Abstract: Methods, systems, and computer-readable storage media for receiving, by a social media management platform, an incoming message on a social media platform of a plurality of social media platforms, determining an enterprise of the plurality of enterprises that the incoming message is directed to, determining a class of the incoming message from a set of classes, generating, by one of a public relations (PR) bot and a social bot of the social media management platform, a first reply to the message based on the class of the incoming message, the PR bot and the social bot each being specific to the enterprise and at least partially controlling a social media account of the enterprise on the social media platform, and posting, by the one of the PR bot and the social bot, the first reply to the incoming message on the social media platform using the social media account.

Abstract: Implementations include receiving, by an application programming interface (API) server of a plurality of API servers, a prediction request from a client system, each of the plurality of API servers including a stateless server, selecting, by the API server, a model server from a plurality of model servers based on the prediction request, each of the plurality of model servers including a stateful server, calling, by the API server, the model server to execute inference using a ML model loaded to memory of the model server, receiving, by the API server, an inference result from the ML model, and sending, by the API server, the inference result to the client system.

Abstract: Implementations include receiving, by an application programming interface (API) server of a plurality of API servers, a prediction request from a client system, each of the plurality of API servers including a stateless server, selecting, by the API server, a model server from a plurality of model servers based on the prediction request, each of the plurality of model servers including a stateful server, calling, by the API server, the model server to execute inference using a ML model loaded to memory of the model server, receiving, by the API server, an inference result from the ML model, and sending, by the API server, the inference result to the client system.

Abstract: The present disclosure is directed to systems and methods including generating image data and audio data during a collaborative work using a stereoscopic camera system and a binaural microphone system, determining that physical media have been affixed to a work surface and, for each physical medium, providing a digital object including data representative of the physical medium, and determining that a session has ended, and in response, automatically: processing at least a portion of data of the digital objects using one or more clustering algorithms to provide two or more groups of digital objects, and providing an affinity diagram including the two or more groups of diagrams, the affinity diagram being displayed as digital representations of the physical media, the digital representations being grouped in the two or more groups.

Abstract: Methods, systems, and computer-readable storage media for receiving digital content, receiving a set of locales, generating a set of ethics ratings by processing the digital content through a plurality of machine-learning (ML) models to provide a set of ethics ratings, each ML model in the plurality of ML models being specific to a locale of the set of locales, each ethics rating in the set of ethics ratings being specific to a locale of the set of locales, and providing the set of ethics ratings for the digital content for the selected locales to the user.

Abstract: Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Abstract: Methods, systems, and computer-readable storage media for determining, by an extraction worker of a data extraction system, a number of pages based on data that is to be extracted from the source system, for each page, by a respective extraction worker: querying the source system, and storing a query result in a respective page file within a staging data file system of the data extraction system, for each extraction worker that queries the source system, verifying a status of the extraction worker after expiration of a predetermined delay time based on a timestamp of the page that the extraction worker is handling, and determining that a page status for each page is completed, and in response, posting an extraction completed message to inform the consuming system that the data has been extracted from the source system.

Abstract: Methods, systems, and computer-readable storage media for determining, by an extraction worker of a data extraction system, a number of pages based on data that is to be extracted from the source system, for each page, by a respective extraction worker: querying the source system, and storing a query result in a respective page file within a staging data file system of the data extraction system, for each extraction worker that queries the source system, verifying a status of the extraction worker after expiration of a predetermined delay time based on a timestamp of the page that the extraction worker is handling, and determining that a page status for each page is completed, and in response, posting an extraction completed message to inform the consuming system that the data has been extracted from the source system.

Abstract: The present disclosure is directed to systems and methods including generating image data and audio data during a collaborative work using a stereoscopic camera system and a binaural microphone system, determining that physical media have been affixed to a work surface and, for each physical medium, providing a digital object including data representative of the physical medium, and determining that a session has ended, and in response, automatically: processing at least a portion of data of the digital objects using one or more clustering algorithms to provide two or more groups of digital objects, and providing an affinity diagram including the two or more groups of diagrams, the affinity diagram being displayed as digital representations of the physical media, the digital representations being grouped in the two or more groups.

Abstract: A framework for improving data set in an enterprise system for machine learning is provided. In accordance with one aspect, user input of a project update is provided by a user to an enterprise system. A record of the project update is created in the enterprise system based on the user input. The project update provided by the user into the enterprise system is analyzed using a gamification technique. The analysis includes quantifying the user's input of the project update to the enterprise system by assigning points to the user based on the project update provided to the enterprise system. The assigned points are displayed to the user on a user interface of a user device to enable friendly competition with other users which encourages more detailed and frequent project updates to the enterprise system by the user.

