Abstract: The system and methods for audience and return recommendation for targeted content items using machine learning and similarity models. The method includes receiving an input dataset comprising of an input audience or specified constraints on the targeted content items. A join operation augments the input dataset by adding individual characteristics data. A trained machine model generates an engagement score for each individual and by using a threshold on the engagement score each individual is classified into a tier category. Based on the engagement level a target audience is generated. Using a similarity model, an expanded audience is generated which increases the size of the target audience by adding similar individuals from the reference audiences. A return is computed using the individuals in the expanded audience while meeting the specified constraints. The expanded audience and return for the targeted content items are returned to the client device.

Abstract: Particular content is retrieved from a particular site, converted into vector embeddings, and categorized based on a similarity between the vector embeddings and aggregate vector embeddings of various categories. Consumer metric values are accessed for instances of content consumption by users and content for the sites accessed by those users as converted to vector embeddings. The consumer metric values and vector embeddings are used to train and validate a machine learning model. Content of a particular site is retrieved and converted into vector embeddings, and the machine learning model is used to generate predicted consumer metric values for the content, the content also having been categorized.

Abstract: Particular content is retrieved from a particular site, converted into vector embeddings, and categorized based on a similarity between the vector embeddings and aggregate vector embeddings of various categories. Consumer metric values are accessed for instances of content consumption by users and content for the sites accessed by those users as converted to vector embeddings. The consumer metric values and vector embeddings are used to train and validate a machine learning model. Content of a particular site is retrieved and converted into vector embeddings, and the machine learning model is used to generate predicted consumer metric values for the content, the content also having been categorized.

Abstract: Techniques for placing content in metaverses in environments contextually compatible with the content are disclosed. A system trains a machine learning model to identify virtual environments compatible with content based on attributes representing contexts of the environments. Using the machine learning model, the system determines an environment for a target content item. The system selects the particular environment for placement of the target content item based on the compatibility score.

Abstract: Techniques for determining content for presentation to users in particular metaverse environments based on behaviors of the users in the environments. A system monitors behavior of a user's avatar in metaverse environments. Based on the behaviors, the system generates an environment-specific user profile. The system selects one of the environments for displaying content based on the environment-specific profiles while the avatar of the user is within the environment.

Abstract: An input dataset corresponding to an audience is augmented by joining a first set of individual IDs of the audience with a second set of individual IDs in a set of reference audiences stored within a cloud system. An engagement score is generated for each individual in the first set of individual IDs by processing the input dataset with augmented data using a trained machine learning model. Each ID in the first set of individual IDs is assigned to a tier category. A target audience (a subset of the first set of individual IDs belonging to a particular tier category) is identified. A similarity score is calculated for each individual belonging to the target audience with the second set of individual IDs in the set of reference audiences. An expanded audience is generated based on the similarity scores.

Abstract: The system and methods for audience and return recommendation for targeted content items using machine learning and similarity models. The method includes receiving an input dataset comprising of an input audience or specified constraints on the targeted content items. A join operation augments the input dataset by adding individual characteristics data. A trained machine model generates an engagement score for each individual and by using a threshold on the engagement score each individual is classified into a tier category. Based on the engagement level a target audience is generated. Using a similarity model, an expanded audience is generated which increases the size of the target audience by adding similar individuals from the reference audiences. A return is computed using the individuals in the expanded audience while meeting the specified constraints. The expanded audience and return for the targeted content items are returned to the client device.

Abstract: Systems are disclosed for determining contextual segments for targeting contextual advertising in metaverses. The system can deploy an observer avatar in a metaverse to capture information inside a portion of a metaverse from behaviors and interactions of a target user avatar. The observer avatar can capture audio, video, images, and text perceived by the observer avatar in the area of the user avatar, along with metadata describing the captured information. Using the captured observations, the system can determine contextual segments classifying the portion of the metaverse. Using a library of the contextual segments, advertisers can distribute contextually targeted advertisements.

Abstract: A learning and motivation booster application for posing questions and taking inputs from user's in response to the questions; designed to provide rewards to user's in the form of movies and video games that can only be assessed when a user specified threshold/rules/objectives is achieved and, depending on the correctness of the user's responses. The first part of the apparatus comprise of computing and application devices that receive inputs and converts them to machine readable codes considered as users' response. It then compares those responses to existing correct responses. The second part is comprised of device that stores questions and answers which can be activated by the first set of devices to display to the user. The third part of the apparatus comprises an entertainment database and all other potential entertaining devices and software with contents to reward users based on the correctness of their inputs/responses.

Abstract: Techniques for determining recommended allocations of resources among different platforms that sell a common type of inventory. Determining the allocations can include obtaining parameters of a campaign from a client. Determining the allocations can include combining current campaign parameters and scoring with historical campaign performance data to create recommendations for dividing resources among different media platforms.

Abstract: Systems are disclosed for determining contextual segments for targeting contextual advertising in metaverses. The system can deploy an observer avatar in a metaverse to capture information inside a portion of a metaverse from behaviors and interactions of a target user avatar. The observer avatar can capture audio, video, images, and text perceived by the observer avatar in the area of the user avatar, along with metadata describing the captured information. Using the captured observations, the system can determine contextual segments classifying the portion of the metaverse. Using a library of the contextual segments, advertisers can distribute contextually targeted advertisements.

Abstract: Techniques for determining recommended allocations of resources among different platforms that sell a common type of inventory. Determining the allocations can include obtaining parameters of a campaign from a client. Determining the allocations can include combining current campaign parameters and scoring with historical campaign performance data to create recommendations for dividing resources among different media platforms.

Abstract: A learning and motivation booster application for posing questions and taking inputs from user's in response to the questions; designed to provide rewards to user's in the form of movies and video games that can only be assessed when a user specified threshold/rules/objectives is achieved and, depending on the correctness of the user's responses. The first part of the apparatus comprise of computing and application devices that receive inputs and converts them to machine readable codes considered as users' response. It then compares those responses to existing correct responses. The second part comprise of devices that stores questions and answers which can be activated by the first set of devices to display to the user. The third part of the apparatus consist of an entertainment database and all other potential entertaining devices and software with contents to reward users based on the correctness of their inputs/responses.

Abstract: A voice recording and label making apparatus for converting users voice into text format, designed to be placed on documents comprising; medical documents, devices, X-rays, medical files, MRI etc. The first part of the apparatus comprise of a computing device that may receive input, the second part comprise of a device that may convert audio to formatted text and the third part comprise an apparatus that places the text format on a medical document or other documents of interest.