Digitized audio command is decoded to generate audio features. An in-domain confidence score is calculated for a model trained by a limited set of peripheral device commands. An out-domain confidence score is calculated for a model trained without the peripheral device commands. The best score determines whether to process the audio locally or at a remote server. In some embodiments, a likelihood ratio (LR) is calculated of the in-domain and out-domain confidence scores. Based on the likelihood ratio, a locally decoded audio command is performed, or the audio features are sent to a remote server for processing to determine the audio command.

Techniques are provided to validate a digitized audio signal that is generated by a conference participant. Reference speech features of the conference participant are obtained, either via samples provided explicitly by the participant, or collected passively via prior conferences. The speech features include one or more of word choices, filler words, common grammatical errors, idioms, common phrases, pace of speech, or other features. The reference speech features are compared to features observed in the digitized audio signal. If the reference speech features are sufficiently similar to the observed speech features, the digitized audio signal is validated and the conference participant is allowed to remain in the conference. If the validation is not successful, a variety of possible actions are taken, including alerting an administrator and/or terminating the participant's attendance in the conference.

Techniques are provided to validate a digitized audio signal that is generated by a conference participant. Reference speech features of the conference participant are obtained, either via samples provided explicitly by the participant, or collected passively via prior conferences. The speech features include one or more of word choices, filler words, common grammatical errors, idioms, common phrases, pace of speech, or other features. The reference speech features are compared to features observed in the digitized audio signal. If the reference speech features are sufficiently similar to the observed speech features, the digitized audio signal is validated and the conference participant is allowed to remain in the conference. If the validation is not successful, a variety of possible actions are taken, including alerting an administrator and/or terminating the participant's attendance in the conference.

Described herein is a neural network accelerator with a set of neural processing units and an instruction set for execution on the neural processing units. The instruction set is a compact instruction set including various compute and data move instructions for implementing a neural network. Among the compute instructions are an instruction for performing a fused operation comprising sequential computations, one of which involves matrix multiplication, and an instruction for performing an elementwise vector operation. The instructions in the instruction set are highly configurable and can handle data elements of variable size. The instructions also implement a synchronization mechanism that allows asynchronous execution of data move and compute operations across different components of the neural network accelerator as well as between multiple instances of the neural network accelerator.

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

The present disclosure is directed to enabling acquisition of information of an argument corresponding to a case. The present disclosure is a language processing apparatus which refers to an argument emergence history database 14 which stores argument emergence patterns associated with cases and arguments of verbs for each meaning of a word or usage of a verb, acquires an argument emergence pattern which matches a verb and a case of the verb included in a request from a user from the argument emergence history database 14, and generates a response to the user using an argument included in the argument emergence pattern acquired from the argument emergence history database 14.

The present disclosure is directed to enabling acquisition of information of an argument corresponding to a case. The present disclosure is a language processing apparatus which refers to an argument emergence history database 14 which stores argument emergence patterns associated with cases and arguments of verbs for each meaning of a word or usage of a verb, acquires an argument emergence pattern which matches a verb and a case of the verb included in a request from a user from the argument emergence history database 14, and generates a response to the user using an argument included in the argument emergence pattern acquired from the argument emergence history database 14.

A method and system are provided. The method includes separating a predicate that specifies a set of events into a temporal part and a non-temporal part. The method further includes comparing the temporal part of the predicate against a predicate of a known window type. The method also includes determining whether the temporal part of the predicate matches the predicate of the known window type. The method additionally includes replacing (i) the non-temporal part of the predicate by a filter, and (ii) the temporal part of the predicate by an instance of the known window type, responsive to the temporal part of the temporal predicate matching the predicate of the known window type. The instance is parameterized with substitutions used to match the temporal part of the predicate to the predicate of the known window type.

A method and system are provided. The method includes separating a predicate that specifies a set of events into a temporal part and a non-temporal part. The method further includes comparing the temporal part of the predicate against a predicate of a known window type. The method also includes determining whether the temporal part of the predicate matches the predicate of the known window type. The method additionally includes replacing (i) the non-temporal part of the predicate by a filter, and (ii) the temporal part of the predicate by an instance of the known window type, responsive to the temporal part of the temporal predicate matching the predicate of the known window type. The instance is parameterized with substitutions used to match the temporal part of the predicate to the predicate of the known window type.