Abstract: System and method for displaying a user interface of an evaluation system configured to evaluate predicted answers generated by a machine learning system. For example, the method includes receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system. In response to determining the confidence level being larger than or equal to a predetermined confidence threshold, the predicted answer and a reference is stored in a storage for retrieval and display. The reference indicates a location of the text object in the textual data. In response to determining the confidence level being smaller than the predetermined confidence threshold, the question and the text object associated with the question is displayed.

Abstract: In one embodiment, a method for adaptive training of a machine learning system configured to predict answers to questions associated with textual data includes receiving predicted answers to questions associated with textual data. The predicted answers are generated based at least in part on one or more first models of a machine learning system. The one or more first models are associated with a first accuracy score. The method further includes determining based at least in part on a quality control parameter whether an evaluation of the questions by one or more external entities is required. In response to determining based at least in part on the quality control parameter that an evaluation of the questions by one or more external entities is required, the questions associated with the textual data and the textual data are sent to the one or more external entities for evaluation.

Abstract: System and method for displaying a user interface of an evaluation system configured to evaluate predicted answers generated by a machine learning system. For example, the method includes receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system. In response to determining the confidence level being larger than or equal to a predetermined confidence threshold, the predicted answer and a reference is stored in a storage for retrieval and display. The reference indicates a location of the text object in the textual data. In response to determining the confidence level being smaller than the predetermined confidence threshold, the question and the text object associated with the question is displayed.

Abstract: A product applicator assembly includes a handle, an applicator, and a product dispensing assembly. The product dispensing assembly is disposed at least in part within the handle and stores a liquid product within a product container. A puncturing device within the product dispensing assembly has a base and a puncture element extending from the base toward the product container. The puncturing device further includes a mating member extending from the base and secured to the product container. The puncture element facilitates rupturing the product container to release a portion of the liquid product into the applicator.

Abstract: In one embodiment, a method for adaptive training of a machine learning system configured to predict answers to questions associated with textual data includes receiving predicted answers to questions associated with textual data. The predicted answers are generated based at least in part on one or more first models of a machine learning system. The one or more first models are associated with a first accuracy score. The method further includes determining based at least in part on a quality control parameter whether an evaluation of the questions by one or more external entities is required. In response to determining based at least in part on the quality control parameter that an evaluation of the questions by one or more external entities is required, the questions associated with the textual data and the textual data are sent to the one or more external entities for evaluation.

Abstract: Systems and methods include receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system based at least in part on one or more models of the machine learning system and the textual data. In response to determining the confidence level being larger than or equal to a predetermined confidence threshold, the predicted answer and a reference is stored in a storage for retrieval and display. The reference indicates a location of the text object in the textual data. In response to determining the confidence level being smaller than the predetermined confidence threshold, the question and the text object associated with the question is displayed, at the user interface, to a user for inputting a true answer.

Abstract: System and method for adaptive training of a machine learning system processing textual data. For example, a method for adaptive training of a machine learning system configured to predict answers to questions associated with textual data includes receiving predicted answers to questions associated with textual data. The predicted answers are generated based at least in part on one or more first models of a machine learning system. The one or more first models are associated with a first accuracy score. The method further includes determining based at least in part on a quality control parameter whether an evaluation of the questions by one or more external entities is required. In response to determining based at least in part on the quality control parameter that an evaluation of the questions by one or more external entities is required, the questions associated with the textual data and the textual data are sent to the one or more external entities for evaluation.

Abstract: Systems and methods for generating logical documents for a document evaluation system are provided. For example, a method for generating logical documents for a document evaluation system includes receiving a first child document associated with a master document. The master document includes one or more master terms. The first child document includes one or more first child terms and a first date. The method further includes generating a logical document based at least in part on the master document. The logical document includes one or more current document values. Each current document value corresponds to one master term and is associated with a current date and a current reference. The current reference identifies a region in the master document related to the one master term. The method further includes selecting a first child term from the one or more first child terms.

Abstract: In an example embodiment, a product applicator assembly is provided. The assembly comprises a housing having a porous portion. The housing defines a hollow interior bounded at least in part by the porous portion. The assembly further comprises a sealed product container positioned within the hollow interior. The product container contains a spreadable product therein. The housing is at least partially compressible. At least a portion of the housing causes the sealed product container to burst and to release at least a portion of the spreadable product into the porous portion of the housing.

