A lens module assembly optimization method includes: in preparing a lens module including assembled N lenses respectively formed in cavities: receiving characteristic information of at least N lenses respectively formed in N cavity groups each including a respective plurality of cavities; and processing information for selecting N cavities from the N cavity groups, based on the characteristic information. A past cavity selection result, a fitness function configured based on data of the assembled N lenses or data of the prepared lens module according to the past cavity selection result, and a genetic algorithm are received or stored. The processing of the information includes updating chromosome entity information based on the fitness function and output chromosome information crossed or mutated based on the genetic algorithm from input chromosome information corresponding to the past cavity selection result, and processing the information based on the chromosome entity information and the characteristic information.

A system and method for constructing a probability model and automatically responding to process anomalies identified by the probability model are disclosed. Data is received for current and prior states of a process, comprising variables in at least two dimensions, and the at least two dimensions being not independently and identically distributed. A segment of a fixed number of prior states is selected and fed into a neural network to output a probability vector for each of the two or more dimensions. The Cartesian product of these probability vectors is calculated to obtain a tensor, wherein each value in the tensor represents a probability that the prior states would be followed by a given state. If the probability in the tensor associated with the present state is less than a predetermined threshold, an electronic communication is automatically generated and transmitted to a client computing device.

Techniques for automatic intelligent information extraction from an electronic document are disclosed. In one embodiment, a computerized method is disclosed comprising training a label prediction model to generate a set of label predictions, obtaining an electronic document, analyzing the electronic document and determining a set of features for each of a set of information items identified in the electronic document, obtaining model output from the label prediction model for each information item, the model output comprising, for a respective information item, a set of probabilities corresponding to a set of information classes, and generating an information extraction comprising a set of labels corresponding to the set of information items.

Systems and methods of optimizing a charging of a vehicle battery are disclosed. Using one or more electronic battery sensors, observable battery state data is determined regarding the charging of the battery. A neural network feature extractor extracts features from preceding vehicle battery state information. A reinforcement learning model, such as an actor-critic model, includes an actor model configured to produce an output associated with a charge command to charge the battery, and a critic model configured to output a predicted reward. The reinforcement learning model is trained based on the vehicle battery state information and the extracted features. This includes updating weights of the actor model to maximize the predicted reward output by the critic model, and updating weights of the feature extractor and weights of the critic model to minimize a difference between the predicted reward and health-based rewards received from charging the battery. Hidden battery state information is approximated based on the extracted features.

A computer-implemented method, a computer program product, and a computer system for using computer vision to automatically generate a unit test case. A computer converts a text file of a source code to an image file of the source code. A computer inputs the image file to a neural network having been trained with source code images. The neural network identifies elements in the image file. The neural network generates a resulting image including identified elements in the image file. A computer extracts the identified elements from the resulting image. A computer generates a text file including a key value map of the identified elements. A computer generates a scaffold of a unit test case, based on the key value map.