Abstract: Methods, systems, and computer-readable storage media for receiving, within a customer-side staging environment, a computer-executable ADR application from a vendor, testing the computer-executable ADR application within the staging environment, the staging environment executing the computer-executable ADR application using sample data, receiving feedback from a customer, the feedback including an election to deploy the computer-executable ADR application to a production environment from the staging environment, and providing the computer-executable ADR application to the production environment in response to the election to deploy the computer-executable ADR application, the production environment being sealed from data transfer to the staging environment.

Abstract: Methods, systems, and computer-readable storage media for receiving, within a customer-side staging environment, a computer-executable ADR application from a vendor, testing the computer-executable ADR application within the staging environment, the staging environment executing the computer-executable ADR application using sample data, receiving feedback from a customer, the feedback including an election to deploy the computer-executable ADR application to a production environment from the staging environment, and providing the computer-executable ADR application to the production environment in response to the election to deploy the computer-executable ADR application, the production environment being sealed from data transfer to the staging environment.

Abstract: Methods, systems, and computer-readable storage media for receiving digital content, receiving a set of locales, generating a set of ethics ratings by processing the digital content through a plurality of machine-learning (ML) models to provide a set of ethics ratings, each ML model in the plurality of ML models being specific to a locale of the set of locales, each ethics rating in the set of ethics ratings being specific to a locale of the set of locales, and providing the set of ethics ratings for the digital content for the selected locales to the user.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for training ML models in aPaaS architectures including actions of receiving, at a training master provided using a first VM, a training request to train a ML model, the training request being received from a first tenant in a multi-tenant, aPaaS architecture, initiating, by the training master, training of the ML model by a training worker provided using a second VM, during training of the ML model, periodically updating training storage metadata based on metadata describing progress of the training of the ML model, and in response to completion of the training of the ML model, storing a trained ML model in a model storage of the aPaaS architecture.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating a patentability metric and training a patentability model. In an embodiment, a patent analysis system generates and updates a patentability model. The patentability model utilizes vectorized patent publication data and public corpus data to generate a function for predicting the likelihood of patent grant. The patentability model also considers patent grant statistics in generating the function. After generating the function, the patent analysis system may maintain and/or update the patentability model based on new publications and idea disclosures. In this manner, the patent analysis system may analyze vectorized versions of idea disclosures to generate an indicator for predicting patentability.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for training ML models in aPaaS architectures including actions of receiving, at a training master provided using a first VM, a training request to train a ML model, the training request being received from a first tenant in a multi-tenant, aPaaS architecture, initiating, by the training master, training of the ML model by a training worker provided using a second VM, during training of the ML model, periodically updating training storage metadata based on metadata describing progress of the training of the ML model, and in response to completion of the training of the ML model, storing a trained ML model in a model storage of the aPaaS architecture.

Abstract: Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Abstract: A framework for improving data set in an enterprise system for machine learning is provided. In accordance with one aspect, user input of a project update is provided by a user to an enterprise system. A record of the project update is created in the enterprise system based on the user input. The project update provided by the user into the enterprise system is analyzed using a gamification technique. The analysis includes quantifying the user's input of the project update to the enterprise system by assigning points to the user based on the project update provided to the enterprise system. The assigned points are displayed to the user on a user interface of a user device to enable friendly competition with other users which encourages more detailed and frequent project updates to the enterprise system by the user.

Abstract: A framework for pre-sales marketing professionals who perform engagements, such as business-to-business engagements. Embodiments of the claimed invention optimize early activities in the sales cycle. For each prospective engagement, the embodiments generate a sales kit that assists the salesperson in getting through the “beauty contest” phase of enterprise solutions sales, in which multiple vendors compete for a prospect's business. Embodiments also function as a repository for product teams to showcase products to the company's salespeople. In this way, product teams are enabled to engage salespeople to market their offerings, and salespeople are enabled to quickly prepare for initial meetings with sales prospects.

Abstract: Disclosed herein are technologies for implementing an automated multimedia narrative—presentation to one or more users. In some implementations, the user selects and views an annual presentation report from a presented listing of digital multimedia narratives. Contents of the annual presentation report may be derived from stored information in the database and the derived contents are subsequently mapped to a humanlike—animation for delivery to the one or more users.