A method is provided for developing a software product in a no-code development platform (NCDP) to address a problem related to a business domain. The method includes designing the software product in the NCDP. This includes receiving design instructions composed in a semi-structured grammar of a domain-driven design (DDD) natural language for the business domain, and generating a metamodel from the design instructions and according to the DDD natural language. The metamodel incorporates logic and data to implement software features, and the software product has an event sourcing architectural pattern in which the logic in the form of aggregate services are invoked by commands, and the data as events that describe changes in state of the software product are generated. The method includes automatically implementing the aggregate services, commands, and events to build the software product from the metamodel, using an event sourcing framework as a programming model.

A method is provided for developing a software product in a no-code development platform (NCDP) to address a problem related to a business domain. The method includes designing the software product in the NCDP. This includes receiving design instructions composed in a semi-structured grammar of a domain-driven design (DDD) natural language for the business domain, and generating a metamodel from the design instructions and according to the DDD natural language. The metamodel incorporates logic and data to implement software features, and the software product has an event sourcing architectural pattern in which the logic in the form of aggregate services are invoked by commands, and the data as events that describe changes in state of the software product are generated. The method includes automatically implementing the aggregate services, commands, and events to build the software product from the metamodel, using an event sourcing framework as a programming model.

Systems, methods, devices, and non-transitory, computer-readable storage media are disclosed for matching a service requester with a service provider via a taxonomy based directed graph. The method includes: receiving a keyword associated with a service; accessing a directed graph including a root node and nodes connected by edges, each node having a title; identifying a second node of the directed graph for each of service providers, each second node having a title matching a skill of a respective service provider; determining a distance between the first node and each second node along the directed graph; and ranking the service providers based at least in part on the distance between the first node and each second node. Systems, methods, devices, and non-transitory, computer-readable storage media are further disclosed for determining and storing a quality score for the revised linguistic content.

Systems and methods for doing presenting an avatar that speaks sign language based on sentiment of a speaker is disclosed herein. A translation application running on a device receives a content item comprising a video and an audio, wherein the audio comprises a first plurality of spoken words in a first language. The video comprises a character speaking the first plurality of spoken words in the first language. The translation application translates the first plurality of spoken words of the first language into a first sign of a first sign language. The translation application determines an emotional state expressed by the character based on sentiment analysis. The translation application generates an avatar that speaks the first sign of the first sign language where the avatar exhibits the determined emotional state. The content item and the avatar are presented for display on the device.

Systems and methods for doing presenting an avatar that speaks sign language based on sentiment of a speaker is disclosed herein. A translation application running on a device receives a content item comprising a video and an audio, wherein the audio comprises a first plurality of spoken words in a first language. The video comprises a character speaking the first plurality of spoken words in the first language. The translation application translates the first plurality of spoken words of the first language into a first sign of a first sign language. The translation application determines an emotional state expressed by the character based on sentiment analysis. The translation application generates an avatar that speaks the first sign of the first sign language where the avatar exhibits the determined emotional state. The content item and the avatar are presented for display on the device.

A translator pen and a translation method using same are provided. The translator pen includes a housing and a pen head. The housing forms a first accommodation cavity adapted to accommodate an image collector. An outer contour of the pen head is formed into a tapered shape, an outer diameter of the pen head gradually decreases along a direction of the pen head away from the housing and forms a second accommodation cavity adapted to accommodate a pen core. At least a portion of the end of the housing, which does not form the pen head, forms an inclined surface. The inclined surface is inclined relative to the outer contour of the pen head toward an outside of the housing along a direction of the housing away from the pen head. The image collector is configured to face the inclined surface.

A translator pen and a translation method using same are provided. The translator pen includes a housing and a pen head. The housing forms a first accommodation cavity adapted to accommodate an image collector. An outer contour of the pen head is formed into a tapered shape, an outer diameter of the pen head gradually decreases along a direction of the pen head away from the housing and forms a second accommodation cavity adapted to accommodate a pen core. At least a portion of the end of the housing, which does not form the pen head, forms an inclined surface. The inclined surface is inclined relative to the outer contour of the pen head toward an outside of the housing along a direction of the housing away from the pen head. The image collector is configured to face the inclined surface.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A data processing system implements obtaining first textual content in a first language from a first client device; determining that the first language is supported by a first machine learning model; obtaining a guard list of prohibited terms associated with the first language; determining that the textual content does not include one or more prohibited terms associated based on the guard list; providing the first textual content as an input to the first machine learning model responsive to the textual content not including the one or more prohibited terms; analyzing the first textual content with the first machine learning model to obtain a first content recommendation; obtaining a first content recommendation policy that identifies content associated with the first language that may not be provided as a content recommendation; determining that the first content recommendation is not prohibited; and providing the first content recommendation to the first client device.

A data processing system implements obtaining first textual content in a first language from a first client device; determining that the first language is supported by a first machine learning model; obtaining a guard list of prohibited terms associated with the first language; determining that the textual content does not include one or more prohibited terms associated based on the guard list; providing the first textual content as an input to the first machine learning model responsive to the textual content not including the one or more prohibited terms; analyzing the first textual content with the first machine learning model to obtain a first content recommendation; obtaining a first content recommendation policy that identifies content associated with the first language that may not be provided as a content recommendation; determining that the first content recommendation is not prohibited; and providing the first content recommendation to the first client device.