A data manipulation detection device that includes an alert engine configured to receive data from a data source, apply a set of rules for a threat model to the data using a first machine learning model, and to obtain an alert vector in response to applying the set of rules to the data. The device further includes an alert feedback engine configured to receive alert feedback that includes text comments for the alert vector. The device further includes a natural language processing (NLP) training engine configured to identify the text comments for the alert status and identify keywords within the text comments associated with a rule parameter value for a rule. The NLP training engine is further configured to determine a new rule parameter value based on the identified keywords and modify a rule parameter value for the rule based on the new rule parameter value.

Translation capability for language processing determines an existence of an abbreviation, followed by non-exact matching to map the abbreviation to the original full term. A received string in a source language is provided as input to a translation service. Translation proposals in a different target language are received back. A ruleset (considering factors, e.g., camel case format, the presence of a concluding period, and/or consecutive consonants) is applied to generate abbreviation candidates from the translation proposals. Non-exact matching (referencing e.g., a comparison metric) may then be used to map the abbreviation candidates to text strings of their original full terms. A mapping of the abbreviation to the text string of the original full term is stored in a translation database comprising linguistic data. Embodiments leverage existing resources (e.g., translation service, non-exact matching) to reduce effort and expense of accurately identifying abbreviations and then mapping them to their full original terms.

Translation capability for language processing determines an existence of an abbreviation, followed by non-exact matching to map the abbreviation to the original full term. A received string in a source language is provided as input to a translation service. Translation proposals in a different target language are received back. A ruleset (considering factors, e.g., camel case format, the presence of a concluding period, and/or consecutive consonants) is applied to generate abbreviation candidates from the translation proposals. Non-exact matching (referencing e.g., a comparison metric) may then be used to map the abbreviation candidates to text strings of their original full terms. A mapping of the abbreviation to the text string of the original full term is stored in a translation database comprising linguistic data. Embodiments leverage existing resources (e.g., translation service, non-exact matching) to reduce effort and expense of accurately identifying abbreviations and then mapping them to their full original terms.

A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

A knowledgebase of an expert system is populated with rules inferred from a set of business processes that govern the manner in which the business interacts with users. Each business process contains an input, an output, an action, and a set of dependency relationships that relate pairs of the input, the output, and the action. Each process's input, output, action, and dependency relationships are translated, respectively, into a subject, an object, a predicate, and a set of dependency relationships among the subject, object, and predicate, of a natural-language rule. Each rule is stored in the expert system's knowledgebase as a directed graph, and nodes representing each stored subject, object, and predicate are assigned domain classifications as a function of characteristics of the business rule. These domain classifications are represented within the knowledgebase as a set of domain classifications determined as a further function of characteristics of the business rule.

A knowledgebase of an expert system is populated with rules inferred from a set of business processes that govern the manner in which the business interacts with users. Each business process contains an input, an output, an action, and a set of dependency relationships that relate pairs of the input, the output, and the action. Each process's input, output, action, and dependency relationships are translated, respectively, into a subject, an object, a predicate, and a set of dependency relationships among the subject, object, and predicate, of a natural-language rule. Each rule is stored in the expert system's knowledgebase as a directed graph, and nodes representing each stored subject, object, and predicate are assigned domain classifications as a function of characteristics of the business rule. These domain classifications are represented within the knowledgebase as a set of domain classifications determined as a further function of characteristics of the business rule.

A method for sharing content is provided. An image of content is obtained. An address is generated based on the image using a set of predefined rules. The address is associated with the content. The content is provided to a computing device in response to the computing device accessing the address.

A method for sharing content is provided. An image of content is obtained. An address is generated based on the image using a set of predefined rules. The address is associated with the content. The content is provided to a computing device in response to the computing device accessing the address.

The present disclosure describes a system, method, and computer program for converting natural language queries to structured database queries, including nested database queries. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. The entities are tagged with an entity type that enables the system to identify any database object names, candidate query fields, operands, and contextual entities in the query. From the tagged entities, the system identifies one or more valid explicit, implicit, and indirect references to database objects in the user query. If there is only one valid reference to a database object in the user's query, the system proceeds with steps to create a single-object query. If there are valid references to two or more database objects in the query, the system proceeds with steps to create a nested database query. This includes grouping candidate query fields, operands, and contextual entities by independent object name, and evaluating each group separately to identify subject fields, conditional parameters, order/sort criteria, and record count limits for each group.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.