Abstract: Systems are presented for generating a natural language model. The system may comprise a database module, an application program interface (API) module, a background processing module, and an applications module, each stored on the at least one memory and executable by the at least one processor. The system may be configured to generate the natural language model by: ingesting training data, generating a hierarchical data structure, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document, receiving the annotation based on the annotation prompt, and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: Systems are presented for generating a natural language model. The system may comprise a database module, an application program interface (API) module, a background processing module, and an applications module, each stored on the at least one memory and executable by the at least one processor. The system may be configured to generate the natural language model by: ingesting training data, generating a hierarchical data structure, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document, receiving the annotation based on the annotation prompt, and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Abstract: A system for a camera phone user to create a photo gift includes a wireless router configured to receive a data structure from a first camera phone operated by a first user. The data structure defines a picture strip that includes a row of images and has a length-to-width ratio in a range between 2.5 and 6. A server can send a message to a recipient about the creation of picture strip. A printing and finishing facility can produce the picture strip in accordance with the data structure, wherein the picture strip is shipped to the recipient.

Abstract: Described herein are one or more implementations for facilitation of computer software testing. One or more implementations, described herein, determine logical type of one or more test input-parameters based upon metadata placed on a function under test (FUT) of software. Using that determined logical type, an implementation generates data values. In some instances, those generated values are values selected from a repository of data values with associated logical types. The selection is based upon the determined logical type. After generating data values for testing the FUT, an implementation supplies the generated data values as input to the FUT.

Abstract: A computing system for associating one or more attribute tags with one or more executable test cases. A test case is accessed by the computing system. In addition, various attribute tags are also accessed by the computing system. The attribute tags comprise one or more attributes that describe properties of the test case. The attribute tags are then associated with the test case. Thus, the attribute tags permit useful designation of a variety of attributes as being associated with the test case.

Abstract: Described herein are one or more implementations for facilitation of computer software testing. One or more implementations, described herein, determine logical type of one or more test input-parameters based upon metadata placed on a function under test (FUT) of software. Using that determined logical type, an implementation generates data values. In some instances, those generated values are values selected from a repository of data values with associated logical types. The selection is based upon the determined logical type. After generating data values for testing the FUT, an implementation supplies the generated data values as input to the FUT.