Systems and methods for exchanging information in a large group
First Claim
1. An electronically implemented method for actively exchanging messages in a group of individuals, comprising:
- a. accumulating messages from individuals in at least one hub comprising a processor;
b. processing with the processor and reorganizing accumulated messages and groups of accumulated messages with similar contents into short representations without or with insignificant information loss for said single messages or said groups; and
c. selectively distributing, by the processor, messages containing the short representations in place of accumulated messages according to a distribution pattern achieving a constrained maximum of a goodness function depending on the distribution pattern and parameters describing group members and the messages,wherein the goodness function is defined as a linear function, ƒ
(d)=Σ
X,Dε
G(X)PIX(D), subject to a linear constraint of a form Σ
X,Dε
G(X)qX(D)≦
r, where X identifies different individuals in the group, G(X) identifies a list of messages to be sent to individual X, D identifies one of the messages in the system, PIX(D) qx(D) are real valued coefficients depending on features of the message D and individual X, r is an acceptability constraint and the sum f(d) is over all individuals X and messages D in G(X) jointly andwherein the coefficient PIX(D) uses a probabilistic model of individuals'"'"' responses to messages, PIX(D)=1−
Π
Yε
A(D)Π
c(1−
RCD(c)VIXY(c)), where PIX(D) is identified with the probability that individual X will respond positively, in a certain sense, to message D, and where Y identifies different individuals in the group, c identifies different categories to which a message can be related, A(D) identifies a list of authors of message D, RCD(c) for all c identifies a set of features, valued between 0 and 1, describing the message D in relation to the set of categories c, and VIXY(c) for all Y and c is a set of features, valued between 0 and 1, describing the individual X in relation to the set of categories c and message authors Y.
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Abstract
Systems and methods are disclosed for actively all-to-all exchanging messages in a large group of individuals, comprising accumulating messages from individuals in at least one hub, processing and reorganizing accumulated messages and their groups into short representations of the messages, and selectively actively delivering short representations according to a distribution pattern found as a maximum of a goodness function. Processing and reorganizing accumulated messages into short representations, that is summarization, is performed by constraining the stream of messages to a hierarchical stream format, having a hierarchy of messages linked by a directional parent-child relationship, and iteratively replacing the parent messages in such hierarchy with a representative message chosen among all children of that message. Distribution pattern of the messages containing short representations is found by constrained maximization of a predetermined goodness function depending on the entire distribution pattern jointly for all messages and all users. The process of summarizing messages into short representations, and the process of calculating goodness function for distribution patterns are formulated in terms of statistical model of individuals'"'"' expertise and interests, which is learnt from observing past individuals'"'"' activity.
28 Citations
16 Claims
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1. An electronically implemented method for actively exchanging messages in a group of individuals, comprising:
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a. accumulating messages from individuals in at least one hub comprising a processor; b. processing with the processor and reorganizing accumulated messages and groups of accumulated messages with similar contents into short representations without or with insignificant information loss for said single messages or said groups; and c. selectively distributing, by the processor, messages containing the short representations in place of accumulated messages according to a distribution pattern achieving a constrained maximum of a goodness function depending on the distribution pattern and parameters describing group members and the messages, wherein the goodness function is defined as a linear function, ƒ
(d)=Σ
X,Dε
G(X)PIX(D), subject to a linear constraint of a form Σ
X,Dε
G(X)qX(D)≦
r, where X identifies different individuals in the group, G(X) identifies a list of messages to be sent to individual X, D identifies one of the messages in the system, PIX(D) qx(D) are real valued coefficients depending on features of the message D and individual X, r is an acceptability constraint and the sum f(d) is over all individuals X and messages D in G(X) jointly andwherein the coefficient PIX(D) uses a probabilistic model of individuals'"'"' responses to messages, PIX(D)=1−
Π
Yε
A(D)Π
c(1−
RCD(c)VIXY(c)), where PIX(D) is identified with the probability that individual X will respond positively, in a certain sense, to message D, and where Y identifies different individuals in the group, c identifies different categories to which a message can be related, A(D) identifies a list of authors of message D, RCD(c) for all c identifies a set of features, valued between 0 and 1, describing the message D in relation to the set of categories c, and VIXY(c) for all Y and c is a set of features, valued between 0 and 1, describing the individual X in relation to the set of categories c and message authors Y. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for distributing messages to individuals in a communication system, comprising
a. accumulating messages from individuals in at least one hub comprising a processor; -
b. processing with the processor and reorganizing accumulated messages and groups of accumulated messages with similar contents into short representations without or with insignificant information loss for said single messages or said groups; and c. distributing, by the processor, messages containing short representations in place of accumulates messages to individuals according to one or more distribution lists, each distribution list specifying which messages are to be delivered to each individual, wherein the one or more distribution lists are chosen by finding a maximum of a goodness function that depends on the distribution lists and the parameters describing the individuals and the messages, wherein the goodness function is defined as a linear function, ƒ
(d)=Σ
X,Dε
G(X)PIX(D), subject to a linear constraint of the form Σ
X,Dε
G(X)qX(D)≦
r, where X identifies different individuals in the group, G(X) identifies the list of messages to be sent to individual X, D identifies one of the messages in the system, PIx(D) qX(D) are real valued coefficients depending on the features of the message D and individual X, r is an acceptability constraint and the sum f(d) is over all individuals X and messages D in G(X) jointly and wherein the coefficient PIX(D) uses a probabilistic model of individuals'"'"' responses to messages,PIX(D)=1−
Π
Yε
A(D)Π
c(1−
RCD(c)VIXY(c)), where PIX(D) is identified with the probability that individual X will respond positively, in a certain sense, to message D, and where Y identifies different individuals in the group, c identifies different categories to which a message can be related, A(D) identifies a list of authors of message D, RCD(c) for all c identifies a set of features, valued between 0 and 1, describing the message D in relation to the set of categories c, and VIXY(c) for all Y and c is a set of features, valued between 0 and 1, describing the individual X in relation to the set of categories c and message authors Y. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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Specification