Docusign, Inc.

A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

A system may determine environmental, social, and governance (ESG) goal data for an ESG clause type and determine that a first clause of a first electronic document of a plurality of electronic documents and that a second clause of a second electronic document of the plurality of electronic documents correspond to the ESG clause type. The system may determine first ESG commitment data based on content of the first clause of the first electronic document and determine second ESG commitment data based on content of the second clause of the second electronic document. The system may generate ESG metric data based on the first ESG commitment data and the second ESG commitment data and generate dashboard data based on the ESG metric data. The system may output the dashboard data.

A centralized auditing system receives an audit block containing one or more audit files generated by an audit agent running in an audited device. The audit block additionally stores a first digital signature corresponding to a previous audit block, and a second digital signature generated based at least on the one or more audit files and the first digital signature. After receiving the audit block, the auditing system verifies the integrity of the received audit block based on the second digital signature stored in the audit block and/or the first digital signature corresponding to the previous audit block. In response to verifying the integrity of the received audit block, the auditing system adds the received audit block to an audit register. Moreover, the auditing system adds the one or more audit files included in the audit block to an audit database.

Techniques are described for deploying software applications associated with document management. The techniques may include receiving a request, by a computing system and from a client device, to execute a software application for managing an electronic document, wherein the software application is configured to execute using at least a first widget and a second widget. The techniques may further include generating, based on the first widget comprising instructions to initiate an execution of the second widget, instructions for executing the software application at the client device, wherein the instructions are configured to cause the client device to generate contextual information for the first widget and wherein the second widget causes the client device to load the contextual information when executing the second widget. The techniques may further include outputting, by the computing system and to the client device, the instructions to execute the software application for managing the electronic document.

Techniques for artificial intelligence (AI) custom fields are described. A method comprises receiving a custom rule comprising a query request for information in an electronic document stored in a data repository, generating a vectorized rule from the custom rule, selecting a subset of vectorized portions from a set of vectorized portions of the electronic document stored in a vector database corresponding to the vectorized rule, sending the vectorized rule and the subset of vectorized portions to a machine learning (ML) model, receiving a query response to the query request from the ML model, the query response comprising a summary of information contained within one or more vectorized portions of the subset of vectorized portions or a comment on the information contained within the one or more vectorized portions. Other embodiments are described and claimed.

A method, a system, and a computer program product for federated document learning. A plurality of representations of a plurality of datasets from a plurality of client systems are received. Each representation corresponds to a dataset associated with a client system and is generated using a first machine learning (ML) model. A second ML model is applied to the plurality of representations to generate a combined representation of the plurality of datasets. Data from each dataset is not provided to the second ML model. The combined representation is filtered using one or more filtering parameters to generate a filtered representation. Using the second ML model, one or more model weights for training a third ML model in a plurality of third ML models are generated. Each third ML model is associated with a respective client system. The model weights are provided to the plurality of third ML models.

A method, a system, and a computer program product for content-based document access. A content of an electronic document is analyzed using a machine learning (ML) model. The ML model determines presence of a plurality sensitive data in the document. One or more document entity-based parameters are received. At least one sensitive data in the plurality of sensitive data is identified. At least one recipient computing device in a plurality of computing devices is prevented from receiving the document containing at least one sensitive data. The sensitive data is extracted from the document. The document is modified to redact the sensitive data and a modified electronic document is generated. The modified electronic document is transmitted to at least one recipient computing device.

A document management system trains a machine-learned model using a first training set of tagged documents to, when applied to a document, tag one or more portions of the document. The document management system applies the machine-learned model to a target document. One or more portions of the target document incorrectly tagged by the machine-learned model are identified. A feature vector representative of the target document is generated. Each entry of the feature vector is representative of a characteristic of the target document without including private information from the target document. The document management system queries a corpus of documents using the feature vector to identify a set of documents that correspond to the feature vector. A second training set of tagged documents is generated using the identified set of documents. The document management system retrains the machine-learned model using the second training set of tagged documents.

A method, a system, and a computer program product for generating a benchmarking dataset. A type of an electronic document is determined. Using the type of the electronic document, a first request to generate one or more labels for the electronic document is generated. Using a content of the electronic document, a second request to generate one or more labels for the electronic document is generated. The electronic document and the first and second requests are sent to a generative artificial intelligence (AI) model. The generative AI model generates one or more first labels for the electronic document based on the first request and one or more second labels for the electronic document based on the second request. Using one or more first and second labels, one or more labels for the electronic document are generated.

Techniques are disclosed for using a machine learning model to generate document templates from input templates. For example, a computing system receives an input document. A machine learning model of the computing system processes the input document to identify one or more text items corresponding to respective variable field types of the plurality of variable field types. The computing system creates, for each text item of the one or more text items, a variable field for the variable field type of the plurality of variable field types corresponding to the text item and creates, for each variable field of the one or more created variable fields, a mapping for the variable field to a corresponding data source. The computing system generates, based on the input document, a document template comprising the one or more created variable fields and the respective one or more mappings.