An information processing apparatus includes: an acquisition unit configured to acquire learning data containing a plurality of items, the learning data being assigned a first level and a second level whose threat level is higher than the first level; a first feature value detection unit configured to detect a first feature value of a specific character string from learning data belonging to the first level; a second feature value detection unit configured to detect a second feature value of the specific character string from learning data belonging to the second level; a difference detection unit configured to detect a difference between the first and second feature values; and a selection unit configured to select, when there is the difference, learning data of an item to which the specific character string belongs.

The present invention relates to an AI-based voice ordering system and a method therefor and provides a voice ordering method and system, the voice ordering method comprising: a first step of an ordering smart terminal standing by for voice data reception; a second step of the ordering smart terminal analyzing whether an input signal has been received by an input unit corresponding to a microphone activation button; and a third step of, if the analysis result indicates that an input signal has not been received, returning to the first step and, conversely, if an input signal has been received, the ordering smart terminal receiving a voice signal from a microphone, converting the voice signal into voice data of a preset format, and then transmitting the converted voice data to a voice ordering server via a host terminal connected to a network, so that analysis of text data is performed.

A method of document processing is provided. An implementation solution is: obtaining target text information and target layout information of a target document, the target text information includes target text included in the target document and character position information of the target text, and the target layout information is used to characterize the region where text in the target document is located; fusing the target text information and the target layout information to obtain first multimodal information of the target document; and inputting the first multimodal information into an intelligent document comprehension model, and obtaining at least one target word in the target document and at least one feature vector corresponding to the at least one target word output by the intelligent document comprehension model, each target word is related to semantics of the target document.

A method of document processing is provided. An implementation solution is: obtaining target text information and target layout information of a target document, the target text information includes target text included in the target document and character position information of the target text, and the target layout information is used to characterize the region where text in the target document is located; fusing the target text information and the target layout information to obtain first multimodal information of the target document; and inputting the first multimodal information into an intelligent document comprehension model, and obtaining at least one target word in the target document and at least one feature vector corresponding to the at least one target word output by the intelligent document comprehension model, each target word is related to semantics of the target document.

The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to predict a type of data that one or more numerical characters and/or one or more natural language word characters of a document correspond to. For instance, a Question Answering systems can be used to predict that a particular number value corresponds to a date, a billing amount, a page number, or the like.

The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to predict a type of data that one or more numerical characters and/or one or more natural language word characters of a document correspond to. For instance, a Question Answering systems can be used to predict that a particular number value corresponds to a date, a billing amount, a page number, or the like.

An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

An electronic device is provided. The electronic device includes processor, and a memory that stores instructions. The instructions, when executed by the processor, cause the electronic device to receive a user input, to identify a natural language input corresponding to the user input, to identify a first natural language output corresponding to the natural language input, to identify at least one specified word from at least one word included in the first natural language output, to identify a second natural language output based on a fact that the at least one specified word is identified, and to output the second natural language output such that the second natural language output is provided to a user.

An electronic device is provided. The electronic device includes processor, and a memory that stores instructions. The instructions, when executed by the processor, cause the electronic device to receive a user input, to identify a natural language input corresponding to the user input, to identify a first natural language output corresponding to the natural language input, to identify at least one specified word from at least one word included in the first natural language output, to identify a second natural language output based on a fact that the at least one specified word is identified, and to output the second natural language output such that the second natural language output is provided to a user.

Systems and methods thereof, of generating a unique identification code for an industrial commodity. The method includes receiving a user query indicative of at least one constructional and operational characteristic of the commodity, inspecting the user query to determine whether the user query is complete for identification of the commodity, updating the user query based on the inspection, identifying at least one attribute of the commodity from the updated user query, based on a list of predefined attributes of the commodity, mapping the at least one attribute to at least one of predefined attribute types, predefined regional standards, predefined commodity rules, and predefined commodity types, and generating the unique identification code for the commodity, based on the mapping. The predefined attribute types may be a predefined commodity group and a predefined commodity part.