System for secure accelerated resource allocation

Abstract

Disclosed in some examples are methods, systems, devices, and machine-readable mediums that provide an ability for an entity to independently commence, advance, and complete a resource allocation offer in a matter of minutes as opposed to weeks or months after an automated resource pre-committal process. The system, using and incorporating machine learning techniques and algorithms, may have several phases, including a setup phase, resource pre-committal phase, an import phase, a processing phase, a verification phase, a resource allocation offer phase, and a resource allocation phase in which the system allocates resources to a vendor.

1 . A computer implemented method for secure resource allocations, the method comprising:
 authenticating a first entity by comparing a captured image of the first entity to an image on a validated credential;   determining a set of historical data describing historical resource management of a second entity from a database, wherein the historical data includes information on a past resource allocation to the second entity;   determining a set of resource management pre-committal parameters for the second entity based upon the set of historical data, wherein the resource management pre-committal parameters include a score;   receiving an image of a request for a resource allocation corresponding to the second entity from the first entity;   extracting resource allocation parameters from the image of a request for a resource allocation by processing the image of a request for a resource allocation using optical character recognition techniques, wherein the resource allocation parameters include a transaction parameter included on the image of the request for a resource allocation, and wherein the transaction parameter includes a resource allocation amount, and recognizing the resource allocation parameters in an output of the optical character recognition by using an artificial intelligence processing of the output, including natural language processing (NLP), wherein the NLP is trained using a supervised learning algorithm;   updating the supervised learning algorithm with the extracted resource allocation parameters;   determining a resource allocation offer including offer parameters based upon the resource management pre-committal parameters and the resource allocation parameters from the request for resource allocation, the resource allocation offer including a plurality of inter-dependent offer parameters;   receiving an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of ones of the plurality of inter-dependent offer parameters; and   causing a resource to be allocated for the second entity based upon the accepted resource allocation offer.   
     
     
         2 . The method of  claim 1 , wherein the method further comprises:
 authenticating an authority of the first entity to act on behalf of a second entity;   receiving a secondary document regarding historical data from the first entity;   extracting a set of secondary historical data describing historical resource management of the second entity from the secondary document using optical character recognition techniques;   validating the set of secondary historical data using at least one validation rule; and   
       wherein determining the set of resource management pre-committal parameters for the second entity based upon the set of historical data comprises using both the set of historical data and the set of secondary historical data. 
     
     
         3 . The method of  claim 1 , wherein a first parameter of the plurality of inter-dependent offer parameters changes based on a selection of a second parameter. 
     
     
         4 . The method of  claim 1 , wherein the method further comprises storing an executed resource allocation offer on a private blockchain database. 
     
     
         5 . The method of  claim 4 , further comprising:
 computing a hash of the executed resource allocation offer; and   storing the hash on a public blockchain database different than the private blockchain database.   
     
     
         6 . The method of  claim 1 , further comprising:
 executing a general lien based upon the resource pre-committals by contacting a first lien service; and   responsive to receiving an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of ones of the plurality of inter-dependent offer parameters, executing a specific lien based upon the selection of ones of the plurality of inter-dependent offer parameters.   
     
     
         7 . The method of  claim 1 , wherein causing the resource to be allocated for the second entity based upon the accepted resource allocation offer comprises allocating resources to a third entity determined based upon the extracted resource allocation parameters. 
     
     
         8 . A system for secure resource allocations comprising:
 at least one processor;   memory, including instructions stored thereon which, when executed by the at least one processor cause the processor to:
 authenticate a first entity by comparing a captured image of the first entity to an image on a validated credential; 
 determine a set of historical data describing historical resource management of a second entity from a database, wherein the historical data includes information on a past resource allocation to the second entity; 
 determine a set of resource management pre-committal parameters for the second entity based upon the set of historical data, wherein the resource management pre-committal parameters include a score; 
 receive an image of a request for resource allocation corresponding to the second entity from the first entity; 
 extract resource allocation parameters from the image of a request for a resource allocation by processing the image of a request for a resource allocation using optical character recognition techniques, wherein the resource allocation parameters include a transaction parameter included on the image of the request for a resource allocation, and wherein the transaction parameter includes a resource allocation amount, and recognize the resource allocation parameters in an output of the optical character recognition by using an artificial intelligence processing of the output, including natural language processing (NLP), wherein the NLP is trained using a supervised learning algorithm; 
 update the supervised learning algorithm with the extracted resource allocation parameters; 
 determine a resource allocation offer based on the resource management pre-committal parameters and the request for resource allocation, the resource allocation offer including a plurality of inter-dependent offer parameters; 
 receive an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of the ones of the plurality of inter-dependent offer parameters; and 
 cause a resource to be allocated for the second entity based upon the accepted resource allocation offer. 
   
