DESC:
[0024] In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practiced is shown by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that the logical, mechanical and other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense.
[0025] The embodiments of the present invention discloses a system and method for automated ticket modification and cancellation which guarantees penalty costs for air ticket refunds/changes and free one-click air ticket refund/ticket
[0026] Accordingly, the disclosed system and method guarantees single click cancellations and automated ticket change. Combined with the proprietary API with the horizontal/vertical Ecommerce providers and others to sell air travel in a seamless manner by automating the complex post ticketing services.
[0027] The disclosed system and method employs Machine Learning (ML) and Natural Language Processing (NLP) techniques, which further can extract the change and cancellation Fees from the unstructured Category-16 documents using these ML and NLP methods. The proprietary algorithms further run over millions of such air tickets, at least from 200 airlines and provide appropriate structure to these penalty data, which can then be consumed by the OTAs and Travel intermediaries as APIs (Application Programming Interface). These algorithms consider complex fare rules such as Flight applicability, Minimum Stay, Maximum stay, Blackout dates, Child and Infant fees and provide robust and accurate datasets to calculate applicable Fees and Change options.
[0028] FIG.1 illustrates a schematic diagram of the system for automated ticket change and cancellation according to an embodiment of the present invention. Accordingly, the system for automated air ticket cancellation and modification (100) comprises of one or more client input devices (104a – 104c); a server computer (103); a natural language processor (NLP) module (103a) located in the server computer (103); a machine learning (ML)module (103b) located in the server computer (103); one or more heterogeneous data servers (101a – 101d) which comprises of heterogeneous data related to air ticket booking, category-16 documents, penalty data etc.; wherein, the client input device (104a – 104c) provides input data to the server computer (103) for cancellation or modification of the air tickets and the server computer (103) processes these received data with the natural language processor module (103a) and the machine learning module (103b) at the server computer (103) extract the change and cancellation fees from the unstructured Category-16 documents spread over one or more heterogeneous data servers (101a – 101-d) using one or more ML and NLP methods and determine the structured fees and the minimalistic airline cancellation charges that can be charged to the end-user or the client.
[0029] The natural language processor (NLP) module (103a) and the machine learning (ML)module (103b) of the server computer (103) executes one or proprietary methods which in turn analyses one or more such air tickets, at least from 200 airlines and provide appropriate structure to these penalty data, which can then be consumed by the OTAs and travel intermediaries as an APIs (Application Programming Interface) for determining the minimalistic airline cancellation charges that can be charged to the end-user or the client. The server computer (103) analyses the retrieved air fare rules such as flight applicability, minimum stay, maximum stay, blackout dates, child and infant fees and provide robust and accurate datasets from one or more heterogeneous data servers (101a – 101d) using natural language processor (NLP) module (103a) and the machine learning (ML) module (103b) and calculates the applicable fees and change options.
[0030] FIG.2 illustrates a schematic diagram of the method for automated ticket change and cancellation which guarantees penalty costs for air ticket refunds/changes and free one-click air ticket refund/ticket according to an embodiment of the present invention. Accordingly, the method comprises of querying and retrieving flight ticket details form the airline/GDS (201); querying and retrieving Category-16 and other related documents from one or more sources (202); the Natural Language Processing Modules and the Machine Learning Modules processing the received data and determining the structured fees and the minimalistic airline cancellation charges (203); and the Structured change fees and the minimalistic cancellation charges are displayed for the users (204).
[0031] Here, querying and retrieving flight ticket details form the airline/GDS and querying and retrieving Category-16 and other related documents from one or more sources include fetching data from one or more heterogeneous data serves spread across various networks globally.
[0032] FIG.3 illustrates a the method of penalty fees extraction from Category -16 documents using NLP (Natural Language Processing) according to an embodiment of the present invention. Accordingly, the method of penalty fees extraction from Category -16 documents using NLP (Natural Language Processing) (300) comprises steps of
a. providing necessary details to the GDS or Airlines through an API that are required to issue the tickets (301).
b. each ticket is attributed with fare rule(s) (Category 16) document and the system takes these documents as input for the step-3/step-c (302)
c. one or more of the following NLP classes are executed on the category-16 documents to parse the data from the category -16 documents (303).
d. the retrieved information such as penalty fees are structured as scalable datasets in a database (304).
[0033] Further, one or more NLP classes that are executed on the category-16 documents to parse the data from the category -16 documents (304) are such as
a. Import re
b. Import flask
c. Import json
d. Import request

