Intelligent electronic signature platform

Abstract

Computerized systems and methods are directed to functionality and architecture that is responsible for, among other things, generating a document that is native to an application, user collaboration at the document in near real-time, generating fields in the document, assigning specific fields to specific users to populate, and sending out the document for signature, all within a single editing module or document that is device responsive. Additionally, such functionality and architecture improves existing user interface functionality relative to existing technologies. Moreover, such functionality and architecture improves computer resource consumption (for example storage costs, CPU, and the like.)

What is claimed is: 
     
         1 . A computerized system comprising:
 one or more processors; and   computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:   receiving, over a computer network, a first request to generate one or more elements of a document of a web application, the document being native to the web application, the document requiring at least one electronic signature by one or more entities, the first request being issued at a user device associated with a first user; and   based at least in part on at least one of: the first request or user input of one or more natural language characters at the document, automatically causing generation, via a machine learning model, of at least one of:
 (1) one or more strings at the document, or 
 (2) a field, the field being a data object representing a predetermined category for which a second user is to input data within the field according to the predetermined category. 
   
     
     
         2 . The computerized system of  claim 1 , wherein the one or more strings include at least one of:
 a natural language sequence that represents a completed portion of the one or more natural language characters of the user input,   a template representing pre-formatted natural language content that was generated at a prior time or session relative to a time or session associated with the receiving of the first request, or   a name of an assignee of the field, wherein the assignee includes the second user.   
     
     
         3 . The computerized system of  claim 1 , wherein the generation of at least one of: the one or more strings or the field is further based at least in part on a past history of computer inputs by the first user that input the one or more natural language characters. 
     
     
         4 . The computerized system of  claim 1 , wherein the method of the computerized system further comprising:
 causing presentation, at the document and at the user device, of a message that informs the first user that the at least one of the one or more strings or the field is a suggestion to input next to a partial string representing the one or more natural language characters.   
     
     
         5 . The computerized system of  claim 1 , wherein the method of the computerized system further comprising;
 at least partially in response to receiving the user input of the one or more natural language characters at the document, encoding the one or more natural language characters into one or more word embedding feature vectors that represent positional information or context of each word in the user input; and   in response to the encoding, generating an attention vector for each word of the user input by determining how relevant, via weighting, each word in the user input is relative to at least one other word in the user input and the one of the one or more strings and the field, and wherein the generating of at least one of the one or more strings or the field is based on the generation of an attention vector for each word.   
     
     
         6 . The computerized system of  claim 1 , wherein the method of the computerized system further comprising: training or fine-tuning the machine learning model by learning string pairs or string-field pairs, each string-field pair indicates at field that is predicted to be placed next to a certain string, each string pair indicates a first string that is predicted to be placed next to a second string. 
     
     
         7 . The computerized system of  claim 1 , wherein the method further comprises:
 as part of the automatic generation of at least one of the one or more strings or the field, automatically moving at least one of: the one or more natural language characters, the one or more strings, or the field from a first line to a second line in the document based at least in part on a screen size of the user device.   
     
     
         8 . The computerized system of  claim 1 , wherein the method of the computerized system further comprises:
 subsequent to the automatic generation of at least one of the one or more strings or the field, assigning the field to the second user based on second computer user input from the first user, the assigning being indicative of authorizing only the second user, and not the first user, to populate the field; and   in response to the assigning, automatically causing a transmission, over the computer network, of an indication to a device associated with the second user, wherein the second user is able to populate the field, at the document, based on the assigning of the field and the transmission of the indication.   
     
     
         9 . The computerized system of  claim 1 , wherein the field is one of: a signature field, a company name field, a personal name field, an email address field, a job title field, or a residential or business address field. 
     
     
         10 . The computerized system of  claim 1 , wherein the method of the computerized system further comprises:
 receiving an indication that a field assignee has input an electronic signature at the field of the document; and   in response to the receiving of the indication, automatically causing presentation at the document of the electronic signature such that the first user can view the electronic signature in near real-time relative to when the field assignee input the electronic signature at the field.   
     
     
         11 . The computerized system of  claim 1 , wherein the method of the computerized system further comprises:
 in response to the generation of the field, receiving an indication that the first user has selected a field type for the field; and   in response to the receiving of the indication, automatically cause display, within the field, a string that describes the field type.   
     