     
     
         9 . The system of  claim 8 , wherein the instructions further cause the processor to:
 authenticate an authority of the first entity to act on behalf of a second entity;   receive a secondary document regarding historical data from the first entity;   extract a set of secondary historical data describing historical resource management of the second entity from the secondary document using optical character recognition techniques;   validate the set of secondary historical data using at least one validation rule; and   
       wherein to determine the set of resource management pre-committal parameters for the second entity based upon the set of historical data comprises using both the set of historical data and the set of secondary historical data. 
     
     
         10 . The system of  claim 8 , wherein a first parameter of the plurality of inter-dependent offer parameters changes based on a selection of a second parameter. 
     
     
         11 . The system of  claim 8 , wherein the instructions further cause the processor to:
 compute a hash of an executed resource allocation offer; and   store the hash on a public blockchain database.   
     
     
         12 . The system of  claim 8 , wherein the instructions further cause the processor to:
 execute a general lien based upon the resource pre-committals by contacting a first lien service; and   responsive to receiving an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of ones of the plurality of inter-dependent offer parameters, execute a specific lien based upon the selection of ones of the plurality of inter-dependent offer parameters.   
     
     
         13 . The system of  claim 8 , wherein to cause the resource to be allocated for the second entity based upon the accepted resource allocation offer comprises allocating resources to a third entity determined based upon the extracted resource allocation parameters. 
     
     
         14 . The system of  claim 11 , wherein the instructions further cause the processor to store the executed resource allocation offer on a private blockchain database different from the public blockchain database. 
     
     
         15 . A non-transitory machine-readable medium including instructions for operation of a computing system, which when executed by the machine, cause the machine to:
 authenticate a first entity by comparing a captured image of the first entity to an image on a validated credential;   determine a set of historical data describing historical resource management of a second entity from a database, wherein the historical data includes information on a past resource allocation to the second entity;   determine a set of resource management pre-committal parameters for the second entity based upon the set of historical data, wherein the resource management pre-committal parameters include a score;   receive an image of a request for resource allocation corresponding to the second entity from the first entity;   extract resource allocation parameters from the image of a request for a resource allocation by processing the image of a request for a resource allocation using optical character recognition techniques wherein the resource allocation parameters include a transaction parameter included on the image of the request for a resource allocation, and wherein the transaction parameter includes a resource allocation amount, and recognize the resource allocation parameters in an output of the optical character recognition by using an artificial intelligence processing of the output, including natural language processing (NLP), wherein the NLP is trained using a supervised learning algorithm;   update the supervised learning algorithm with the extracted resource allocation parameters; determine a resource allocation offer based on the resource management pre-committal parameters and the request for resource allocation, the resource allocation offer including a plurality of inter-dependent offer parameters;   receive an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of the ones of the plurality of inter-dependent offer parameters; and   cause a resource to be allocated for the second entity based upon the accepted resource allocation offer.   
     
     
         16 . The non-transitory machine-readable medium of  claim 15 , wherein the instructions further cause the machine to:
 authenticate an authority of the first entity to act on behalf of a second entity;   receive a secondary document regarding historical data from the first entity;   extract a set of secondary historical data describing historical resource management of the second entity from the secondary document using optical character recognition techniques;   validate the set of secondary historical data using at least one validation rule; and   
       wherein to determine the set of resource management pre-committal parameters for the second entity based upon the set of historical data comprises using both the set of historical data and the set of secondary historical data. 
     
     
         17 . The non-transitory machine readable medium of  claim 15 , wherein a first parameter of the plurality of inter-dependent offer parameters changes based on a selection of a second parameter. 
     
     
         18 . The non-transitory machine-readable medium of  claim 15 , wherein the instructions further cause the machine to:
 store an executed resource allocation offer on a private blockchain database;   compute a hash of the executed resource allocation offer on the executed resource allocation offer; and   store the hash on a public blockchain database different than the private blockchain database.   
     
     
         19 . The non-transitory machine-readable medium of  claim 15 , wherein the instructions further cause the machine to:
 execute a general lien based upon the resource pre-committals by contacting a first lien service; and   responsive to receiving an acceptance of the resource allocation offer from the first entity, the acceptance including a selection of ones of the plurality of inter-dependent offer parameters, execute a specific lien based upon the selection of ones of the plurality of inter-dependent offer parameters.   
     
     
         20 . The non-transitory machine-readable medium of  claim 15 , wherein to cause the resource to be allocated for the second entity based upon the accepted resource allocation offer comprises allocating resources to a third entity determined based upon the extracted resource allocation parameters.