Insight and algorithmic clustering for automated synthesis
First Claim
1. A decision support system, comprising:
- a user input port configured to receive user inputs, from a user, comprising at least one user criterion and at least one user input tuning parameter comprising a dimensionless quantitative variable representing a tradeoff preference of the user between at least two competing criteria;
an information repository configured to store a set of tagged data comprising a plurality of hidden dimensions;
a reference-user input port configured to receive, from at least one reference-user, at least one reference-user input parameter representing at least one analysis from the reference-user of at least a portion of the set of tagged data with respect to the received user inputs, wherein the at least one reference-user being distinct from the user; and
at least one processor, configured to;
define an adaptively optimized clustering distance function adapted independence on at least the at least one user criterion, the at least one user input tuning parameter, and the at least one reference-user input parameter; and
produce a clustered output of the at least portion of the tagged data in dependence on the at least one user criterion, the at least one user input tuning parameter, and the adaptively optimized clustering distance function, whereinthe clustered output having a number of dimensions less than a number of the plurality of hidden dimensions, andthe at least one user input tuning parameter impacts a cluster assignment of members of the set of tagged data according to the plurality of hidden dimensions by altering an application of the at least two competing criteria with respect to the adaptively optimized clustering distance function.
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Abstract
A decision support system and method, which receives user inputs comprising: at least one user criterion, and at least one user input tuning parameter representing user tradeoff preferences for producing an output; and selectively produces an output of tagged data from a clustered database in dependence on the at least one user criterion, the at least one user input tuning parameter, and a distance function; receives at least one reference-user input parameter representing the at least one reference-user'"'"'s analysis of the tagged data and the corresponding user inputs, to adapt the distance function in accordance with the reference-user inputs as a feedback signal; and clusters the database in dependence on at least the distance function, wherein the reference-user acts to optimize the distance function based on the user inputs and the output, and on at least one reference-user inference.
1089 Citations
20 Claims
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1. A decision support system, comprising:
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a user input port configured to receive user inputs, from a user, comprising at least one user criterion and at least one user input tuning parameter comprising a dimensionless quantitative variable representing a tradeoff preference of the user between at least two competing criteria; an information repository configured to store a set of tagged data comprising a plurality of hidden dimensions; a reference-user input port configured to receive, from at least one reference-user, at least one reference-user input parameter representing at least one analysis from the reference-user of at least a portion of the set of tagged data with respect to the received user inputs, wherein the at least one reference-user being distinct from the user; and at least one processor, configured to; define an adaptively optimized clustering distance function adapted independence on at least the at least one user criterion, the at least one user input tuning parameter, and the at least one reference-user input parameter; and produce a clustered output of the at least portion of the tagged data in dependence on the at least one user criterion, the at least one user input tuning parameter, and the adaptively optimized clustering distance function, wherein the clustered output having a number of dimensions less than a number of the plurality of hidden dimensions, and the at least one user input tuning parameter impacts a cluster assignment of members of the set of tagged data according to the plurality of hidden dimensions by altering an application of the at least two competing criteria with respect to the adaptively optimized clustering distance function. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A decision support method, comprising:
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receiving user inputs, from a user, comprising at least one user criterion, and at least one user input tuning parameter comprising a dimensionless quantitative variable representing tradeoff preferences of the user between at least two competing criteria; receiving at least one a reference-user input parameter, from at least one reference-user, representing at least one analysis from the reference-user of at least a portion of a set of tagged data comprising a plurality of hidden dimensions, with respect to the received user inputs, wherein the at least one reference-user being distinct from the user; defining an adaptively optimized clustering distance function adapted in dependence on at least the at least one user criterion, the at least one user input tuning parameter, and the at least one reference-user input parameter; and producing, with at least one automated processor, a clustered output of the at least portion of the set of tagged data in dependence on the at least one user criterion, the at least one user input tuning parameter, and the adaptively optimized clustering distance function, wherein the clustered output having a number of dimensions less than a number of the plurality of hidden dimensions of the set of tagged data, and the at least one user input tuning parameter impacts a cluster assignment of members of the set of tagged data according to the plurality of hidden dimensions by altering an application of the at least two competing criteria with respect to the adaptively optimized clustering distance function. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A decision support method, comprising:
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receiving a set of inputs from at least one user, the set comprising at least one user criterion, and at least one user input tuning parameter comprising a dimensionless quantitative variable representing tradeoff preferences of the at least one user between at least two independent criteria; selectively producing, by at least one automated processor, a clustered output of a set of tagged data comprising a plurality of hidden dimensions, in dependence on the set of inputs from the at least one user, and an adaptive clustering distance function adapted to define clusters of the set of tagged data having a number of dimensions less than a number of the plurality of hidden dimensions; receiving at least one reference-user input tuning parameter, from at least one reference-user, representing at least one analysis of the reference-user of the set of tagged data and the set of inputs from the at least one user; and adapting the adaptive clustering distance function in accordance with the reference-user input tuning parameter as a feedback signal, to optimize the adaptive clustering distance function based on the set of user inputs and the clustered output, wherein the at least one user input tuning parameter impacts a cluster assignment of members of the set of tagged data according to the plurality of hidden dimensions by altering an application of the at least two independent criteria with respect to the adaptive clustering distance function. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification