System and method for automated analysis of electronic travel data

1. A system for automatically analyzing contracts and travel data, the system comprising:

a database storing a plurality of document images of disparate contracts; and

a server equipped with one or more processors and in communication with the database, the server configured to execute an image processing module, an image segmentation module, a segment filtering module, classification module, an analysis module; and an optimization module, wherein the image processing module when executed:

removes noise from each of the plurality of document images;

aligns each of the plurality of document images; and

prepares each of the plurality of document images for optical character recognition (OCR);

wherein the image segmentation module when executed:

segments each of the plurality of document images into multiple defined segments, where the segments are smaller than a corresponding document image;

converts each of the defined segments into corresponding text blocks using OCR;

wherein the segment filtering module when executed:

identifies relevant segments by analyzing the corresponding text blocks and determining that the segment indicates a contract term;

wherein the classification module when executed:

executes a trained machine learning model on the relevant segments of each of the plurality of document images;

automatically classifies each of the plurality of document images into a contract category;

generates a contract category identifier corresponding to the contract category; and

transmits the contract term and the contract category identifier of each of the plurality of document images to a client device displaying a user interface;

wherein the analysis module when executed:

receives travel data and a data category identifier;

receives the contract term and the contract category identifier;

compares the contract term to the travel data if the data category identifier corresponds to the contract category identifier and generates a discrepancy value; and

wherein the optimization module when executed:

generates hypothetical travel data with a discrepancy value that is less that the discrepancy value for the contract term;

generates a hypothetical contract term with a discrepancy value that is less than the discrepancy value for the travel data; and

outputs the hypothetical travel data and the hypothetical contract term to a user device.

2. The system of claim 1 , wherein the trained machine learning model is a deep learning neural network model.

3. The system of claim 1 , wherein the trained machine learning model is a naïve B ayes classifier model.

4. The system of claim 1 , wherein the trained machine learning model is a natural language processing model.

5. The system of claim 1 , wherein the trained machine learning model is a tree-based classifier model.

6. The system of claim 1 , wherein the trained machine learning model is a logistic regression model.

7. The system of claim 1 , wherein the trained machine learning model is a support vector machine model.

8. The system of claim 1 , wherein one or more of the image processing module and optimization module when executed implements threshold calculation techniques.

9. The system of claim 1 , wherein one or more of the image processing module and optimization module when executed implements dilation and erosion techniques.

10. The system of claim 1 , wherein the segment filtering module when executed implements font-based segment filtering.

11. The system of claim 1 , wherein the image segmentation module when executed implements segmentation based on white space and line space in the document images.

12. The system of claim 1 , wherein the classification module further automatically classifies each of the document images into a sub-category.

13. A method for automatically analyzing contract and travel data, the method comprising:

receiving a plurality of document images of disparate contracts;

storing the plurality of document images in a database;

removing noise from each of the plurality of document images;

aligning each of the plurality of document images;

preparing each of the plurality of document images for optical character recognition (OCR);

segmenting each of the plurality of document images into multiple defined segments, where the segments are smaller than the corresponding document image;

converting each of the defined segments into corresponding text blocks using OCR;

identifying relevant segments by analyzing the corresponding text blocks and determining that the relevant segments indicate a contract term;

executing a trained machine learning model on the relevant segments of each of the plurality of document images;

automatically classifying each of the plurality of document images into a contract category;

generating a contract category identifier corresponding to the contract category;

transmitting the contract term and the contract category identifier of each of the plurality of documents images to a client device displaying a user interface

receiving travel data and a data category identifier;

determining if the data category identifier corresponds to the contract category identifier and comparing the travel data to the contact term to generate a discrepancy value; generating hypothetical travel data with a discrepancy value that is less that the discrepancy value for the contract term; generating a hypothetical contract term with a discrepancy value that is less than the discrepancy value for the travel data;

generating optimization information based on the contract terms and travel data; and

outputting the optimization information to the user device, wherein the optimization information includes the hypothetical travel data and the hypothetical contract term.

14. The method of claim 13 , wherein the optimization information includes travel data optimized to the contract terms.

15. The method of claim 13 , wherein the optimization information includes contract terms optimized to the travel data.

16. The method of claim 13 , wherein the trained machine learning model is a natural language processing model.

17. The method of claim 13 , wherein the contract category identifier identifies one or more of discount rate, route, airline, or ticket type.

18. The method of claim 13 , wherein the data category identifier identifies one or more of discount rate received, route, airline, ticket type, or number of tickets purchased.

19. The method of claim 13 , wherein the travel data is a set of discrete pieces of travel data wherein each piece has its own data category identifier and the contract terms are a set of discrete contract terms wherein each discrete term has its own contract category identifier.

20. A non-transitory machine-readable medium storing instructions executable by a processing device, wherein execution of the instructions causes the processing device to implement a method for automatically processing and classifying contract terms and travel data, the method comprising:

receiving a plurality of document images of disparate contracts;

storing the plurality of document images in a database;

removing noise from each of the plurality of document images;

aligning each of the plurality of document images;

preparing each of the plurality of document images for optical character recognition (OCR);

segmenting each of the plurality of document images into multiple defined segments, where the segments are smaller than the corresponding document image;

converting each of the defined segments into corresponding text blocks using OCR;

identifying relevant segments by analyzing the corresponding text blocks and determining that the relevant segments indicate a contract term;

executing a trained machine learning model on the relevant segments of each of the plurality of document images;

automatically classifying each of the plurality of document images into a contract category;

transmitting data relating to the classification of each of the plurality of document images to a client device displaying a user interface;

receiving input from the client device via the user interface indicating the classification of a document image of the plurality of document images is accurate or inaccurate;

transmitting the input as feedback to the trained machined learning model to retrain the machine learning model

generating a contract category identifier corresponding to the contract category;

receiving travel data and a data category identifier;

determining if the data category identifier corresponds to the contract category identifier and comparing the travel data to the contract term;

generating a discrepancy value based on the comparison of the travel data to the contract term;

generating hypothetical travel data and comparing it to the contract term;

generating a hypothetical travel discrepancy value;

outputting the hypothetical travel data if the hypothetical travel discrepancy value is less than the discrepancy value;

generating a hypothetical contract term and comparing it to the travel data;

generating a hypothetical contract discrepancy value;

generating optimization information;

outputting the hypothetical contract term if the hypothetical contract discrepancy value is less than the discrepancy value;

outputting optimization information wherein the optimization information includes the hypostatical travel data and the hypostatical contract term.