Abstract: The present disclosure relates to using a large language model (LLM), provided with user context information and user high-level goal information to generate contextualized action recommendations that can help the user achieve the high-level goal(s). In one exemplary embodiment, a user system, and an AI action recommendation system that is associated with an LLM, are communicatively coupled and cooperatively implement a contextualized action recommendation virtual assistant. Input data comprising personal information data of the user that includes at least one high-level goal of the user, and user context data obtained from the user system can be collected and used to generate a prompt that is input to the LLM. The LLM can then generate a contextualized action recommendation for the user based on the prompt, and the contextualized action recommendation can be presented to the user via a virtual assistant user interface on a display of the user system.

Abstract: Methods of detecting SARS-CoV-2 antibodies with improved specificity are described. The methods can include contacting a sample potentially containing SARS-CoV-2 antibodies with a reagent to remove non-anti-SARS-CoV-2 antibodies, such as a reagent comprising an epitope from a nucleocapsid protein or spike protein of a common coronavirus. The methods can include contacting the sample with a mutant SARS-CoV-2 nucleocapsid protein or spike protein comprising a reduced number of common coronavirus epitopes. The methods can involve comparing results from an immunoassay performed with a SARS-CoV-2 protein to results obtained from immunoassays performed with analogous protein from one or more common coronaviruses.

Abstract: In some embodiments, a method comprises receiving an electronic message. In response to determining that the electronic message includes an express indication from a user that a classification applies or does not apply, the method comprises identifying message attributes of the electronic message that correspond to policy attributes of a machine learning policy and determining values of the policy attributes based on the identified message attributes. The method additionally comprises providing information to a machine learning trainer adapted to train the machine learning policy based on the information. The information comprises the values of the policy attributes and information indicating the classification that applies or does not apply to the electronic message, where such information is based on the express indication that the user included in the electronic message.

Abstract: In some embodiments, a method comprises receiving an electronic message. In response to determining that the electronic message includes an express indication from a user that a classification applies or does not apply, the method comprises identifying message attributes of the electronic message that correspond to policy attributes of a machine learning policy and determining values of the policy attributes based on the identified message attributes. The method additionally comprises providing information to a machine learning trainer adapted to train the machine learning policy based on the information. The information comprises the values of the policy attributes and information indicating the classification that applies or does not apply to the electronic message, where such information is based on the express indication that the user included in the electronic message.

Abstract: In some embodiments, a method comprises receiving an electronic message. In response to determining that the electronic message includes an express indication from a user that a classification applies or does not apply, the method comprises identifying message attributes of the electronic message that correspond to policy attributes of a machine learning policy and determining values of the policy attributes based on the identified message attributes. The method additionally comprises providing information to a machine learning trainer adapted to train the machine learning policy based on the information. The information comprises the values of the policy attributes and information indicating the classification that applies or does not apply to the electronic message, where such information is based on the express indication that the user included in the electronic message.