Abstract: In an embodiment, a component includes a lead frame, a semiconductor chip and a housing body, wherein the lead frame has a first subregion and a second subregion laterally spaced from the first subregion, wherein the housing body laterally encloses the first subregion and the second subregion and thereby mechanically connects the first subregion to the second subregion, wherein the semiconductor chip is arranged on a mounting surface of the first subregion and is electrically conductively connected to the subregions of the lead frame, wherein the first subregion has a first local elevation, which vertically projects beyond at least one edge region of the first subregion, and, in top view of the mounting surface, at least partially surrounds the semiconductor chip, and wherein, in the top view of the mounting surface, the housing body completely covers the first local elevation and does not cover the semiconductor chip.

Abstract: The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training a digital assistant with expert knowledge. An organization or individual utilizing a suite of software applications with a harmonized user experience may employ a digital assistant within the user experience to assist users when problems arise. The digital assistant may field questions and requests in informal and unstructured speech and text, analyze this natural language, and determine an appropriate response. The digital assistant may utilize existing suitable knowledge bases and leverage subject-matter experts. The digital assistant may learn from the expert's response to the user in order to apply the knowledge to a similar question or request in the future.

Abstract: The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.

Abstract: The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.

Abstract: Disclosed herein are system, method, and computer program product embodiments for encoding symbolic data into a subsymbolic format while preserving the semantic arrangement of the symbolic data. In an embodiment, to encode the symbolic data, a subsymbolic encoder system may convert a symbolic graph into a tuple representation having tuple elements corresponding to the nodes of the symbolic graph. The subsymbolic encoder system may retrieve a dictionary identification for each tuple element and calculate a subsymbolic value for each tuple element using an exponential component. The subsymbolic encoder system may standardize the length of the subsymbolic values and/or add a weighted relationship indicator to the subsymbolic values. The subsymbolic encoder system may transmit the subsymbolic values to a subsymbolic intelligence system.

Abstract: A method and apparatus for determining relationships between objects in a meta-model semantic network is described. A contextual network graph comprising nodes and edges representing semantic objects and semantic relationships is generated from a meta-model of business objects from the meta-model semantic network. The contextual network graph is used to generate a unique identifier for each node and associated edge. The unique identifiers are used to compute information of cost and energy between the nodes. The information is stored in a meta-model semantic network database.

Abstract: The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training a digital assistant with expert knowledge. An organization or individual utilizing a suite of software applications with a harmonized user experience may employ a digital assistant within the user experience to assist users when problems arise. The digital assistant may field questions and requests in informal and unstructured speech and text, analyze this natural language, and determine an appropriate response. The digital assistant may utilize existing suitable knowledge bases and leverage subject-matter experts. The digital assistant may learn from the expert's response to the user in order to apply the knowledge to a similar question or request in the future.

Abstract: Disclosed herein are system, method, and computer program product embodiments for encoding symbolic data into a subsymbolic format while preserving the semantic arrangement of the symbolic data. In an embodiment, to encode the symbolic data, a subsymbolic encoder system may convert a symbolic graph into a tuple representation having tuple elements corresponding to the nodes of the symbolic graph. The subsymbolic encoder system may retrieve a dictionary identification for each tuple element and calculate a subsymbolic value for each tuple element using an exponential component. The subsymbolic encoder system may standardize the length of the subsymbolic values and/or add a weighted relationship indicator to the subsymbolic values. The subsymbolic encoder system may transmit the subsymbolic values to a subsymbolic intelligence system.

Abstract: A method is provided for operating a navigation system of a hybrid motor vehicle having a combustion engine and an electric motor that is fed from an associated battery. In this method, an efficiency route enabling a maximum utilization of the electric motor is determined by the navigation system, where at least one item of efficiency information is considered in the determination of the efficiency route. The efficiency information is provided dynamically by a drive control device of the motor vehicle and is dependent on the current operating state and/or the current operating prediction.

Abstract: In an embodiment, a method is provided for utilizing a meta-model semantic network. In this method, a meta-model of the enterprise data is obtained. The meta-model provides semantic information regarding a definition of a business object. The meta-model is then used to generate a rule definition that maps enterprise data to a semantic object definition and a semantic relation definition. With the rule definition, embodiments may then generate a semantic object and a semantic relation from data extracted from the enterprise data. The semantic object and semantic relation are stored in the meta-model semantic network.

Abstract: A method is provided for operating a navigation system of a hybrid motor vehicle having a combustion engine and an electric motor that is fed from an associated battery. In this method, an efficiency route enabling a maximum utilization of the electric motor is determined by the navigation system, where at least one item of efficiency information is considered in the determination of the efficiency route. The efficiency information is provided dynamically by a drive control device of the motor vehicle and is dependent on the current operating state and/or the current operating prediction.

Abstract: A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network.

Abstract: A method and apparatus for detection of relationships between objects in a meta-model semantic network is described. Semantic objects and semantic relations of a meta-model of business objects are generated from a meta-model semantic network. The semantic relations are based on connections between the semantic objects. A probability model of terminology usage in the semantic objects and the semantic relations is generated. A neural network is formed based on usage of the semantic objects, the semantic relations, and the probability model. The neural network is integrated with the semantic objects, the semantic relations, and the probability model to generate a contextual network. The generated probability model is integrated with semantic objects and neural networks for form parallel networks.

Abstract: Operations to represent a computer system state to a user include maintaining a model in a computer system. The model is updated at times and represents a current state of the computer system. The model uses formal-language statements to associate each of several predefined goals with at least one of several predefined actions that can be performed in the computer system to accomplish the associated predefined goal. The operations comprise providing an output to a user regarding the current state of the computer system, the output comprising a natural-language statement generated using at least one of the formal-language statements. A statement generating module may include a text planner, a grammar, a lexicon, a translator or a text post-processor.

Abstract: Operations to manage statements relating to a computer system state include obtaining, in a computer system and from any of several sources, statements that relate to a current state of the computer system. Each statement comprises several elements. The operations comprise assigning focus values to the elements of each statement, each focus value indicating a relevance of the element to the current state of the computer system. The operations comprise identifying a subset of the statements based on the focus values. The operations comprise making a first output to a user, the first output being selected from several predefined outputs using the subset of the statements. The output may represent the current state to the user or it may present at least one of several predefined goals to the user for selection, the at least one goal being identified as being possible to accomplish based on the current state.

Abstract: An input handler may receive natural language input associated with a command from a user through a user interface, and a language parser may parse the natural language input to determine parsed natural language input. A context monitor may receive context information associated with the user, and a context parser may parse the context information to obtain parsed context information associated with the natural language input and with the command. A command interpreter may interpret the parsed natural language input, using the parsed context information, to thereby determine the command.

Abstract: An input handler may receive natural language input associated with a command from a user through a user interface, and a language parser may parse the natural language input to determine parsed natural language input. A context monitor may receive context information associated with the user, and a context parser may parse the context information to obtain parsed context information associated with the natural language input and with the command. A command interpreter may interpret the parsed natural language input, using the parsed context information, to thereby determine the command.

Abstract: User interaction with a computer system is supported by maintaining, in a computer system, a model comprising several concepts having properties associated therewith and any relationships existing between the several concepts. A current state of the several concepts, properties and relationships reflects a real-world situation that involves a user of the computer system. An output is made to the user that identifies at least a subset of several predefined goals, the model indicating the subset as being possible to accomplish based on the current state. At least a first action of several predefined actions in the computer system is performed, the model associating the several predefined actions with accomplishing respective ones of the several predefined goals. The first action is identified as associated with accomplishing a first goal that the user selects from the subset. The current state is updated based on performing at least the first action.