the query parameter can also include an interaction event type, for example, text message, email, or web pages.
  • the interaction identification module 120 can select the interaction events from among the type of interaction event included in the query parameter to be processed for identification. It will be understood that in case the query parameter is devoid of the interaction event type, all the interaction events are selected for identification.
  • the interaction identification module 120 can include each interaction event type one by one in the query parameter, and then step-wise determine the linked interaction events, also based on the other parameters in the query parameter. In such a scheme of identifying the linked interaction events, the interaction identification module 120 ensures that the entire spectrum of types of interaction events is covered for identifying the linked interaction events.
  • the interaction identification module 120 can request the query parameter from the user and obtain the query parameter from the user.
  • the interaction identification module 120 can be configured to automatically select the query parameter based on predefined rules, for example, the type of interaction events, the users, the content, or a combination thereof.
  • the query parameter obtained from the user or the one automatically selected is stored in the linked interaction data 128.
  • the interaction identification module 120 is configured to compare the context features extracted from the interaction information of each interaction event with the query parameter.
  • the interaction events, whose context features include the query parameter and which also belong to the interaction type as specified in the query parameter are selected as the linked interaction events. Subsequently, in an implementation, the relevant interaction events are determined from among the linked interaction events.
  • the interaction identification module 120 is configured to compare the extracted context features associated with each of the linked interaction events among themselves and compute a relational score for each comparison.
  • the relational score is computed based on the extent to which the context features associated with one interaction event is similar to the context features associated with another interaction event.
  • the relational score can be understood to be based on a similarity index computed for each comparison between the context features of the linked interaction events.
  • the interaction identification module 120 can also identify the matching context features between the linked interaction events, and store the matching context features in the linked interaction data 128.
  • relational score can also be based on a recentness index of the linked interaction events.
  • the recentness index can be indicative of an age or recentness of an interaction event.
  • the relational scores computed by the interaction identification module 120 is also based on the recentness index of the interaction events for which the context features are compared. For example, in case the recentness index of either of the interaction events being compared is low, which means that either of the interaction events is older than, say, a week, then even for a high similarity index, the relational score for the comparison is low.
  • relational score for a comparison of context features, based on the similarity index is s
  • Age(d) represents a recentness factor associated with the interaction event and is indicative of the recentness of the interaction event.
  • the recentness factor can be measured in terms of, for example, hours, days, or weeks.
  • the interaction identification module 120 in such an implementation, can compare the context features of the captured interaction event with the context features of each of the previous interaction events. For each comparison, the interaction identification module 120 can compute the relational score in the manner as described above.
  • the interaction identification module 120 can determine the relevant interaction events.
  • the interaction identification module 120 can associate the top k linked interaction events with highest relational score and determine the linked interaction events as the relevant interaction events.
  • the relevant interaction events can be related to each other by more than one context features, as determined by the interaction identification module 120 and stored in the linked interaction data 128.
  • a text message can be linked with another text message based on the sender, and can be linked with an email based on the content or the subject.
  • the related events group obtained includes a plurality of interaction events, which may be linked with each other on the basis of more than one context feature, and hence, involve a complex linking between among themselves.
  • the linked interaction events and the linking of each interaction event with another interaction event in the related events group are also stored in the linked interaction data 128.
  • the identification of the relevant interaction events from the various interaction events taking place on the user devices 104 is described with reference to a two-step approach, it will be understood that the two-steps of the above approach can be combined to form a single step. In said implementation, the relevant interaction events can be identified from all the interaction events in one step.
  • the rendering module 122 is configured to generate a representation to depict the interaction events and the links between the interaction events.
  • the representation hence achieved serves as visualization of the interaction events and of the relationship between the various interaction events.
  • the rendering module 122 can obtain graphics related to the interaction events and use the graphics to render the representation.
  • the rendering module 122 can obtain the graphics relating to the context features of the interaction events, say, images relating to the primary user, the one or more secondary users, location of the occurrence of the interaction event, and the content of the interaction event.
  • the representation of the interaction events is achieved in the form of a cartoon strip, and the graphics relating to, for example, the context features, are obtained in the form of cartoon images.
  • the rendering module 122 can obtain previously stored graphics from the representation data 130.
  • the rendering module 122 can be configured to connect to the internet and fetch graphics relating to the interaction event, for example, based on the context features associated with the interaction event. In yet another implementation, the rendering module 122 can also request the user operating the user devices 104 to select or provide the graphics for generating the representation.
  • the rendering module 122 can generate the representation using the obtained graphics for that interaction event.
