Various systems, mediums, and methods to process requests involve retrieving a first pattern detection model that is configured to detect a plurality of first patterns based on features of the first patterns. Processing the requests also includes retrieving a plurality of second patterns and selecting a plurality of second features for detecting the plurality of second patterns. Processing the requests further includes generating a second pattern detection model by incorporating one or more of the second feature into the first pattern detection model to create an interim pattern detection model and to train the interim model by second patterns. Then a request having a request pattern is received and based on the second detection model a pattern detection score is generated for the received request. The request is processed based on the detection score. In some embodiments, the user activity patterns are associated with the requests.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a non-transitory memory storing at least a first and a second database; and one or more hardware processors configured to execute instructions to cause the system to perform operations comprising: retrieving, from the first database, a first pattern detection model, wherein the first pattern detection model is configured to detect a plurality of first patterns, and wherein the first pattern detection model comprises a plurality of first features for detecting the plurality of first patterns; retrieving, from the second database, a plurality of second patterns; selecting, based at least on the plurality of second patterns, a plurality of second features for detecting the plurality of second patterns; generating a second pattern detection model based at least on the first pattern detection model, wherein the generating comprises: incorporating one or more of the plurality of second features into the first pattern detection model to create at least a partially trained interim pattern detection model, wherein the second pattern detection model comprises one or more features that do not exist in the first pattern detection model; and training the partially trained interim pattern detection model using the plurality of second patterns to generate the second pattern detection model; receiving a request that includes a request pattern; applying the second pattern detection model to the request pattern to generate a pattern detection score for received request pattern; and processing the received request based on the pattern detection score. 2. The system of claim 1, wherein the operations further comprises: receiving the request through a network interface module and a network from a client device of a user; extracting one or more features of the received request pattern; generating, based at least on the extracted one or more features and the second pattern detection model, the detection score corresponding to the received request; classifying, based at least on the detection score, the received request into one of a plurality of groups; and processing the received request based on the detection score, the classification, or both. 3. The system of claim 2, wherein the client device is a mobile device communicating with the system through a wireless network. 4. The system of claim 2, wherein based at least on the detection score, the received request is classified into a fraudulent request group and the request is not processed. 5. The system of claim 1, wherein the first pattern detection model and the second pattern detection model are models for detecting transaction fraud patterns, and wherein the plurality of first patterns and the plurality of second patterns are transaction patterns. 6. The system of claim 1, wherein generating the second pattern detection model further comprises removing one or more of the plurality of first features. 7. The system of claim 6, wherein a first number of the features of the first pattern detection model and a second number of the features of the second pattern detection model are within five percent of each other. 8. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: retrieving a first pattern detection model, wherein the first pattern detection model is configured to detect a plurality of first patterns; retrieving a plurality of second patterns; generating a second pattern detection model based at least on the first pattern detection model, wherein the generating includes re-training the second pattern detection model using the plurality of second patterns; receiving a request that includes a request pattern via a network interface module and a network from a client device of a user; applying the second pattern detection model to the request pattern to generate a pattern detection score for received request pattern; and processing the received request based on the pattern detection score. 9. The non-transitory machine-readable medium of claim 8, wherein the first pattern detection model comprises a plurality of first features for detecting the plurality of first patterns; wherein the operations further comprise: selecting, based at least on the plurality of second patterns, a plurality of second features for detecting the plurality of second patterns; and wherein the generating further includes: prior to the re-training, incorporating one or more of the plurality of second features into the first pattern detection model to create a partially trained interim pattern detection model, wherein the second pattern detection model comprises one or more features that does not exist in the first pattern detection model. 10. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise: extracting one or more features of the received request pattern; generating, based at least on the extracted one or more features and the second pattern detection model, the detection score corresponding to the received request; classifying, based at least on the detection score, the received request into one of a plurality of groups; and processing the received request based on the detection score, the classification, or both. 11. The non-transitory machine-readable medium of claim 8, wherein the client device is a mobile device communicating with the machine through a wireless network. 12. The non-transitory machine-readable medium of claim 8, wherein the operations are performed by a pattern processing engine executing on the machine, and wherein the first pattern detection model and the second pattern detection model are implemented by the pattern processing engine as neural networks. 13. The non-transitory machine-readable medium of claim 8, wherein the first pattern detection model and the second pattern detection model are models for detecting transaction fraud patterns, and wherein the plurality of first patterns and the plurality of second patterns are transaction patterns. 14. The non-transitory machine-readable medium of claim 8, wherein the plurality of first patterns and the plurality of second patterns are patterns of user activities interacting a network-based system. 15. A method of updating a pattern detection model, comprising: retrieving, by a pattern processing engine executing on a server, a first pattern detection model, wherein the first pattern detection model is configured to detect a plurality of first patterns, and wherein the first pattern detection model comprises a plurality of first features for detecting the plurality of first patterns; retrieving, by the pattern processing engine, a plurality of second patterns; selecting, by the pattern processing engine and based at least on the plurality of second patterns, a plurality of second features for detecting the plurality of second patterns; generating, by the pattern processing engine, a second pattern detection model based at least on the first pattern detection model, wherein the generating includes: incorporating one or more of the plurality of second features into the first pattern detection model to create a partially trained interim pattern detection model, wherein the second pattern detection model comprises one or more features that does not exist in the first pattern detection model; and training the partially trained interim pattern detection model using the plurality of second patterns to generate the second pattern detection model. 16. The method of claim 15, further comprising: receiving, by the pattern processing engine, a request that includes a request pattern through a network interface module and a network from a client device of a user; extracting, by the pattern processing engine, one or more features of the received request pattern; applying, by the pattern processing engine and based at least on the extracted one or more features, the second pattern detection model to the request pattern to generate a pattern detection score for received request pattern; classifying the received request, by the pattern processing engine and based at least on the pattern detection score, into one of a plurality of groups; and processing, by the pattern processing engine, the received request based on the detection score, the classification, or both. 17. The method of claim 16, wherein the client device is a mobile device communicating with the server through a wireless network. 18. The method of claim 15, wherein the first pattern detection model and the second pattern detection model are models for detecting patterns of activities of users when interacting network-based systems, and wherein the plurality of first patterns and the plurality of second patterns are the patterns of activities of users when interacting network-based systems. 19. The method of claim 15, wherein a first number of the features of the first pattern detection model and a second number of the features of the second pattern detection model are within a predetermined percentage of each other. 20. The method of claim 15, wherein the first pattern detection model and the second pattern detection model are models for detecting patterns of activities occurring inside a systems, and wherein the plurality of first patterns and the plurality of second patterns are the patterns of activities associated with one or more subsystems of the system.