These methods, parse the data from the category -16 documents and other such documents and the retrieved information is processed in determining the structured fees and the minimalistic airline cancellation charges (203) and hence the structured change fees and the minimalistic cancellation charges are displayed for the users (204).
[0034] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modifying and/or adapting for various applications, such specific embodiments, without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.
[0035] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims. ,CLAIMS:Claims:
I/We Claim:
1. A system for automated air ticket cancellation and modification (100), the system comprising:
one or more client input devices (104a – 104c);
a server computer (103);
a natural language processor (NLP) module (103a) located in the server computer (103);
a machine learning (ML)module (103b) located in the server computer (103);
one or more heterogeneous data servers (101a – 101d) comprising heterogeneous data related to air ticket booking, category-16 documents, penalty data etc.;
wherein the client input device (104a – 104c) provides input data to the server computer (103) for cancellation or modification of the air tickets and the server computer (103) processes these received data with the natural language processor module (103a) and the machine learning module (103b) at the server computer (103) extract the change and cancellation fees from the unstructured Category-16 documents spread over one or more heterogeneous data servers (101a – 101-d) using one or more ML and NLP methods and determine the structured fees and the minimalistic airline cancellation charges that can be charged to the end-user or the client.

2. The system of claims 1, wherein the natural language processor (NLP) module (103a) and the machine learning (ML)module (103b) of the server computer (103) executed one or proprietary methods which in turn analyses one or more such air tickets, at least from 200 airlines and provide appropriate structure to these penalty data, which can then be consumed by the OTAs and travel intermediaries as an APIs (Application Programming Interface) for determining the minimalistic airline cancellation charges that can be charged to the end-user or the client.
3. The system of claims 1, wherein the server computer (103) analyses the retrieved air fare rules such as flight applicability, minimum stay, maximum stay, blackout dates, child and infant fees and provide robust and accurate datasets from one or more heterogeneous data servers (101a – 101d) using natural language processor (NLP) module (103a) and the machine learning (ML)module (103b) and calculates the applicable fees and change options.
4. The system of claims 1, wherein the system (100) assures the end-user/client of minimalistic penalty costs and ticket refunds/modification and free once click air ticket refund.
5. A method for automated air ticket cancellation and modification, the method comprising the steps of:
querying and retrieving flight ticket details form the airline/GDS (201);
querying and retrieving Category-16 and other related documents from one or more sources (202);
the Natural Language Processing Modules and the Machine Learning Modules processes the received data and determine the structured fees and the minimalistic airline cancellation charges (203);
the Structured change fees and the minimalistic cancellation charges are displayed for the users (204);

6. The method of claim 5, wherein querying and retrieving flight ticket details form the airline/GDS and querying and retrieving Category-16 and other related documents from one or more sources include fetching data from one or more heterogeneous data serves.
7. The method of claim 5, wherein the method of penalty fees extraction from Category -16 documents using NLP (Natural Language Processing) (300) comprises steps of:
a. provide necessary details to the GDS or Airlines through an API that are required to issue the tickets (301).
b. each ticket is attributed with fare rule(s) (Category 16) document and the system takes these documents as input for the step-3/step-c (302).
c. one or more of the following NLP classes are executed on the category-16 documents to parse the data from the category -16 documents (303).
d. the retrieved information such as penalty fees are structured as scalable datasets in a database (304).
8. The method of claim 7, wherein one or more NLP classes that are executed on the category-16 documents to parse the data from the category -16 documents (304) are such as
a. Import re
b. Import flask
c. Import json
d. Import request