SYSTEM AND METHOD FOR ANALYZING MESSAGES IN A NETWORK OR ACROSS NETWORKS

• US 20120272160A1
• Filed: 02/23/2012
• Published: 10/25/2012
• Est. Priority Date: 02/23/2011
• Status: Active Grant

• Current Assignee: Blazent Incorporated (ServiceNow Incorporated)
• Original Assignee: Blazent Incorporated (ServiceNow Incorporated)
• Inventors: Spivack, Nova, ter Heide, Dominiek
• Granted Patent: US 9,614,807 B2
• Time in Patent Office: Days
• US Class Current: 715/752
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 43/16 Threshold monitoring
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Systems and methods for analyzing messages in a network or across networks are disclosed. In one aspect, embodiments of the present disclosure include a method, for detecting trends from a set of messages in a network or across networks, identifying, from the set of messages in the network or across networks, commonly or frequently occurring topics, computing statistical attributes for the commonly or frequently occurring topics in the set of messages that indicate respective levels of trendiness, and/or presenting, the commonly or frequently occurring topics as indicators in a user interface, the indicators being actionable to access additional information relating to a selected topic via the action. The messages include messages interacted with by humans or machines and interactions can include, posted a message, shared a message, liked a message, commented on a message, replied to a message, viewed a message, saved or bookmarked a message.

Claims

1. A method for analyzing a stream of incoming messages from online media services for a user, the method, comprising:

determining the interests of the user from online activity of the user;

wherein, the online activity of the user is automatically detected from those activities of the user on or via the online media services without requiring additional interaction or input from the user;

increasing visibility of those incoming messages which are more interesting to the user among other incoming messages in the stream for presentation in a user interface;

wherein, the user interface is a part of a platform which is independent of any of the online media services.

2. The method of claim 1, further comprising,

generating personalization indicators for each of the incoming messages in the stream received from the online media services;

wherein, the personalization indicators for the incoming messages correspond to relevancy of the message to interests of the user;

wherein, those incoming messages which are more interesting to the user are determined using the personalization indicators.

3. The method of claim 2, further comprising,

customizing an interactive graphical visualization of concepts or topics contained in the messages for the user, with each concept or topic is represented by a label which is arranged radially from a node,

wherein, further information related to the represented concept or topic is depicted in the interactive graphical visualization upon detected interaction with the label.

4. The method of claim 3, wherein, wherein, graphical characteristics of the label or edges connecting the label to the node are adjusted based on a level of interest of the user in the concept or topic determined using the personalization indicators.

5. The method of claim 1, wherein, the interests of the user are automatically determined upon receipt of user credentials to one of the online media services.

6. The method of claim 1, wherein, the online activity of the user includes user interaction with content on or via the online media services.

7. The method of claim 6, wherein, the content includes one or more of, user profiles, user events, user likes or dislikes, status updates, mentions, feeds, tweets, links, notes, video content, audio content.

8. The method of claim 6, wherein, the content includes one or more of, photos, web pages, documents, email messages, comments, chat messages.

9. The method of claim 1, wherein, the interests are determined by analyzing user content provided in the online activity at or via the online media services, the user content including user-submitted content or user-generated content.

10. The method of claim 1, wherein, the interests of the user are determined from other users with whom the user is connected or is friends in the online media services.

11. The method of claim 1, wherein, the interests of the user are determined from other users with whom the user interacts in the online media services.

12. The method of claim 1, wherein, the interests of the user are represented by concepts weighted according to analysis of user content which is subject of the online activities at the online media services.

13. The method of claim 12, wherein, the user content which is subject of the online activities includes, one or more of, user liked content, user disliked content.

14. The method of claim 12, wherein, the user content which is subject of the online activities includes, one or more of, user status updates, user posted content, user shared content, saved content, content favorited by the user, and user tweets.

15. A method for presenting an information stream of messages from a social networking service for a user, the method comprising:

assigning a personalization indicator to a message from the social networking service to be presented in the information stream for the user;

using the personalization indicator to determine visual placement of the message among the messages in the information stream;

wherein, the personalization indicator for the message corresponds to relevancy of the message to interests of the user.

