Abstract: In an approach for automatically identifying one or more updates in a Scientific Drug Label (SL) relevant to a patient and incorporating the one or more updates into a Patient Drug Label (PL), a processor receives a pair of documents, wherein the pair of documents include the SL and the PL. A processor converts a complex medical language of the SL into a simplified patient friendly language. A processor identifies one or more words, one or more phrases, or one or more sentences that have been modified, inserted, or deleted. A processor searches for a location in the PL that closely maps to the one or more words, the one or more phrases, or the one or more sentences to the SL. A processor incorporates the one or more words, the one or more phrases, or the one or more sentences in a mapped location of the PL.

Abstract: Disclosed is a text sentence processing method performed by a computer device, including: acquiring sample text sentences including entity pairs and relationship labels of the entity pairs; extracting positive and negative example sentence pairs from the sample text sentences according to the relationship labels, and performing positive-negative example sampling, to obtain a training set; inputting the training set into a relationship extraction model to generate loss values; and adjusting parameters of the relationship extraction model according to the loss values, and repeating the step of extracting positive and negative example sentence pairs from the sample text sentences according to the relationship labels to perform iterative training of the relationship extraction model until a training end condition is met, to obtain an updated relationship extraction model, the relationship extraction model being configured to identify an entity relationship of an entity pair in an input text sentence.

Abstract: 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.

Abstract: Implementations relate to mitigating client device latency in rendering of remotely generated automated assistant content. Some of those implementations mitigate client device latency between rendering of multiple instances of output that are each based on content that is responsive to a corresponding automated assistant action of a multiple action request. For example, those implementations can reduce latency between rendering of first output that is based on first content responsive to a first automated assistant action of a multiple action request, and second output that is based on second content responsive to a second automated assistant action of the multiple action request.

Abstract: 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.

Abstract: 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.

Abstract: The technology described in this document can be embodied in a computer-implemented method that includes receiving, at a processing system, a first signal including an output of a speaker device and an additional audio signal. The method also includes determining, by the processing system, based at least in part on a model trained to identify the output of the speaker device, that the additional audio signal corresponds to an utterance of a user. The method further includes initiating a reduction in an audio output level of the speaker device based on determining that the additional audio signal corresponds to the utterance of the user.

Abstract: Compensating a speech signal for the presence of a speaker mask includes receiving a speech signal, dividing the speech signal into subframes, generating speech parameters for a subframe, and determining whether the subframe is suitable for use in detecting a mask. If the subframe is suitable for use in detecting a mask, the speech parameters for the subframe are used in determining whether a mask is present. If a mask is present, the speech parameters for the subframe are modified to produce modified speech parameters that compensate for the presence of the mask.

Abstract: 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.

Abstract: A method including in response to voice information, acquiring a response object to which corresponding voice information is registered and which occupies an interactive medium; and informing the response object to respond to the voice information. The technical solution enables an operating system to automatically determine a response object responding to voice information triggered by a user from numerous response objects in view of voice information registered at each response object and the occupation of interactive media by each response object. The technical solution removes the restriction that a user must speak a wakening phrase to activate a required response object each time before speaking voice information. Thus, a user only needs to speak short voice information that shows the intention for use thereof during a process of using a certain response object, thereby improving the convenience of a user performing voice interaction.

Abstract: An assistance device that assists with the generation of a computer program for controlling operations of a device to be controlled, the assistance device including an output control unit that controls operations of an output unit that outputs an utterance inputted into an input unit that receives an input of an utterance which is information outputted by an output source, wherein, in a case in which information indicating an event that has a possibility of occurring in a human-device conversation, that is, an interaction between the device to be controlled and a user of the device to be controlled, is inputted, the output control unit causes the output unit to output an event indicating the inputted information, and in a case in which information indicating an event that has a possibility of occurring in an (N?1)th place (where N is an integer equal to or greater than 1) during the human-device conversation is inputted into the input unit, the output control unit causes the output unit to output information

Abstract: 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.

Abstract: There is provided a computer implemented method for the automated analysis or use of data, comprising the steps of: (a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; in which the structured, machine-readable representation of data includes a plurality of tenets defining rules, the plurality of tenets including at least one constraint tenet, the at least one constraint tenet preventing a bad action; wherein the plurality of tenets comprise different sets of tenets; (b) using the different sets of tenets in different contexts, e.g. for different types of chatbots, or for different end-users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for handing off a user conversation between computer-implemented agents. One of the methods includes receiving, by a computer-implemented agent specific to a user device, a digital representation of speech encoding an utterance, determining, by the computer-implemented agent, that the utterance specifies a requirement to establish a communication with another computer-implemented agent, and establishing, by the computer-implemented agent, a communication between the other computer-implemented agent and the user device.

Abstract: Disclosed herein are computer-implemented methods for intelligent phrase generation. Example methods include acquiring a bulk data input that includes one or more seed phrases that are requested for derivation, inputting the bulk data input into a model, and returning an arrangement result to a user. The model is configured to determine one or more derivative phrases from each of the seed phrases in the bulk data input, each of the one or more derivative phrases corresponding to a respective seed phrase. The model is configured to determine one or more arrangements with which to arrange each of the derivative phrases in the one or more derivative phrases. The model is configured to determine a characteristic of the respective seed phrase, the one or more arrangement corresponding to the characteristic of the respective seed phrase.

Abstract: Systems and methods are described for a maintaining consistent and reliable outputs from artificial intelligence (“AI”) based search systems that use pipelines with a dataset, AI model, and prompt. An application can send a query through a pipeline and set the result as a baseline for future results. The application can periodically resend the query through the pipeline and compare the new results to the baseline. If the new results vary from the baseline above a predetermined threshold, then corrective measures can be taken. This can include notifying an administrator or querying the pipeline for how to change the prompt so that results are more similar to the baseline.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

Abstract: On-device processor(s) of a client device may store, in on-device storage and in association with a time to live (TTL) in the on-device storage, a correction directed to ASR processing of audio data. The correction may include a portion of a given speech hypothesis that was modified to an alternate speech hypothesis. Further, the on-device processor(s) may cause an on-device ASR model to be personalized based on the correction. Moreover, and based on additional ASR processing of additional audio data, the on-device processor(s) may store, in the on-device storage and in association with an additional TTL in the on-device storage, a pseudo-correction directed to the additional ASR processing. Accordingly, the on-device processor(s) may cause the on-device ASR model to be personalized based on the pseudo-correction to prevent forgetting by the on-device ASR model.

Abstract: 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.