Abstract: In an example embodiment, a product applicator assembly is provided. The assembly comprises a housing having a porous portion. The housing defines a hollow interior bounded at least in part by the porous portion. The assembly further comprises a sealed product container positioned within the hollow interior. The product container contains a spreadable product therein. The housing is at least partially compressible. At least a portion of the housing causes the sealed product container to burst and to release at least a portion of the spreadable product into the porous portion of the housing.

Abstract: System and method for adaptive training of a machine learning system processing textual data. For example, a method for adaptive training of a machine learning system configured to predict answers to questions associated with textual data includes receiving predicted answers to questions associated with textual data. The predicted answers are generated based at least in part on one or more first models of a machine learning system. The one or more first models are associated with a first accuracy score. The method further includes determining based at least in part on a quality control parameter whether an evaluation of the questions by one or more external entities is required. In response to determining based at least in part on the quality control parameter that an evaluation of the questions by one or more external entities is required, the questions associated with the textual data and the textual data are sent to the one or more external entities for evaluation.

Abstract: System and method for dynamically displaying a user interface of an evaluation system configured to evaluate predicted answers generated by a machine learning system. For example, the method includes receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system based at least in part on one or more models of the machine learning system and the textual data. In response to determining the confidence level being larger than or equal to a predetermined confidence threshold, the predicted answer and a reference is stored in a storage for retrieval and display. The reference indicates a location of the text object in the textual data.

Abstract: Systems and methods for generating logical documents for a document evaluation system are provided. For example, a method for generating logical documents for a document evaluation system includes receiving a first child document associated with a master document. The master document includes one or more master terms. The first child document includes one or more first child terms and a first date. The method further includes generating a logical document based at least in part on the master document. The logical document includes one or more current document values. Each current document value corresponds to one master term and is associated with a current date and a current reference. The current reference identifies a region in the master document related to the one master term. The method further includes selecting a first child term from the one or more first child terms.

Abstract: In an example embodiment, a product applicator assembly is provided. The assembly comprises a housing having a porous portion. The housing defines a hollow interior bounded at least in part by the porous portion. The assembly further comprises a sealed product container positioned within the hollow interior. The product container contains a spreadable product therein. The housing is at least partially compressible. At least a portion of the housing causes the sealed product container to burst and to release at least a portion of the spreadable product into the porous portion of the housing.

Abstract: A system and method for performing searches may include storing data available for searching. In response to receiving a search request includes a “what” search parameter and a “with” search parameter from a user, the data using the “what” and “with” search parameters may be searched, and search results may be generated. The search results may be returned to a user who submitted the search request.

Abstract: A method and apparatus are provided for steaming and blending food products such as baby food. The apparatus includes a base including a steam chamber and a drive shaft. A container assembly can be mounted to the base in an inverted position for steaming the food and in an upright position for blending the food. The apparatus may further include a baby bottle sterilizing unit and a bottle warmer.

Abstract: A method and apparatus are provided for steaming and blending food products such as baby food. The apparatus includes a base including a steam chamber and a drive shaft. A container assembly can be mounted to the base in an inverted position for steaming the food and in an upright position for blending the food. The apparatus may further include a baby bottle sterilizing unit and a bottle warmer.

Abstract: A method and apparatus are provided for steaming and blending food products such as baby food. The apparatus includes a base including a steam chamber and a drive shaft. A container assembly can be mounted to the base in an inverted position for steaming the food and in an upright position for blending the food. The apparatus may further include a baby bottle sterilizing unit and a bottle warmer.

Abstract: A method of forming a poly(arylene ether)-containing solid concentrate is described. The method includes blending a poly(arylene ether), a thermosetting resin, and a compatibilizing agent in the presence of a solvent to form a homogeneous solution, and removing the solvent to yield the solid concentrate. The solid concentrate is useful for preparing curable conditions.

Abstract: A connector (30) can include a housing (32), a plurality of magnets (14) within the housing used for data transfer and at least one alignment magnet (16 or 18) within the housing used for proper alignment and polarity. The connector can also include at least one magnetic induction circuit (52) coupled to at least one of the plurality of magnets. The plurality of magnets can include a plurality of inductor elements or micro-metric inductor elements. The magnetic induction circuit can be a magnetic induction circuit using Gaussian Minimum Shift Keying modulation. A connector (64) can further include an inductive coil (67) operating at a lower frequency than the at least one magnetic induction circuit. The inductive coil can enable contactless energy transfer from the connector to an energy storage device (69) operatively coupled to an electronic product (62).