Robert Boschgmbh

Robert Bosch Gmbh

HyeongSik Kim

Pongtep Angkititrakul

Xiaoyang Gao

Zhengyu Zhou

Jun Araki

Xiaowei Zhou

Naoki Otani

Amir Hossein Yazdavar

Sola Shirai

A computer-implemented method for identifying substitutions for procedural instructions include parsing a procedural instruction to identify object-action tuples pertaining to the steps of the instruction, and generating a flow graph for the procedural instruction using the identified object-action tuples. The flow graph is then processed using a knowledge graph embedding model to calculate embeddings for the intermediate nodes and the result node, respectively, of the flow graph based on triples that include a head entity, a relation and a tail entity, wherein the head entity for a given intermediate nodes or the result node comprises an aggregation of the input nodes connected to the given intermediate node or the result node. A substitution for nodes in the flow graph may then be identified by calculating new embeddings for the result node based on the substitution and determining a similar between the original embeddings and the new embeddings.

System and method for identifying substitutable entities in procedural instructions

A speech recognition system includes a microphone configured to receive a speech input from one or more users and a processor in communication with the microphone. The processor is programmed to parse a plurality of candidate speech recognition results from a speech input from a user, extract one or more entities from the plurality of candidate speech recognition results, based on natural language understanding (NLU) information, select and retrieve data indicative of information of the one or more entities from a remote server, update a personal knowledge graph associated with a user profile of the user utilizing the data indicative of information of the one or more entities, and output a response to the speech input utilizing the user profile.

System and method for a personalized dialogue system using knowledge-based entity services

Systems and methods for aligning knowledge graphs. A subgraph type is assigned to each node of a plurality of nodes in a first knowledge graph and a second knowledge graph. The assigned subgraph type for each node is determined based on the labels of a plurality of edges and/or other nodes coupled to the node in the knowledge graph. An initial matching is performed to identify a plurality of candidate node-node pairs each including one node from each knowledge graph. A candidate node-node pair is identified as a valid node-node mapping based at least in part on a determination that the subgraph type combination of the candidate node-node pair matches a subgraph type combination of another candidate node-node pair that has previously been confirmed as a valid node-node mapping. In some implementations, a machine-learning model is trained to use the aligned knowledge graphs.

Aligning knowledge graphs using subgraph typing

A computer-implemented system and method relate to natural language processing and knowledge representation and reasoning. A first dataset is created that includes input data and situational data. The situational data provides context for the input data. An encoder is configured to generate an encoded representation of the first dataset. The encoder includes at least an encoding network of a first pre-trained generative machine learning model, which relates to a generative knowledge graph. A decoder includes a decoding network of a second pre-trained generative machine learning model. The decoder is configured to generate response data based on the first dataset by decoding the encoded representation. The decoder is also configured to generate event-centric knowledge based on the first dataset by decoding the encoded representation. The input data and the response data are connected to the same event-centric knowledge via the generative knowledge graph. For example, the event-centric knowledge includes goal data, which is inferred from the input data and the situational data.

System and method with neural representation of event-centric commonsense knowledge for response selection

A smart mask includes a main body having a back frame and a front cover. The back frame and the front cover each include an opening that is aligned with the mask wearer's mouth when worn. The front cover and back frame may be detachable from one another, or a single piece. A microphone is provided in the main body, as well as a speaker. A processor located in the main body is connected to the microphone and the speaker, and is configured to enhance the speech of the mask wearer. In particular, the processor receives audio signals representing a transformation of a spoken utterance of the wearer, processes the audio signals to enhance the speech, and then outputs the enhanced speech to the speaker. This helps other people better understand what the mask wearer is saying.

Smart mask and smart mask system

Workflows stored in a workflow database are annotated, each of the workflows including an ordered sequence of steps. Sub-workflows are extracted from the workflows by splitting the workflows into sub-workflows at annotations that exist between the steps, a first of the sub-workflows including the respective annotation as an output of the first of the sub-workflows, a second of the sub-workflows including the respective annotation as an input to the second of the sub-workflows. Annotations in common between the sub-workflows are identified, including identifying beginning sub-workflows that include the respective annotation as an output and identifying ending sub-workflows that include the respective annotation as an input. The sub-workflows are recombined into new workflows by splicing together the sub-workflows at the annotations in common, such that each new workflow is formed from one of the beginning sub-workflows followed by one of the ending sub-workflows.

On-demand workflow combination and variant generation

Systems and methods for detecting speech activity. The system includes an audio source and an electronic processor. The electronic processor is configured to receive a first audio signal from the audio source, buffer the first audio signal, add random noise to the buffered first audio signal, and filter the first audio stream to create a filtered signal. The electronic processor then determines a signal entropy of each frame of the filtered signal, determines an average signal entropy of a first plurality of frames of the filtered signal occurring at a beginning of the filtered signal, and compares the signal entropy of each frame of the filtered signal to the average signal entropy. Based on the comparison, the electronic processor determines a first speech endpoint located in a first frame of the filtered signal.

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HyeongSik Kim