16. The method of claim 15, further comprising:

generating personalization indicators for each of the messages in the information stream;

wherein, the messages are received from multiple social networking services;

using the personalization indicators to increase visibility of messages which are more interesting to the user among the messages in the information stream.

17. The method of claim 15, further comprising, presenting the message among the messages in the information stream in a platform that is independent of the social networking service.

18. The method of claim 15, wherein, the personalization indicator is assigned to the message by performing one or more of:

comparing message content against the interests of the user;

identifying connections of the user who have interacted with the message;

identifying connections of the user who have interacted with content from a URL or URL fragment associated with the message.

19. The method of claim 18 wherein the identifying the connections of the user who have interacted with the message includes, one or more of, posted the message, shared the message, liked the message, favorited the message, tagged a message, annotated a message, rated a message, and commented on the message.

20. The method of claim 18, wherein the identifying the connections of the user who have interacted with the message includes, one or more of, replied to the message, viewed the message, saved or bookmarked the message.

21. The method of claim 17, wherein, the platform is web-based.

22. The method of claim 17, wherein, the platform is a desktop application.

23. The method of claim 17, wherein, the platform is a mobile application.

24. The method of claim 15, wherein, the personalization indicator includes a quantitative score or a qualitative indicator.

25. The method of claim 15, further comprising,

determining the interests of the user from online activity of the user;

wherein, the online activity of the user is detected from the social networking service;

wherein, the online activity of the user includes user interaction with content on or via the social networking service.

26. The method of claim 25, wherein, the content includes one or more of, user profiles, user events, user likes or dislikes, status updates, mentions, feeds, tweets, links, notes, video content, audio content, photos, web pages, documents, email messages, comments, chat messages.

27. The method of claim 25, further comprising:

determining the interests by analyzing user content provided in the online activity at or via the social networking service,

wherein, the user content includes user-submitted content or user-generated content.

28. The method of claim 27, wherein, the user content which is subject of the online activities includes, one or more of, user status updates, user posted content, user shared content, saved content, content favorited by the user, and user tweets.

29. The method of claim 27, further comprising:

determining the interests by analyzing user content provided during the online activity by identifying concepts from the user content using keywords that occur in the user content.

30. The method of claim 27, further comprising:

generating weighted concepts by determining frequencies with which keywords occur in the user content;

wherein, concepts corresponding to more frequently occurring keywords are assigned higher weights.

31. The method of claim 30, wherein, keywords include single words, a string of words, or a phrase.

32. The method of claim 30, wherein, keywords include, one or more of, a tag, a username, or a name.

33. The method of claim 30, wherein, keywords include, a resource identifier including a URL or URI.

34. The method of claim 30, further comprising, creating an interest profile for the user using the weighted concepts to represent relative strength of each of the interests of the user.

35. The method of claim 30, wherein, the concepts are identified from the user content through natural language processing.

36. The method of claim 30, wherein, the concepts are identified from the user content through natural language processing including identifying social network tokens.

37. The method of claim 36, wherein, the social network tokens identified in the natural language processing include one or more of hash-tags, URLs, user names, and emoticons.

38. The method of claim 35, wherein, nouns, pronouns, and unknown words are identified for the natural language processing.

39. The method of claim 30, wherein, the concepts are detected through disambiguation of a given keyword having multiple meanings.

40. The method of claim 39, wherein, the disambiguation of the given keyword includes:

compiling concept category occurrences of the keywords collected for the user;

assigning scores to each of the multiple meanings of the given keyword based on occurrence frequency of the concept categories of the keywords collected for the user;

using the scores to select a meaning of the given keyword for use in identifying the interests of the user.

41. A method for detecting trends from a set of messages in a network or across networks, the method, comprising:

identifying, from the set of messages in the network or across networks, commonly or frequently occurring topics;

computing statistical attributes for the commonly or frequently occurring topics in the set of messages that indicate respective levels of trendiness;

presenting, the commonly or frequently occurring topics as indicators in a user interface, the indicators being actionable to access additional information relating to a selected topic via the action.

