Abstract: In some implementations, the techniques described herein relate to a method including: receiving, by a processor, a natural language input; retrieving, by the processor, a plurality of semantically relevant results based on the natural language input; classifying, by the processor, an intent of the natural language input using a first machine learning model; selecting, by the processor, a second machine learning model based on the intent; generating, by the processor, a prompt based on a type of the second machine learning model using the natural language input and the plurality of semantically relevant results. inputting, by the processor, to prompt into the second machine learning model; obtaining, by the processor, a result responsive to the prompt from the second machine learning model; and providing, by the processor, the result to the user.

Abstract: The techniques described herein relate to a method including: receiving, by a processor, a data record having a plurality of fields; generating, by the processor, a risk score for the data record using a predictive model; determining, by the processor, that the data record is a potential anomaly based on the risk score; identifying, by the processor, an anomalous field from the plurality of fields; generating, by the processor, a plurality of permutations of the data record, the plurality of permutations generated by changing a value of the anomalous field; and outputting, by the processor, a replacement record selected from the plurality of permutations, the replacement record having a field value for the anomalous field that generates a lowest risk score among the plurality of permutations.

Abstract: The techniques described herein relate to a method including: receiving, by a processor, a data record having a plurality of fields; generating, by the processor, a risk score for the data record using a predictive model; determining, by the processor, that the data record is a potential anomaly based on the risk score; identifying, by the processor, an anomalous field from the plurality of fields; generating, by the processor, a plurality of permutations of the data record, the plurality of permutations generated by changing a value of the anomalous field; and outputting, by the processor, a replacement record selected from the plurality of permutations, the replacement record having a field value for the anomalous field that generates a lowest risk score among the plurality of permutations.

Abstract: In some aspects, the techniques described herein relate to a method including: initializing a population of hypotheses; computing misfit values for each of the hypotheses, the misfit values computed using a fitness function including a weighted summation, wherein terms of weighted summation include metric functions; generating a plurality of offspring hypotheses based on the population of hypotheses and a crossover bitmask; generating a new population using the plurality of offspring and a subset of the population of hypotheses; mutating at least one hypothesis in the new population; selecting a hypothesis from the new population based on a corresponding misfit value of the hypothesis; and allocating at least one resource based on the hypothesis.

Abstract: The techniques described herein relate to a method including: receiving, by a processor, a data record having a plurality of fields; generating, by the processor, a risk score for the data record using a predictive model; determining, by the processor, that the data record is a potential anomaly based on the risk score; identifying, by the processor, an anomalous field from the plurality of fields; generating, by the processor, a plurality of permutations of the data record, the plurality of permutations generated by changing a value of the anomalous field; and outputting, by the processor, a replacement record selected from the plurality of permutations, the replacement record having a field value for the anomalous field that generates a lowest risk score among the plurality of permutations.

Abstract: The example embodiments relate to matching response data to request data. in an embodiment, a method includes retrieving a request and a plurality of responses; generating packed candidates based on the request and the plurality of responses, a given packed candidate in the packed candidates including the request and a subset of the plurality of responses; generating feature vectors corresponding to the packed candidates, a given feature vector in the feature vectors including at least one aggregated feature computed based on the request and one or more corresponding responses in a respective packed candidate; inputting the feature vectors into a machine learning (ML) model, the ML model configured to output predictions corresponding to the feature vectors; selecting a feature vector from the feature vectors based on the predictions; and storing responses associated with the optimal feature vector and the request in a data storage device.

Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.

Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.

Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.