Robert W Schwanke

US Patent:

20030018508, Jan 23, 2003

Filed:

Jul 19, 2001

Appl. No.:

09/908953

Inventors:

Robert Schwanke - North Brunswick NJ, US

International Classification:

G06F017/60

US Classification:

705/009000

Abstract:

A data-triggered process definition language is employed, wherein each activity specified in a preferred process definition language is permitted to be enacted whenever a specified combination of data conditions is met, regardless of which activities have previously been enacted. A data-triggered workflow engine utilizes a current state of a process instance, the permitted and schedule rules, an activity network, and additional attributes of activities to schedule the enactment of activities. The activity network does not completely prescribe the enactment order, but rather controls what enactment order the data-triggered workflow engine will suggest to a participant. A participant, however, may select a different order based on other information, and can even enact activities that have not been scheduled.

US Patent:

53177410, May 31, 1994

Filed:

May 10, 1991

Appl. No.:

7/698635

Inventors:

Robert W. Schwanke - North Brunswick NJ

Assignee:

Siemens Corporate Research, Inc. - Princeton NJ

International Classification:

G06F 944
G06F 1540

US Classification:

395700

Abstract:

A method for identifying software objects that have been assigned to a wrong group, in which the similarity between objects is known, such as by evaluating a similarity function, comprises the steps of checking each object to see whether it belongs to its current group with K peers and confidence N, checking whether each object belongs to another group with a lower and therefore better confidence rating, and identifying as misclassified those objects having a lower confidence rating in said another group.

US Patent:

54386764, Aug 1, 1995

Filed:

Jun 4, 1993

Appl. No.:

8/072657

Inventors:

Robert W. Schwanke - North Brunswick NJ

Assignee:

Siemens Corporate Research, Inc. - Princeton NJ

International Classification:

G06F 944

US Classification:

395650

Abstract:

A method of reoptimizing the coefficients of a similarity function coefficient estimation as mavericks are resolved in a maverick analysis comprises computing initial weights for each feature and passing the similarity function to an estimation procedure, along with software objects, their group assignments, a peer parameter K and a confidence parameter N. Receiving as output and using updated values for the coefficients to obtain lists of misclassified and poor-confidence mavericks and placing them in a Current Maverick Set. Presenting the Current Maverick Set to an analyst to determine (1) if the maverick should be deferred and placed in the Deferred Maverick Set; or (2) if the maverick is assigned to a certain group it is removed from the Current Maverick Set and placed in the Firmly Assigned Set; or (3) if the input set of software objects should have certain features added to, or removed from them, or (4) if the similarity function coefficient estimation should be returned to the estimation procedure wherein this time, its inputs are: the original set of software objects less the members of the Deferred Maverick Set and the Current Maverick Set plus the members of the Firmly Assigned Set; the weights of the features and the coefficients previously used, which may be modified if need be; and the modified group assignments. Updated values for the coefficients are received, and when maverick resolution is complete, the reoptimizing stops.

US Patent:

54616980, Oct 24, 1995

Filed:

Jun 17, 1993

Appl. No.:

8/079687

Inventors:

Robert W. Schwanke - North Brunswick NJ
Stephen J. Hanson - Princeton NJ

Assignee:

Siemens Corporate Research, Inc. - Princeton NJ

International Classification:

G06F 1580
G06K 962

US Classification:

395 22

Abstract:

Given a set of objects (A, B, C,. . . ), each described by a set of attribute values, and given a classification of these objects into categories, a similarity function accounts well for this classification when only a small number of objects are not correctly classified. A method for modelling a similarity function using a neural network comprises the steps of: (a) inputting feature vectors to a raw input stage of a neural network respectively for object S in the given category, for other objects G in the same category being compared the object S, and for object B outside the given category; (b) coupling the raw inputs of feature vectors for S, G, and B to an input layer of the neural network performing respective set operations required for the similarity function so as to have a property of monotonicity; (c) coupling the input elements of the input layer to respective processing elements of an hidden layer of the neural network for computing similarity function results adaptively with different values of a coefficient w of the similarity function; (d) coupling the processing elements of the hidden layer to respective output elements of an output layer of the neural network for providing respective outputs of an error function measuring the extent to which object S is more similar to object G than to object B; and (e) obtaining an optimal coefficient w by back propagation through the neural network which minimizes the error outputs of the error function.

US Patent:

54856213, Jan 16, 1996

Filed:

Apr 6, 1994

Appl. No.:

8/224084

Inventors:

Robert W. Schwanke - North Brunswick NJ

Assignee:

Siemens Corporate Research, Inc. - Princeton NJ

International Classification:

G06F 944

US Classification:

395700

Abstract:

A method of using a group similarity measure, with an analyst, on a set containing a plurality of groups, the groups containing software objects, for providing a decision on which groups to combine, and wherein certain combined pairs are disapproved, comprises the following steps: (a) starting with a set of groups, each containing software objects, repeatedly identifying the two most similar groups according to the group similarity measure; (b) when an identified pair results, combining the identified pair by one of (A) merging the two groups and (B) making a supergroup containing the two groups as subgroups; and (c) stop repeating steps (a) and (b) when the current resulting set of groups is satisfactory.

US Patent:

54287880, Jun 27, 1995

Filed:

Jun 4, 1993

Appl. No.:

8/072656

Inventors:

Robert W. Schwanke - North Brunswick NJ

Assignee:

Siemens Corporate Research, Inc. - Princeton NJ

International Classification:

G06F 944

US Classification:

395700

Abstract:

In a software system, a method for computing the similarity between first and second software objects, comprises the steps of using a monotonic, matching, symmetric function of the common distinctive features and including a term to account for linking.