42. The method of claim 41, wherein, the indicators are depicted in a radial arrangement from a common node;

wherein a radial distance from the common node varies based on relevance to a facet represented by the common node using the statistical attributes.

43. The method of claim 41, wherein, the visual characteristics of the indicators correspond to the levels of trendiness of the corresponding topic in the commonly or frequently occurring topics.

44. The method of claim 41, wherein, the commonly or frequently occurring topics are identified from the set of messages via natural language processing of the set of messages.

45. The method of claim 41, wherein, the set of messages are associated with a given user, and the levels of trendiness computed correspond to levels of interest of the given in the commonly or frequently occurring topics.

46. The method of claim 41, wherein, the set of messages include one or more of, user profiles, user events, user likes or dislikes, status updates, mentions, feeds, tweets, messages associated links, notes, messages associated with video content, messages associated audio content, messages associated photos, web pages, documents, email messages, comments, chat messages.

47. The method of claim 41, wherein, the set of messages include one or more of, messages associated with video content, messages associated audio content, and messages associated photos.

48. The method of claim 41, wherein, the set of messages include messages interacted with by humans or machines.

49. The method of claim 48, wherein, the interaction includes one or more of, one or more of, posted a message, shared a message, liked a message, commented on a message, replied to a message, viewed a message, saved or bookmarked a message.

50. The method of claim 41, wherein, the commonly or frequently occurring topics are identified through natural language processing including one or more of:

identifying social network tokens;

identifying nouns, pronouns or unknown words in the set of messages.

51. The method of claim 50, wherein, the natural language tokens include one or more of hashtags, @ tags, + tags, URLs, user names, and emoticons.

52. The method of claim 41, wherein, the commonly or frequently occurring topics are identified through disambiguation of a given keyword in the set of messages, which has multiple meanings.

53. The method of claim 41, wherein, the set of messages include messages with different destinations and different origins.

54. The method of claim 41, wherein, the user interface is a part of a platform which is independent of any of the destination sites and any of the different origins.

55. The method of claim 53, wherein a destination site includes one or more of, a social media network and a user.

56. The method of claim 53, wherein an origin site includes one or more of, a social media network and a user.

57. The method of claim 53, wherein, the different origins include users and online service platforms.

58. The method of claim 41, wherein, the set of messages originate from one source.

59. The method of claim 41, wherein, the set of messages originate from one social media network.

60. The method of claim 41, wherein, the set of messages originate from multiple sources including multiple social media networks.

61. The method of claim 41, wherein, the set of messages are directed to multiple destinations including multiple social media networks.

62. A machine-readable medium having stored thereon instructions which when executed cause a processor to perform a method for aggregating an information stream of content from a content sharing service, the method, comprising:

determining the interests of a user from online activity of the user with respect to the content sharing service;

identifying meanings or context surrounding content to be presented among the information stream for the user;

using the meanings or the context surrounding the content in relation to the interests of the user, to increase or decrease visibility of the content among other pieces of content in the information stream to be presented to the user.

63. The method of claim 62, wherein, the online activity includes web-page browsing detected by a browser extension;

wherein, the browser extension analyzes the web-page;

extracts metadata; and

provides the metadata to a repository for future use in personalizing streams for the user or other users.

64. The method of claim 62,

wherein, the visibility of the content is increased or decreased for presentation in a user interface component of a third-party platform independent from the content sharing service;

wherein, the interests of the user is also determined by the platform; and

wherein, the meanings or the context surrounding the content is also determined by the third-party platform.

65. The method of claim 62, wherein, the online activity of the user is from activity on the content sharing service.

66. The method of claim 62, wherein, the online activity is further determined from activity of the user on other content sharing services.

67. The method of claim 62, wherein, the meaning or context surrounding the content is determined from associated metadata.

68. The method of claim 67, wherein, the associated metadata identifies an ontology type of the content.

69. The method of claim 68, wherein an ontology type of the content includes, a message content type, a media content type, a marketplace content type, or a knowledge content type.

70. The method of claim 68, wherein, the content identified includes one or more of, video content, audio content, news, breaking news, popular content, content relating to certain people, opinions, business news, sports news and technology news.

71. The method of claim 69, wherein, the message content type identified includes, one or more of, an event, humor, an invitation, news, a note, a notification, and a status.

72. The method of claim 69, wherein, the media content type identified includes, one or more of animation, audio, collections, documents, a game, a map, a picture, publication, software, video and websites.

73. The method of claim 69, wherein, the marketplace content type identified includes, one or more of, advertisements, offers, wanted, products, and services.

74. The method of claim 69, wherein, the knowledge content type identified includes one or more of, a factoid, a how-to, an opinion, a Q &

A, and a quotation.

75. The method of claim 67, wherein, the associated metadata include geolocation information.

76. The method of claim 67, wherein, the associated metadata includes manual annotations contributed by other users.

77. The method of claim 76, wherein, the other users are users of the third-party platform or of the content sharing service.

78. The method of claim 76, wherein, the manual annotations for the content are submitted as a result of the prompting of the other users to provide the annotations by the third-party platform in detecting interaction of the other users with the content.

79. The method of claim 67, wherein, the associated metadata is machine generated in accordance with a set of rules for the content.

80. The method of claim 79, wherein, the set of rules applies to a message associated with the content, including criteria based on one or more of, a pattern of the message, a keyword contained in the message, a regular expression contained in the message and a string contained in the message.

81. The method of claim 79, wherein, the set of rules includes criteria based on one or more of, a source of the content as identified by a URL or URL fragment, an author or poster of the content.

82. The method of claim 76, further comprising:

automatically deriving annotation rules from the manual annotations;

automatically annotating messages using the annotation rules;

optimizing and revising the annotation rules over time.

83. A system for filtering incoming messages from online media services for a user into an information stream, the system comprising:

means for, assigning a personalization indicator to an incoming message to be presented in the information stream for the user, the user having an interest profile indicating the interests of the user based on online activity of the user;

means for, comparing message content of the incoming messages to the interest profile to assign the personalization indicators;

means for, using the personalization indicators to filter and prioritize the incoming messages into the information stream to be presented to the user.

84. The system of claim 83, further comprising,

means for, extracting keywords representing concepts from the message content;

means for, using the keywords for comparison with the interest profile in comparing the message content with the interest profile.

85. The system of claim 83, further comprising,

means for, determining frequencies of occurrence of the keywords in the interest profile;

means for, computing the personalization indicators based on the frequencies of occurrence.

86. The system of claim 83, further comprising,

means for, identifying weighting of interests corresponding to the keywords occurring in the interest profile;

means for, computing the personalization indicators of the incoming messages based on the weightings.

87. The system of claim 83, wherein, the message content of the incoming messages is determined through natural language processing.

88. A system for processing incoming messages from multiple web-based media services, the system, comprising:

a processing unit;

a memory unit having stored thereon instructions which when executed by the processing unit, cause the processing unit to;

perform an action on an incoming message in accordance with a rule set to process the incoming messages,

wherein the action is performed by an independent service, the independent service being independent from the multiple web-based media services.

89. The system of claim 88, wherein, the action includes generating a personalized data stream for a user in accordance with the rule set to filter and prioritize incoming messages for the user.

90. The system of claim 89, wherein, the personalized data stream includes a likestream comprised of messages implicitly or explicitly liked by the user as defined by the rule set.

91. The system of claim 90, wherein, the action performed on likestream includes:

categorizing the messages contained therein by message type;

visually presenting the messages in the likestream by category to the user in a user interface provided by the independent service.

92. The system of claim 90, wherein, the messages in the likestream are visually presented in user interface of the independent service in chronological order or based on degree to which they are liked.

93. The system of claim 90, wherein, other users are able to subscribe to the likestream of the user.

94. The system of claim 88, wherein the rule set is specified by the user, via a user interface provided by the service which is independent of the multiple web-based media services.

95. The system of claim 88, wherein the rule set is determined as a result of machine learning from observance of user behavior and tracking user preferences.

