Abstract: A data processing system includes a processor; and a memory in communication with the processor. The memory contains executable instructions that, when executed by the processor alone or in combination with other processors, cause the data processing system to perform functions of: detecting column headers within an unstructured text using a trained classifier; prompting a Large Language Model (LLM) to produce a table sketch based on detected headers and the unstructured text; generating candidate rows for lines of the unstructured text not included in the table sketch using a symbolic system; ranking the candidate rows based on consistency with a consistency ranker; and assembling a final table based on the unstructured text by adding candidate rows based on rank to the table sketch.

Abstract: Some embodiments automatically generate data processing rules based on positive examples of processed data, e.g., formatting rules based on formatted data, filtering rules based on filtered data, or validating rules based on valid data. Some embodiments also use negative examples, e.g., unformatted data. A machine learning rule generation architecture includes a predicate generator, a cell cluster creator, a rule enumerator, and in some versions a rule ranker. Formatting rules written by a user are replaced by simpler autogenerated rules. Spreadsheet formatting rule functionality is enhanced, and surfaced in a user interface.

Abstract: A data processing system implements extracting symbolic property information from a training dataset by analyzing the training dataset with a symbolic property mining pipeline to extract properties of program code from one-shot program code examples, the symbolic property information indicative of types of properties of the one-shot program code examples determined to improve program code output by a large language model (LLM) in response to natural language utterances; and training a property recognition model to recognize symbolic properties associated with a natural language utterance using the training dataset and the symbolic property information, the property recognition model being configured to analyze the natural language utterance and to output the symbolic properties of program code.

Abstract: A code generating system is configured to receive a code description prompt which includes a natural language description of a code snippet for the code generating system to generate via a user interface. The system encodes the prompt to generate a code description embedding that embeds the natural language prompt into a continuous space using an encoder. A noisy code snippet embedding is then iteratively denoised conditioned on the code description embedding to generate a denoised code snippet embedding. The denoised code snippet embedding is decoded using a pre-trained decoder having full self-attention and cross-attention with the code snippet embedding to generate a final predicted code snippet embedding. The final predicted code snippet embedding is then projected to discrete code tokens using a classification head that computes a probability distribution over code tokens and selects code tokens for a predicted code snippet based on the probability distribution.

Abstract: A multimodal assistant system receives a multimodal input that includes an input image and a natural language task description which describes a structured task to perform based on the input image. The system generates a visual Chain-of-Thought (v-CoT) prompt by combining the input image, the natural language task description, and a v-CoT instruction, the v-CoT instruction defining a series of steps for the MLLM to take in generating an output. The series of steps includes instructing the MLLM to describe relevant information derived from the image and relevant image artifacts required to generate the output, instructing the MLLM to reason over the relevant information and relevant image artifacts to determine a solution for the task, and including the solution in the output.

Abstract: Some embodiments perform fully unsupervised string error detection and repair, for strings containing both syntactic errors and semantic errors. In some embodiments, string error mitigation includes learning a regular-expression-based pattern that characterizes at least a predefined fraction of a set of string data values, identifying and masking a semantic substring, deriving edits to repair a string which contains the semantic substring, and replacing a mask value with a replacement substring that conforms to the pattern. Some embodiments utilize a language model. Some embodiments include or utilize a pattern learner, one or more learned significant patterns, and a repair engine. Some embodiments exploit program execution information to generate patterns when significant patterns are not initially found.

Abstract: Some embodiments automatically generate data processing rules based on positive examples of processed data, e.g., formatting rules based on formatted data, filtering rules based on filtered data, or validating rules based on valid data. Some embodiments also use negative examples, e.g., unformatted data. A machine learning rule generation architecture includes a predicate generator, a cell cluster creator, a rule enumerator, and in some versions a rule ranker. Formatting rules written by a user are replaced by simpler autogenerated rules. Spreadsheet formatting rule functionality is enhanced, and surfaced in a user interface.

Abstract: A biocompatible intravaginal device adapted for the delivery of therapeutic active ingredients (agents) is provided. The device is constructed to be insertable and remain in the vagina for up to a month (28 to 31 days) or more. The device is loaded with the active agents prior to insertion and constructed to continuously release a therapeutically effective amount of the agents during the duration of up to a one month insertion. Preferred agents include contraceptive and anti-infection agents. It is preferred that the agents are non-hormonal.

Abstract: The present invention relates to a non-hormonal, biocompatible, and biodegradable intravaginal device for the delivery of spermiostatic, spermicidal and anti-infectious agents. The present invention also relates to methods of contraception using such a device, as well as the prevention and treatment of sexually transmitted diseases and vaginal infections through the application of the device.

Abstract: The present invention relates to a non-hormonal, biocompatible, and biodegradable intravaginal device for the delivery of spermiostatic, spermicidal and anti-infectious agents. The present invention also relates to methods of contraception using such a device, as well as the prevention and treatment of sexually transmitted diseases and vaginal infections through the application of the device.

Abstract: The present invention relates to a non-hormonal, biocompatible, and biodegradable intravaginal device for the delivery of spermiostatic, spermicidal and anti-infectious agents. The present invention also relates to methods of contraception using such a device, as well as the prevention and treatment of sexually transmitted diseases and vaginal infections through the application of the device.