     
         12 . A computer-implemented method comprising:
 receiving one or more first natural language characters that were input by a first user at a document of an application, the document requiring at least one electronic signature by one or more entities, the one or more first natural language characters representing a first portion of a sentence;   in response to the receiving of the user input, providing the one or more first natural language characters as input into one or more machine learning models, wherein the one or more machine learning models generates at least one of a field or one or more second natural language characters according to the one or more first natural language characters, the field being a data object representing a predetermined category for which a second user is to input data within the field according to the predetermined category, the one or more second natural language characters representing a second portion of the sentence, and the field representing a third portion of the sentence; and   based at least in part on the generation, causing presentation of at least one of: the field next to the one or more first natural language characters in the document or the one or more second natural language characters next to the one or more first natural language characters at the document.   
     
     
         13 . The computer-implemented method of  claim 12 , wherein the one or more second natural language characters include at least one of:
 a natural language sequence that represents a completed portion of the one or more first natural language characters of the user input,   a template representing pre-formatted natural language content that was generated at a prior time or session relative to a time or session associated with the receiving of the one or more first natural language characters, or   a name of an assignee of the field, wherein the assignee includes the second user.   
     
     
         14 . The computer-implemented of  claim 12 , wherein the generation of at least one of: the one or more second natural language characters or the field is further based at least in part on a past history of computer inputs by the first user that input the one or more first natural language characters. 
     
     
         15 . The computer-implemented method of  claim 12 , further comprising:
 causing presentation, at the document and at the user device, of a message that informs the first user of at least one of the one or more second natural language characters or the field is a suggestion to input next to a partial string representing the one or more first natural language characters; and   in response to receiving an indication of a user selection associated with the message, providing the user selection as feedback to the one or more machine learning models.   
     
     
         16 . The computer-implemented method of  claim 12 , further comprising;
 at least partially in response to receiving the one or more first natural language characters, encoding the one or more first natural language characters into one or more word embedding feature vectors that represent positional information or context of each word in the one or more first natural language characters; and   in response to the encoding, generating an attention vector for each word of the one or more first natural language characters by determining how relevant, via weighting, each word is relative to at least one other word and relative to at least one of the one or more second natural language characters and the field, and wherein the generating of at least one of the one or more second natural language characters or the field is based on the generation of an attention vector for each word.   
     
     
         17 . The computer-implemented method of  claim 12 , further comprising: training or fine-tuning the one or more machine learning models by learning string pairs or string-field pairs, each string-field pair indicates at field that is predicted to be placed next to a certain string, each string pair indicates a first string that is predicted to be placed next to a second string. 
     
     
         18 . One or more non-transitory computer storage media having computer-executable instructions embodied thereon that, when executed, by one or more processors, cause the one or more processors to perform a method, the method comprising:
 receiving, over a computer network, a first request to open a document of an application, the document being native to the application, the document requiring at least one electronic signature by at least a first entity, the first request being issued at a user device associated with a user;   causing generation of a first partial string based on computer user input of a first partial string at a first page of the document, the first partial string being a first portion of a first sentence of an agreement that requires the at least one electronic signature by the first entity;   causing generation of at least one of a first field or a second partial string based at least in part on one of, computer user input or a language model, the second partial string and the first field being a second portion of the first sentence of the agreement that requires the at least one electronic signature by at least the first entity;   receiving, over the computer network and at the user device via a selection at the first page, a request to input a signature field at the first page of the agreement that requires the at least one electronic signature;   assigning the signature field to at least the first entity, the signature field being a data object for which at least the first entity is to input a signature within the signature field; and   in response to the receiving of the request, automatically causing generation, at the first page of the document, of the signature field below the first partial string and at least one of the second partial string or the first field.   
     
     
         19 . The one or more non-transitory computer storage media of  claim 18 , wherein the language model generates the second partial string or the first field, and wherein the second partial string or field includes one of:
 a template representing pre-formatted natural language content that was generated at a prior time or session relative to a time or session associated with the receiving of the request;   a natural language name of an assignee of the field, wherein the assignee includes the first entity, or   a natural language name of the first field;   
     
     
         20 . The one or more non-transitory computer storage media of  claim 18 , the method of the one or more non-transitory computer storage media further comprising;
 at least partially in response to receiving the one or more first natural language characters, encoding the one or more first natural language characters into one or more word embedding feature vectors that represent positional information or context of each word in the one or more first natural language characters; and   in response to the encoding, generating an attention vector for each word of the one or more first natural language characters by determining how relevant, via weighting, each word is relative to at least one other word and relative to each word in the second partial string, and wherein the generating of the second partial string is based on the generation of an attention vector for each word.