Methods, systems, and apparatus, including computer programs encoded on computer storage media provide for the intelligent detection of sensitive information within a communication platform. The system displays a communication interface including a first input section for receiving an input message associated with a sending user account, and a display section for displaying message information received by the sending user account from other user accounts. The system determines or retrieves a sensitive messaging profile for the sending user account, then receives an input message associated with the sending user account. The system detects that the input message comprises sensitive information, and transmits a sensitive message to one or more receiving user accounts within a sensitive container component, with the sensitive message including at least a subset of the input message.

Technology is provided for causing a computing system to extract conversation features from a multiparty conversation (e.g., between a coach and mentee), apply the conversation features to a machine learning system to generate conversation analysis indicators, and apply a mapping of conversation analysis indicators to actions and inferences to determine actions to take or inferences to make for the multiparty conversation. In various implementations, the actions and inferences can include determining scores for the multiparty conversation such as a score for progress toward a coaching goal, instant scores for various points throughout the conversation, conversation impact score, ownership scores, etc. These scores can be, e.g., surfaced in various user interfaces along with context and benchmark indicators, used to select resources for the coach or mentee, used to update coach/mentee matchings, used to provide real-time alerts to signify how the conversation is going, etc.

Disclosed herein are an apparatus and method for converting a neural network. The method includes separating neural network data of a source framework to form a tree structure by analyzing the same, converting the neural network data in a tree structure to a neural network optimized for a target framework, classifying training data based on the result of analysis of the neural network data of the source framework, converting the classified training data to the training data structure of the target framework, and creating a neural network and training data of the target framework by combining the converted neural network and the converted training data.

When a character required to be corrected is specified in a character string of a character recognition result, a plurality of candidate character strings are generated by using a substitution candidate for the specified character and not using a substitution candidate for a character other than the specified character, and one correct character string is finalized from the plurality of generated candidate character strings.

A system and methods for creating correlation nodes is provided. The system generally comprises a computing device having a user interface, processor operably connected to the computing device, power supply, and non-transitory computer-readable medium coupled to the processor and having instructions stored thereon. The system and method are designed to take correlation data of a PoV profile and create correlation nodes. The system may then weight the correlation nodes of the PoV profile and connect the PoV profile with other PoV profiles based on said correlation nodes. Among other things, this technique may be used to create shared experience networks using individual experiences and historical narratives, allowing the system to connect users without the need of a common user connection.

A computer implemented method for optical character recognition (OCR) of a character string in a text image. The method efficiently combines two different OCR engines with the computation that needs to be done by the second OCR engine depending on the results found by the first OCR engine. This method provides, in particular, a high speed and accurate results when the first OCR engine is fast and the second OCR engine is accurate. The combination is possible because the second OCR engine identifies each segment to be processed by the second OCR engine without needing to process all segments.

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

The present disclosure includes systems and methods for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text is determined based on the matching.

A hierarchical document classification system is disclosed. The system includes a text-based document classifier model for classifying an input electronic document into one of a set of predefined document categories. The system further includes an image-based metadata identification model for classifying electronic documents of a particular document category into a set of metadata categories. The system further includes a fuzzy text matcher for supplementing classification accuracy of the image-based metadata identification model to obtain a metadata category for the input electronic document.

The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.