Method and system for feature interaction detection in a telecommunication network

• US 6,185,519 B1
• Filed: 12/01/1998
• Issued: 02/06/2001
• Est. Priority Date: 02/10/1998
• Status: Expired due to Fees

• Current Assignee: Hanger Solutions, LLC (IP Investments Group LLC)
• Original Assignee: Telcordia Technologies Incorporated (Telefonaktiebolaget LM Ericsson)
• Inventors: Liu, Hong, Lin, Fuchun Joseph, Ghosh, Abhrajit
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): CHOI, KYLE JAEHUN
• Application Number:
  US09/204,019
• Time in Patent Office: 798 Days
• Field of Search: 703/13, 703/22, 703/17, 703/21, 714/37, 714/38, 714/47, 370/242
• US Class Current: 703/21
• CPC Class Codes: H04Q 3/0041 involving techniques for avoiding interaction of call service features

Abstract

Methods and systems disclosed efficiently detect potential interactions between features in a telecommunications network. The methods and systems specify AIN (Advanced Intelligent Network) and switch-based features and detect their interactions when present within a feature package provided to a single subscriber. The methodology supports the assumption that each feature is created without the knowledge of other features, and that each feature is specified as a “black box,” i.e., nothing is known about its internal logic except its input/output behaviors. The invention models a call environment, models two or more features, and combines the call variable usage for each feature. Methods then compare the combined call variable usages to detect potential feature interactions. The invention assists a service mediator in the tasks of detecting potential interactions among AIN features provided by different third party service providers, and detecting potential interactions between a third party service provider'"'"'s AIN features and switch-based features.

Claims

1. A method for detecting feature interaction between a first Service Control Point (SCP) based feature and a second SCP-based feature in a call processing network, said method comprising the steps of:

using relational database models to model originating and terminating basic call models and to tie Transaction Capabilities Application Part (TCAP) messages to the originating and terminating basic call models;

modeling the first feature and the second feature based on TCAP messages and TCAP call variables wherein the resultant first and second feature models are distinct from said relational database models representing the basic call models;

determining from said first feature model a set of TCAP message sequences that can occur when executing the first feature and from said second feature model a set of TCAP message sequences that can occur when executing the second feature;

determining from said first feature model a call variable usage vector for each TCAP message sequence within the set of TCAP message sequences for the first feature and from said second feature model a call variable usage vector for each TCAP message sequence within the set of TCAP message sequences for the second feature, wherein the call variable usage vectors indicate how the corresponding TCAP message sequence uses call variables; and

detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the call variable usage vectors of the first feature against the call variable usage vectors of the second feature.

2. The method of claim 1, wherein the step of modeling the first and second features further includes the step of:

modeling the first and second features using a relational database.

3. The method of claim 1, wherein the call processing network is an AIN Release 0.1 network, and wherein the originating and terminating basic call models are

AIN Release 0.1 originating and terminating basic call models.

4. The method of claim 1, wherein the step of determining the set of TCAP message sequences for the first and second features further includes the step of:

reducing the number of TCAP message sequences by grouping the message sequences for each feature into equivalence classes based on an equivalence relation;

wherein the step of determining the call variable usage vectors for the first and second features further includes the steps of;

reducing the number of call variable usage vectors by determining one call variable usage vector for each of the equivalence classes of a given feature; and

combining the call variable usage vectors among each of the equivalence classes of a given feature into a single call variable usage vector; and

wherein the step of detecting feature interaction only requires;

detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the single combined call variable usage vector of the first feature against the single combined call variable usage vector of the second feature.

5. The method of claim 4 wherein the first feature is a switch-based feature,

wherein the step of modeling the first and second features requires modeling the first switch-based feature based only on TCAP call variables;

wherein the step of determining call variable usage vectors requires determining from said first switch-based feature model, call variable usage patterns for the first switch-based feature, wherein the call variable usage patterns indicate how the first switch-based feature uses TCAP variables and wherein the call variable usage patterns are independent of TCAP message sequences; and

wherein the step of detecting feature interaction requires detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the call variable usage patterns of the first switch-based feature against the single combined call variable usage vector of the second SCP-based feature.