  • the rendering engine 122 can provide a list of all the linked interaction events, which include the selected graphic as a common feature.
  • the rendering module 122 can render the representation for each of the linked interaction event and generate a complete map of the linked interaction events, having interconnected graphics.
  • the rendered representation can be stored in the representation data 130 and can be accessed by the user devices 104 over the network 106.
  • the rendering module 122 can further be configured to generate a thumbnail representation for each interaction event on a time-line and depict the interaction events relating to the user devices 104 on the time-line.
  • the rendering module 122 can depict the interaction events in an hour-by-hour, a day-by-day, or a week-by- week manner.
  • the user can view the time-line of the interaction events on a graphical user interface (not shown) connected to, for example, the user devices 104 or directly connected to the interaction processing system 102.
  • the rendering module 122 can render the representation for that interaction event in the manner as described above.
  • the rendering module 122 can be provided as a client application on one or more of the user devices 104 or on a cloud based server.
  • the user devices 104 are provided with the capability of rendering the representation of the interaction events based on the interaction events, the context features, and the linking among the interaction events, for example, stored in the linked interaction data 128.
  • the representation can be rendered and provided to the users on a web page associated with the cloud based server.
  • FIG. 2 illustrates a visualization 200 of the interaction events provided by the interaction processing system 102, according to an embodiment of the present subject matter.
  • the visualization 200 is an overall representation of the related interaction events and includes the interaction events shown on a time-line 202.
  • each interaction event is depicted as a miniaturized graphic 204.
  • the interaction events are depicted by the miniaturized graphics 204-1 , 204-2,...204-N, respectively, collectively referred to as miniaturized graphics 204 and individually referred to as miniaturized graphic 204.
  • the miniaturized graphics 204 can be in the form of thumbnail representations.
  • time-line 202 can represent the interaction events in hour- wise manner, i.e., the interaction events occurring per hour, as shown in Figure 2. However, it will be understood that the time-line 202 can represent the interaction events in day-wise or in week- wise manner also.
  • the rendering module 122 When prompted by the user to view an enlarged representation of the miniaturized graphic 204, say by clicking on the miniaturized graphic 204, the rendering module 122 renders a representation 206 and provides the rendered representation 206 to the user.
  • the representation 206 can be rendered by the rendering module 122 in real-time when prompted by the user.
  • the rendering module 122 can store rendered representation 206 and can retrieve the representation 206 upon request by the user for viewing.
  • the rendering module 122 can render the representation 202 for the interaction event in the form of a graphical representation.
  • the representation 202 can be in the form of a cartoon illustration formed by superimposing images related to the context features of the interaction event.
  • the rendering module 122 can obtain the graphics and render the representation 206.
  • the rendering module 122 can obtain graphics relating to the context features of the interaction event and use the graphics to render the representation 206.
  • the rendering module 122 can use the image related to the location of the interaction event as background and may superimpose the images of the primary users, the secondary users, and the content over the background image.
  • the rendering module 122 can be configured to generate a clear and uncluttered graphic by, for example, summarizing the content and using a graphic for the summarized content, and using a group image in case the secondary users exceed a predefined number, say, 3.
  • the rendering module 122 can also render the representation 206 to depict all the relevant interaction events 208.
  • the relevant interaction events 208 as explained earlier are related through one or more context features, and are also linked in the representation 206 by the context features.
  • the relevant interaction events 208 can include various interaction events, such as mails, text messages, meetings, files accessed, and web pages visited.
  • each graphic in the representation 206 can be linked to another graphic of another interaction event.
  • the rendering module 122 provides a list of all the relevant interaction events 208 which are related through the context feature represented by the graphic, which is clicked on.
  • the relevant interaction events 208 depicted in the visualization 202 can be selected for inclusion in the visualization 202 based on a relevance score.
  • the relevance score can be based on the similarity index, i.e., based on the matching context features between the interaction events.
  • “Tom” receives an email from “Bob” about a project having content “Sparrow, Lifelog”. "Tom” makes a phone call to “Bob” to discuss the content “Sparrow. Lifelog”. In addition, “Tom” visits “Bob” at an office to attend a meeting regarding the content “Sparrow, Lifelog”. The meeting, involving "Tom” and “Bob", is conducted in a conference room "Ellora”.
  • the extraction module 118 can extract the various context features, for example, classified into users, location, and content, from the above interaction events.
  • the context feature extracted from the interaction event "meeting” can include “Tom” and Bob classified as primary user and secondary user, respectively, “Ellora” as the location of the interaction event, and “Sparrow, Lifelog” as the content of the interaction event.
  • the interaction identification module 120 can identify linked interaction events from among "email”, “phone call” and “meeting”, based on the content selected as the query parameter.
  • the interaction events "email”, “phone call”, and “meeting” are linked based on the content.
  • the relational score is computed for the comparison of the interaction events, based on the similarity index and the recentness index for comparisons between the interaction events to identify the relevant interaction events.
  • the linked interaction events are selected as the relevant interaction events.
  • links between the relevant interaction events can be identified based on the other context features of the interaction events. In this case, apart from being linked on the basis of the content, the interaction events are linked based on the users "Tom" and "Bob". Hence, the links between the interaction events can be identified based on more than one context feature.
  • the rendering module 122 can render the representation 206 for the interaction event "meeting” based on the context features related to the interaction event.
  • the rendering module 122 obtains the graphics related to the primary user "Tom”, the secondary user "Bob", the location conference room “Ellora”, and the content "Sparrow, Lifelog".
  • the graphic for the conference room is used as the background and the graphics for the primary user "Tom” and the secondary user "Bob" are superimposed on the graphic of the conference room.
  • the graphics for the location and the content i.e., the name of the location "Ellora” and the content “Sparrow, Lifelog", are superimposed on the background graphic to include the content of the interaction event "meeting" in the representation 206.
  • the rendering module 122 can render the representation of the relevant interaction events 208 and provide the graphic of the linked interaction events.
  • graphics for the linked interaction events "email” and “phone call” are obtained by the rendering module 122, and included in the overall visualization 200.
  • the rendering module 122 provides all the interaction events linked to the graphic clicked upon.
  • Figure 3 illustrates a method 300 for context-based processing of interaction event in a communication network, according to an embodiment of the present subject matter.
  • the order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300, or alternative methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein.
  • the methods can be implemented in any suitable hardware, software, firmware, or combination thereof.
  • steps of the method can be performed by programmed computers.
  • program storage devices for example, digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all of the steps of the described method.
  • the program storage devices may be, for example, digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media.
  • the embodiments are also intended to cover both, communication network and communication devices configured to perform said steps of the method 300.
  • interaction information associated with a plurality of interaction events achieved through one or more user devices 104 is received.
  • the extraction module 118 or the interaction capture module 134, or both can track the plurality of interaction events on the user devices 104 obtain the interaction information associated with the interaction events.
  • the interaction events can include a text message, an email, a phone call, a document accessed, an audio played, a video or movie watched, a location visited by the user, a photograph uploaded or a blog uploaded on a social networking site.
  • the interaction information associated with the interaction events can include the interaction event itself, say, the mail received or a text message sent, as well as a metadata associated with the interaction event, say, the sender and recipient of the mail of text message and time stamp associated with the interaction event.
  • the interaction information can include environmental information, such as geographical coordinates relating to a location of the user device 104, ambient light levels, and acceleration values.
  • the interaction information can be obtained in response to each interaction event achieved on the user device.
  • the interaction events achieved on the user devices are tracked for a period of time, say a day or a week, and the interaction information is received periodically.
  • one or more context features are extracted from the received interaction information for the plurality of interaction events.
  • the context features extracted from the interaction information can be related to one or more of the users involved in the interaction event, location related to the interaction event, or content of the interaction event.
  • the context features can include the sender and recipients of a text message relating to a project or a user uploading a photograph on the social networking site, the location of a meeting held in relation to a project, and the subject of the text messages or the agenda of the meeting, and type of the interaction event.
  • context features can also include sentiment parameters associated with the interaction event.
  • Such context features can be determined based on, for example, processing the graphics for emotion analysis, a smiley identified in a text message, the books or web pages accessed, and the audio files or videos watched.
  • the extraction module 118 can implement variety of machine learning and data mining techniques to extract the context features from the interaction information.
  • linked interaction events are identified from the plurality of interaction events, based on a query parameter.
  • the query parameter can be based on one or more of the context feature, such as the content, the user, or the location of the interaction events.
  • the query parameter can include the type of the interaction event.
  • the interaction identification module 120 sorts and identifies those interaction events as linked interaction events from among the plurality of interaction events whose context features include the query parameter.
  • the query parameter is based on the content of the interaction event and includes a keyword "Sparrow”
  • all the interaction events having the keyword "Sparrow” in the content-related context features are identified as linked interaction events.
  • the query parameter additionally includes "email” as the interaction event type, then the interaction events are further sorted out based on whether the interaction events are "email" type interaction events or not.
