A computer-implemented neural network graph (1) system, comprising a plurality of neurons (2), each represented by a unique addressable node in a dynamic data structure and each containing a plurality of data, and a plurality of axons and dendrites (4) connecting two or more neurons (2) between them in order to represent a relation and transport one or more data contained in a neuron (2) to another neuron. Each axon (4) having at its end a synapse (3) for connecting it to a neuron (2) and at least one intermediate neuron (2) is connected through an intermediate axon (4) or dendrite and its synapse (3) directly to another axon (4) which connects two main neurons (2). The intermediate neuron (2) and intermediate axon (4) being configured for: selecting one or more specific data contained in the main neurons (2) and transmitted between them along their axon (4) or dendrites (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a first combination of data; selecting one or more specific data, different from the first selection, contained in the main neurons (2) and transmitted between them along the axon (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a second combination of data different from the first; creating a graphical representation comprising a graph (1) of said data in which a first abstraction level is defined by said first selection and a second abstraction level is defined by said second selection different from the first.

A computer-implemented neural network graph (1) system, comprising a plurality of neurons (2), each represented by a unique addressable node in a dynamic data structure and each containing a plurality of data, and a plurality of axons and dendrites (4) connecting two or more neurons (2) between them in order to represent a relation and transport one or more data contained in a neuron (2) to another neuron. Each axon (4) having at its end a synapse (3) for connecting it to a neuron (2) and at least one intermediate neuron (2) is connected through an intermediate axon (4) or dendrite and its synapse (3) directly to another axon (4) which connects two main neurons (2). The intermediate neuron (2) and intermediate axon (4) being configured for: selecting one or more specific data contained in the main neurons (2) and transmitted between them along their axon (4) or dendrites (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a first combination of data; selecting one or more specific data, different from the first selection, contained in the main neurons (2) and transmitted between them along the axon (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a second combination of data different from the first; creating a graphical representation comprising a graph (1) of said data in which a first abstraction level is defined by said first selection and a second abstraction level is defined by said second selection different from the first.

Methods, systems and apparatus, including computer programs encoded on computer storage medium, for training a neurosymbolic data imputation system on training data inputs in a domain to impute missing data in a data input from the data domain. In one aspect a method includes, for each training data input, adding random noise to missing fields of the training data input;

generating an embedding data input for the training data input using concept embeddings from the domain; processing the noisy data input and the embedding data input through a correlation network to obtain correlation data; applying attention to the noisy training data input and the correlation data to generate a combined data input; processing, by an autoencoder, the combined data input to obtain a decoded data output; computing a difference between the decoded data output and the training data input; and updating parameters of the data imputation system using the difference.

A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

A self-managing database system includes a metrics collector to collect metrics data from one or more databases of a computing system and an anomaly detector to analyze the metrics data and detect one or more anomalies. The system includes a causal inference engine to mark one or more nodes in a knowledge representation corresponding to the metrics data for the one or more anomalies and to determine a root cause with a highest probability of causing the one or more anomalies using the knowledge representation. The system includes a self-healing engine, to take at least one remedial action for the one or more databases in response to determination of the root cause.

Aspects described herein describe providing content recommendations such as, for example, recommendations for video content. A content recommendation may be based on when content was previously consumed.

Methods, systems, and computer-readable media consistent with the present disclosure manage multiple telephone calls by managing a session record associated with the call, amending the session record according to a plurality of rules to reflect a plurality of instructed actions, evaluating an amended session record to derive at least one of the plurality of instructed actions, and implementing a derived instructed action on the call under the control of an automated apparatus.

This disclosure relates to recommendations made to users based on learned behavior patterns. User behavior data is collected and grouped according labels. The grouped user behavior data is labeled and used to train a machine learning model based on features and tasks associated with the classification. User behavior is then predicted by applying the trained machine learning model to the collected user behavior data, and a task is recommended to the user.

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes at least an AI module executing a model, a dataset, and the module adapted to be trained. The method includes receiving input data in the module adapted to be trained, labelling data as good data and bad data in the module adapted to be trained, classifying binarily the labelled good data and the labelled bad data in the module adapted to be trained, inputting the binarily classified data into the AI module, and recording internal behavior of the AI module in response to the binarily classified data on the module adapted to be trained.