6. The method of claim 5, wherein the step of modeling the first and second features includes modeling a set of features wherein said set of features includes said first and second features.

7. The method of claim 6, wherein the step of detecting feature interaction requires:

comparing the combined call variable usage vectors and/or call variable usage patterns in a pairwise manner.

8. A method for feature interaction detection in a call processing network, comprising the steps of:

modeling originating and terminating basic call models;

modeling a plurality of features based on TCAP messages wherein the resultant feature models are distinct from the models representing the basic call models;

generating call variable usage vectors for each of the plurality of features from the model of each feature wherein the call variable usage vectors for a given feature represent how that feature affects call variables;

reducing the number of call variable usage vectors for each of the plurality of features by eliminating redundant call data usage information through the use of equivalence classes; and

detecting feature interaction among the plurality of features by comparing, through use of the models representing the basic call models, the reduced number of call variable usage vectors of the plurality of features in a pairwise manner.

9. Computer-readable memory containing instructions for controlling a data processing system to perform a method for detecting feature interaction in call processing network, the method comprising the steps of:

modeling originating and terminating basic call models;

modeling a first feature and a second feature based on TCAP messages wherein the resultant feature models are distinct from the models representing the basic call models;

generating call variable usage vectors for the first feature from the first feature model wherein the call variable usage vectors represent how the feature affects call variables;

reducing the number of call variable usage vectors of the first feature by eliminating redundant call data usage information through the use of equivalence classes;

generating call variable usage vectors for the second feature from the second feature model wherein the call variable usage vectors represent how the feature affects call variables;

reducing the number of call variable usage vectors of the second feature by eliminating redundant call data usage information through the use of equivalence classes; and

comparing, through use of the models representing the basic call models, the reduced call variable usage vectors of the first feature to the reduced call variable usage vectors of the second feature to detect a feature interaction.

10. A system for detecting feature interaction between a first Service Control Point (SCP) based feature and a second SCP-based feature in a call processing network, said system comprising:

means for using relational database models to model originating and terminating basic call models and to tie Transaction Capabilities Application Part (TCAP) messages to the originating and terminating basic call models;

means for modeling the first feature and the second feature based on TCAP messages and TCAP call variables wherein the resultant first and second feature models are distinct from said relational database models representing the basic call models;

means for determining from said first feature model a set of TCAP message sequences that can occur when executing the first feature and from said second feature model a set of TCAP message sequences that can occur when executing the second feature;

means for determining from said first feature model a call variable usage vector for each TCAP message sequence within the set of TCAP message sequences for the first feature and from said second feature model a call variable usage vector for each TCAP message sequence within the set of TCAP message sequences for the second feature, wherein the call variable usage vectors indicate how the corresponding TCAP message sequence uses call variables; and

means for detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the call variable usage vectors of the first feature against the call variable usage vectors of the second feature.

11. The system of claim 10, wherein the means for determining the set of message sequences for the first and second features further includes:

means for reducing the number of TCAP message sequences by grouping the message sequences for each feature into equivalence classes based on an equivalence relation;

wherein the means for determining the call variable usage vectors for the first and second features farther includes;

means for reducing the number of call variable usage vectors by determining one call variable usage vector for each of the equivalence classes of a given feature; and

means for combining the call variable usage vectors among each of the equivalence classes of a given feature into a single call variable usage vector; and

wherein the means for detecting feature interaction only requires;

means for detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the single combined call variable usage vector of the first feature against the single combined call variable usage vector of the second feature.

12. The system of claim 11 wherein the first feature is a switch-based feature,

wherein the means for modeling the first and second features requires means for modeling the first switch-based feature based only on TCAP call variables;

wherein the means for determining call variable usage vectors requires means for determining from said first switch-based feature model, call variable usage patterns for the first switch-based feature, wherein the call variable usage patterns indicate how the first switch-based feature uses TCAP variables and wherein the call variable usage patterns are independent of TCAP message sequences; and

wherein the means for detecting feature interaction requires means for detecting feature interaction by comparing, through use of the relational database models representing the basic call models, the call variable usage patterns of the first switch-based feature against the single combined call variable usage vector of the second SCP-based feature.