Abstract: An example method includes receiving by a server and from a computing device a natural language-based query, converting the natural language-based query into an embedding formatted query having a vector format, inputting the embedding formatted query into a machine-learning logic model, and determining by the machine-learning logic model a query response to the embedding formatted query that is predicted by the machine-learning logic model to be in context of beauty or cosmetic industry. The machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space and the embedding space is limited to salient terms associated with the beauty or cosmetic industry. The method also includes communicating by the machine-learning logic model the query response to the computing device.

Abstract: Systems and methods for selecting a charging entity based on occupancy status are provided. In one embodiment, a method includes determining a current geo-location and state of charge of a requesting vehicle and a plurality of charging entities that are within a remaining distance of the requesting vehicle. Occupancy statuses for one or more charging entities of the plurality of charging entities are determined based on a number charging vehicles and a charging parameter. The method includes estimating charging speeds for the one or more charging entities based on the occupancy statuses. A charging station map user interface that pin points the current geo-location of the requesting vehicle and the one or more charging entities is presented. The one or more charging entities are presented with labels. The method further includes reserving a charging station of a selected charging entity of the plurality of charging entities by selecting a label.

Abstract: A system for guiding interactions with a user device requests a response from first users, stores the response as response data forming a subset of a personal data set of each of the responding first users, and generates a predictive model during training of a machine learning program with a training data set including the personal data set of each of the first users. The predictive model predicts a predicted response of a second user associated with the user device by correlating a personal data set of the second user to the personal data set of at least one of the first users. The computer sends a communication to the user device of the second user having content relating to the first product and/or service when it is determined that the predicted response indicates an interest in or preference of the second user for the first product and/or service.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: In one aspect, a computerized method for dynamically determining a veteran status of a candidate in a set of candidate search results, comprising: generating a searchable online database of diverse candidates; providing the searchable online database of diverse candidates qualified for a specified set of specialized and skilled positions, wherein a job title of candidate is associated with each candidate, and wherein a veteran's status probability is associated with each of the candidates; dynamically determining the veteran's status probability of each candidate in the online database by: parsing the candidate profiles to obtain a set of profile content for each candidate profile in the searchable online database of diverse candidates; matching the set of profile content with a set of veteran-status related keywords; based on a specified number of matches between the set of veteran-status related keywords and the profile content, calculating a probability that each candidate has a veteran status.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for guiding interactions with a user device requests a response from first users, stores the response as response data forming a subset of a personal data set of each of the responding first users, and generates a predictive model during training of a machine learning program with a training data set including the personal data set of each of the first users. The predictive model predicts a predicted response of a second user associated with the user device by correlating a personal data set of the second user to the personal data set of at least one of the first users. The computer sends a communication to the user device of the second user having content relating to the first product and/or service when it is determined that the predicted response indicates an interest in or preference of the second user for the first product and/or service.

Abstract: A computerized method useful for managing an online employee search tool and recruitment platform comprising: providing a searchable online database of diverse candidates qualified for a specified set of specialized and skilled positions, wherein a job title of candidate is associated with each candidate, and wherein an ethnicity is associated with each of the candidates; dynamically determining an ethnicity of each candidate in the online database; providing an online employee search tool configured to implement sourcing services of the searchable on line database by: receiving a search query comprising a search for the job title of each candidate, a ethnicity of each candidate, implementing a specified type of search for the job title and ethnicity, retrieving a set of search results based on their relevancy to the job title and the ethnicity, and ordering the set of search results for the set of candidates with the ethnicity based on a relevancy of each candidate's qualifications and experience to the job

Abstract: A computerized method useful for managing an online employee search tool and recruitment platform including the step of providing a searchable online database of diverse candidates qualified for a specified set of specialized and skilled positions. A job title of candidate is associated with each candidate. A gender is associated with each of the candidates. The method includes the step of dynamically determining a gender of each candidate in the online database. The method includes the step of obtaining a set of first names and associated gender of each first name. The method includes the step of breaking apart the set of first names into twenty-six (26) files each differentiated by different first letter, and for all the candidates in the database assigning a sex to each candidate based on the first name. The method includes the step of providing an online employee search tool configured to implement sourcing services of the searchable online database.

Abstract: A first data set associated with a first group of users is accessed. The first data set includes demographic data, online behavior data, and additional user data associated with the users in the first group. A second data set associated with a second group of users is accessed. The second data set includes demographic data and online behavior data but not additional user data associated with the users in the second group. One or more sets of matched users are determined based on the demographic data and online behavior data included in the first data set and the demographic data and online behavior data included in the second data set. Each set includes a user from the first group matched with a user from the second group. Based on the one or more sets of matched users, an augmented second data set that includes additional user data associated with the users in the second group is generated. One or more reports are generated based on the augmented second data set.

Abstract: Disclosed is a method and apparatus that receives, over a network from a computing device being used by a user, a request to configure a first software module host with respect to a software module, the first software module host associated with the user. The apparatus transmits over the network to the computing device instructions to configure, for display by the computing device, the first software module host in accordance with the request. The apparatus computer transmits over the network to the computing device instructions to apply the same configuration with respect to the software module to a second software module host, the second software module host also associated with the user.

Abstract: Disclosed is a method and apparatus that receives, over a network from a computing device being used by a user, a request to configure a first software module host with respect to a software module, the first software module host associated with the user. The apparatus transmits over the network to the computing device instructions to configure, for display by the computing device, the first software module host in accordance with the request. The apparatus computer transmits over the network to the computing device instructions to apply the same configuration with respect to the software module to a second software module host, the second software module host also associated with the user.

Abstract: Disclosed is a method and apparatus that receives, over a network from a computing device being used by a user, a request to configure a first software module host with respect to a software module, the first software module host associated with the user. The apparatus transmits over the network to the computing device instructions to configure, for display by the computing device, the first software module host in accordance with the request. The apparatus computer transmits over the network to the computing device instructions to apply the same configuration with respect to the software module to a second software module host, the second software module host also associated with the user.