Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining clusters of similar digital documents using unique document signatures. Specifically, the disclosed system processes digital text in a digital document to tokenize character strings (e.g., words) in the digital document by combining a subset of character values and string lengths in the character strings. Additionally, the disclosed system generates a document signature for the digital document by combining subsets of tokens generated for the digital document into a token sequence indicative of the digital text in the digital document. The disclosed system determines a cluster of similar digital documents including the digital document by comparing the document signature of the digital document to document signatures corresponding to a plurality of digital documents.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations modifying physical emissions sources of an entity based on past and modeled emissions for the entity. Specifically, the disclosed system monitors emissions produced by an entity by determining a number of emissions sources corresponding to an entity and a plurality of emissions values for the emissions sources. Additionally, the disclosed system determines a plurality of constraints corresponding to the entity. The disclosed system also determines goals for the entity including target emissions values. The disclosed system utilizes a modified gradient descent model to iteratively adjust emissions values for the physical emissions sources to obtain the target emissions values according to the constraints.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for correcting entity detection errors with entity correction and resolution in optical character recognition for digitization of physical documents. Specifically, the disclosed system utilizes named entity recognition to extract entities from character strings (e.g., words) in a digital text document. The disclosed system also tokenizes the character strings in the digital text document based on attributes of the character strings. Furthermore, the disclosed system compares the extracted entities and tokenized character strings to determine similarity metrics between the extracted entities and tokenized character strings. The disclosed system also compares extracted entities to character strings including special/numerical characters to determine similarity metrics indicating correlation probabilities between entities and character strings.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations modifying physical emissions sources of an entity based on past and modeled emissions for the entity. Specifically, the disclosed system monitors emissions produced by an entity by determining a number of emissions sources corresponding to an entity and a plurality of emissions values for the emissions sources. Additionally, the disclosed system determines a plurality of constraints corresponding to the entity. The disclosed system also determines goals for the entity including target emissions values. The disclosed system utilizes a modified gradient descent model to iteratively adjust emissions values for the physical emissions sources to obtain the target emissions values according to the constraints.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations modifying physical emissions sources of an entity based on past and modeled emissions for the entity. Specifically, the disclosed system monitors emissions produced by an entity by determining a number of emissions sources corresponding to an entity and a plurality of emissions values for the emissions sources. Additionally, the disclosed system determines a plurality of constraints corresponding to the entity. The disclosed system also determines goals for the entity including target emissions values. The disclosed system utilizes a modified gradient descent model to iteratively adjust emissions values for the physical emissions sources to obtain the target emissions values according to the constraints.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for modifying physical emissions sources based on a plurality of simulations of different scenarios utilizing a modified gradient descent model. Specifically, the disclosed system utilizes the modified gradient descent model to generate emissions value modifications for physical emissions sources corresponding to an entity based on a set of constraints and target emissions values. The disclosed system runs a plurality of simulations to generate modified target emissions values, utilizing the modified gradient descent model, by modifying source attributes of the physical emissions sources according to a plurality of probability distributions representing source attributes of the physical emissions sources.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining clusters of similar digital documents using unique document signatures. Specifically, the disclosed system processes digital text in a digital document to tokenize character strings (e.g., words) in the digital document by combining a subset of character values and string lengths in the character strings. Additionally, the disclosed system generates a document signature for the digital document by combining subsets of tokens generated for the digital document into a token sequence indicative of the digital text in the digital document. The disclosed system determines a cluster of similar digital documents including the digital document by comparing the document signature of the digital document to document signatures corresponding to a plurality of digital documents.