Abstract: Vehicle diagnostic methods and systems are provided for providing predictive step-by-step troubleshooting guidance. One method involves determining a sequence of recommended troubleshooting actions for resolving a potential fault condition associated with a diagnostic code based at least in part on a vehicle component associated with the diagnostic code and metadata associated with the vehicle using historical diagnostics data and sequentially providing, within a client application at a client device, graphical indicia of the recommended troubleshooting actions in accordance with the sequence based on a respective probability of resolution associated with the respective recommended troubleshooting actions based at least in part on the historical diagnostics data.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. In certain aspects, the digital data may include textual data from the digital media communications. The apparatus may input the textual data into a natural language processing (NLP) model. The apparatus may obtain a life event indication as an output of the NLP model. In certain aspects, the life event indication may include a classification tag and intent information associated with an item. The apparatus may determine whether the intent information meets a likelihood threshold. The apparatus may output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.

Abstract: A system and method for identifying prospects with a buying intent and connecting them with relevant businesses. An example method may comprise obtaining a training dataset; applying a first scoring algorithm to obtain a first score for each entry in the training dataset; receiving one or more scores from a user for one or more entries in the training dataset; rescoring the training dataset based on the one or more scores received from the user; creating a deep learning model based on the rescored dataset; obtaining digital media posts comprising data from one or more digital media platforms; scoring each received digital media post by using the deep learning model; providing certain scored digital media posts to the user; receiving a second score from the user; and updating the deep learning model based on the second score.

Abstract: A system and method for identifying prospects with a buying intent and connecting them with relevant businesses. An example method may comprise obtaining a training dataset; applying a first scoring algorithm to obtain a first score for each entry in the training dataset; receiving one or more scores from a user for one or more entries in the training dataset; rescoring the training dataset based on the one or more scores received from the user; creating a deep learning model based on the rescored dataset; obtaining digital media posts comprising data from one or more digital media platforms; scoring each received digital media post by using the deep learning model; providing certain scored digital media posts to the user; receiving a second score from the user; and updating the deep learning model based on the second score.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. In certain aspects, the digital data may include textual data from the digital media communications. The apparatus may input the textual data into a natural language processing (NLP) model. The apparatus may obtain a life event indication as an output of the NLP model. In certain aspects, the life event indication may include a classification tag and intent information associated with an item. The apparatus may determine whether the intent information meets a likelihood threshold. The apparatus may output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. The apparatus may input the textual data into a NLP model. The apparatus may obtain intent data associated with an item as an output of the NLP model. The apparatus may input the intent data into a tagging model. The apparatus may obtain an attribute tag as an output of the tagging model. The apparatus may determine whether the intent data meets a likelihood threshold. The apparatus may determine whether the attribute tag meet a relevance threshold. The apparatus may output the intent data and the attribute tag to an external device upon determining that the intent data meets the likelihood threshold and that the attribute tag meets the relevance threshold.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. In certain aspects, the digital data may include textual data from the digital media communications. The apparatus may input the textual data into a natural language processing (NLP) model. The apparatus may obtain a life event indication as an output of the NLP model. In certain aspects, the life event indication may include a classification tag and intent information associated with an item. The apparatus may determine whether the intent information meets a likelihood threshold. The apparatus may output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. The apparatus may input the textual data into a NLP model. The apparatus may obtain intent data associated with an item as an output of the NLP model. The apparatus may input the intent data into a tagging model. The apparatus may obtain an attribute tag as an output of the tagging model. The apparatus may determine whether the intent data meets a likelihood threshold. The apparatus may determine whether the attribute tag meet a relevance threshold. The apparatus may output the intent data and the attribute tag to an external device upon determining that the intent data meets the likelihood threshold and that the attribute tag meets the relevance threshold.

Abstract: A system and method for identifying prospects with a buying intent and connecting them with relevant businesses. An example method may comprise obtaining a training dataset; applying a first scoring algorithm to obtain a first score for each entry in the training dataset; receiving one or more scores from a user for one or more entries in the training dataset; rescoring the training dataset based on the one or more scores received from the user; creating a deep learning model based on the rescored dataset; obtaining digital media posts comprising data from one or more digital media platforms; scoring each received digital media post by using the deep learning model; providing certain scored digital media posts to the user; receiving a second score from the user; and updating the deep learning model based on the second score.