96. The system of claim 88, wherein, the rule set includes, type of interaction with a message, including one or more of, posted, replied to, reposted, received, liked, disliked, annotated, read, saved, favorited, and tagged.

97. The system of claim 88, wherein, the rule set includes, people parameters of people who interacted with a message, including, specific people, connected people, followed people, following people, people who follow specified other people, people with a certain social influence level, geographical parameters of the people, membership or group affiliation.

98. The system of claim 97, wherein, the people parameters further include, screen name of an author, author follower count, author following count, author average messages per day.

99. The system of claim 88, wherein, the rule set includes, message parameters including, one or more of, message type, topics of a message, words in a message, tags in a message, message importance, level of interest of message to the user, relevance of message source, repost score, popularity, influence of author.

100. The system of claim 88, wherein, the rule set further includes, number of reposts.

101. The system of claim 88, wherein, the rule set includes, timing or geographical parameters of a message.

102. The system of claim 88, wherein the action is specified by the user, via a user interface provided by the service which is independent of the multiple web-based media services.

103. The system of claim 88, wherein the action is determined as a result of machine learning from observance of user behavior and tracking user preferences.

104. The system of claim 88, wherein, the action includes adjusting a user interface experience of the incoming message including one or more of, adjusting visibility or audibility of the message.

105. The system of claim 88, wherein, the action includes one or more of, annotate or tag the message, repost the message, delete the message, hide the message, file the message, reply to the message,

106. The system of claim 88, wherein, the action includes one or more of, save the message, add the message to my interests, add the author of the message to my interests, share the message.

107. The system of claim 88, wherein, the action includes, one or more of, launch a plug-in or another application, call a function, send the message to another application, export the message as data or export the message into another format, view the message in a specific viewer.

108. The system of claim 88, wherein, the action includes one or more of, translate the message, change the personalization score of the message.

109. The system of claim 88, wherein, instructions which when executed by the processing unit, further cause the processing unit to:

identify the interests of the user from online activity or online relationships of the user;

increase visibility of those incoming messages which are more interesting to the user among other incoming messages in the personalized data stream for presentation, in a user interface provided by the independent service.

110. The system of claim 88, wherein, the online activity of the user is automatically detected from those activities of the user on or via the multiple web-based media services.

111. The system of claim 88, wherein, the online activity of the user is automatically detected from those activities of the user with online content.

112. The system of claim 88, wherein, the online activity of the user is automatically detected from interactions or relationships of the user with other users in any of the multiple web-based media services.

113. The system of claim 88, wherein, the user interface is a part of a platform which is independent of any of the online media services.

114. A method for generating a layout of a stream of messages from a social networking service, the method comprising:

identify the interests of a user from online activity or online relationships of the user;

generating personalization indicators for each of the messages in the information stream;

wherein, the personalization indicators for the messages correspond to relevance to the interests of the user.

creating a graphical visualization of concepts or topics contained in the messages for the user, with each concept or topic is represented by a label which is arranged radially from a node.

115. The method of claim 114, wherein, the graphical visualization is interactive, wherein, responsive to detection of selection or activation of the label, information related to the represented concept or topic is further depicted in the graphical visualization

116. The method of claim 115, wherein, the information related includes, one or more of, related topics, related tags, or related keywords.

117. The method of claim 114, wherein, a radial distance of the label with the node is determined by a level of interest of the user in the concept or topic.

118. The method of claim 114, wherein, graphical characteristics of the label or edges connecting the label to the node are adjusted based on a level of interest of the user in the concept or topic.

119. The method of claim 114, further comprising, updating the graphical visualization continuously in accordance with an adjustable timeframe.

120. The method of claim 114, wherein, labels representing each concept or topic corresponds are sized according to frequency or number of occurrence of the concept or topic.

121. The method of claim 114, wherein, the visualization, is created by a platform independent of the social networking service

122. The method of claim 114, wherein, the messages are received from multiple social networking services

123. The method of claim 114, further comprising, using the personalization indicators to increase visibility of messages which are more interesting to the user among the messages in the information stream.

124. The method of claim 114, wherein, the messages are received from multiple social networking services