  • a context feature of the fresh interaction event referred to as the captured interaction event
  • the query parameter the linked interaction events for the captured interaction event
  • each of the linked interaction events are compared to obtain a relational score.
  • the interaction identification module 120 compares context features extracted from one of the linked interaction events to the context features of another linked interaction event.
  • the relational score can be based on a similarity index between the context features of the two interaction events.
  • the similarity index can be a measure of the extent to which the context features of the two interaction events are similar. In an example, higher the similarity index, higher is the relational score.
  • the relationship between two linked interaction events can be identified based on the other context features apart from those included in the query parameter.
  • the relational score can be based on a recentness index associated with the two interaction events.
  • the recentness index can be indicative of the age of the interaction event, say, whether the interaction event is a freshly occurred interaction event or an old event.
  • a recent interaction event has a higher recentness index than an old interaction event. Accordingly, a high recentness index can translate into a high relational score, and a low recentness index can result in a low relational score.
  • the relational score for the comparison between the linked interaction events is computed based on a cumulative score of the similarity index and the recentness index.
  • s illustrates the relational score on the basis of similarity index
  • s ' indicates the relational score based on the recentness index
  • Age(d) represents a recentness factor indicative of the recentness of the interaction event for which the relational score is computed.
  • each interaction event is tracked and the interaction information achieved, each of the linked interaction event is compared with the reference interaction event to compute a relational score for each linked interaction event.
  • relevant interaction events are selected from the linked interaction events.
  • the interaction identification module 120 selects the linked interaction events with the top k relational scores as the relevant interaction events.
  • the relevant interaction events can be understood as the most related interaction events and are stored.
  • the additional links between the interaction events based on the other context features are also determined and stored.
  • the various links between the interaction events are identified based on the various context features.
  • complex interrelationships between the relevant interaction events are achieved based on different context features. For example, a text message may be related to a mail message based on the senders, whereas the text message may be related to a phone call based on the location of the two.
  • a representation is rendered for the relevant interaction events.
  • the representation is rendered in the form of a graphic, for example, a cartoon image.
  • the rendering module 122 can generate the representation for the relevant interaction based on the query parameter and the context features not included in the query parameter.
  • the rendering module 122 can obtain graphics associated with the query parameter and the contexts features, and render the representation using the graphics.
  • the graphics obtained for rendering the representation for an interaction event can include images of a primary user operating the user devices 104 on which the interaction event is captured, one or more secondary users participating with the primary user in the interaction event, a location of occurrence of the interaction event, and the content of the interaction event.
  • a collage-type image of all the secondary users can be generated for use in the representation.
  • the graphics in the representation depicting a context feature, are linked to other graphics.
  • the rendering module 122 can render the representations for all the related interaction events and provides a list of the other representations. It will be understood that, in said example, the rendered representations of the related interaction events include the graphic which was clicked on.
  • the representation rendered by the rendering module 122 can also include a graphic depicting the various related interaction events.
  • the rendering module 122 can further depict the various representations for the relevant interaction events on a time-line.
  • the rendering module 122 can depict the various representations in an hour-wise, a day-wise, or a week-wise manner on the time-line.

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Library & Information Science (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

Une implémentation de la présente invention concerne un procédé de traitement d'événements d'interaction basé sur un contexte dans un réseau de communication. Dans ladite implémentation, le procédé consiste à extraire un ou plusieurs éléments de contexte pour chaque événement d'une pluralité d'événements d'interaction. Les éléments de contexte peuvent être extraits dans des informations d'interaction associées à la pluralité d'événements d'interaction. Par ailleurs, parmi la pluralité d'événements d'interaction, des événements d'interaction liés sont identifiés sur la base d'un paramètre d'interrogation. Le paramètre d'interrogation peut être basé sur un ou plusieurs des éléments de contexte. Sur la base de comparaisons entre les événements d'interaction liés, des événements d'interaction pertinents sont déterminés parmi les événements d'interaction liés. Par ailleurs, les événements d'interaction pertinents sont rendus pour illustrer sous forme de graphiques les événements d'interaction pertinents. Le rendu des événements d'interaction pertinents peut être basé sur les éléments de contexte et sur les comparaisons entre les événements d'interaction liés.
PCT/EP2012/066980 2011-09-20 2012-08-31 Traitement d'événements d'interaction basé sur un contexte WO2013041345A1 (fr)

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WO2016205007A1 (fr) * 2015-06-19 2016-12-22 Microsoft Technology Licensing, Llc Sélection d'événements en fonction d'une entrée d'utilisateur et d'un contexte courant
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