Abstract: A catheter body has a lubricious polymeric inner liner and a composite wall. The composite wall includes a reinforcement structure and an outer jacket. The reinforcement structure has at least two layers of metallic wires, with each layer of metallic wires wound in helical patterns, with the first layer of metallic wires wound around the inner liner and the second layer of metallic wires wound around the first layer of metallic wires, and so on. The outer jacket that is comprised of a distal jacket material that is a soft elastomer of hardness 60 Shore A or lower.

Abstract: Sorbent materials are described that have enhanced performance in removing chlorine and chloramine, among other toxic compounds. The sorbent materials are formed by a process which includes steps of oxidation, adding a nitrogen-containing compound, and calcining the sorbent. The processes of forming the sorbent materials are also disclosed. The sorbent materials have excellent performance as measured by a chloramine and/or chlorine destruction number, and the sorbents retain a high nitrogen edge concentration. The sorbent materials may also be incorporated into devices such as filter assemblies.

Abstract: Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

Abstract: Relevance scores may be determined based on text included in a document. The text may be divided into a text portions, with the relevance scores being determined based on a comparison of a text portion of the plurality of text portions with a criterion specified in natural language. A subset of the plurality of text portions may be selected based on the plurality of relevance scores, with each of the subset of the plurality of text portions having a relevance score surpassing a threshold. A criteria evaluation prompt may be sent to a remote text generation modeling system via a communication interface. The criteria evaluation prompts may include an instruction to evaluate one or more of the subset of text portions against the criterion.

Abstract: A query request identifying a query and a plurality of text portions for determining an answer to the query may be received. Relevance scores corresponding with respective ones of the text portions may be determined based on application of one or more machine learning models to the respective text portion and the query. A subset of the text portions may be selected based on the relevance scores. A response message to the query request including an answer to the query in natural language text generated by a large language model based on the first subset of text portions may be determined.

Abstract: A first set of text generation prompts may be determined based on an input document and a first text generation prompt template. The first text generation prompts may include an instruction to identify factual assertions in the input text. The prompt may be sent to a remote text generation modeling system, which may respond by identifying factual assertions in the input text. A second text generation prompt may be determined based on the factual assertions and a second text generation prompt template. The second text generation prompt may include an instruction to respond to the factual assertions. A response to the input text may be generated based on written responses provided by the remote text generation modeling system.

Abstract: A system may determine relevance prompts based on input documents and a relevance prompt template and may transmit the plurality of relevance prompts to a large language model for completion. The system may receive response messages including chunk relevance scores. The system may select a subset of the input documents based on the chunk relevance scores. The system may determine query response prompts including text from the selected input documents the natural language query, and a second set of natural language instructions to address the natural language query. The system may determine a response to the natural language query based on answers determined in response to the query response prompts.

Abstract: A regular expression prompt may be determined by combining a regular expression prompt template with input text from an input document. The regular expression prompt template may include a natural language instruction to identify one or more regular expressions from the input text and one or more fillable portions designated for filling with the input text. The regular expression prompt may be sent to a large language model for evaluation, and one or more regular expressions may be identified based on a response received from the large language model. The regular expressions may be used to disaggregate the input text, and the disaggregated text portions may be used to determine a structured document based on the input document. The structured document may be used to determine a response to a query of the input document.

Abstract: Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

Abstract: A first set of text generation prompts may be determined based on an input document and a first text generation prompt template. The first set of text generation prompts may include an instruction to identify factual assertions in the input text. The prompts may be sent to a remote text generation modeling system, which may respond by identifying factual assertions in the input text. A second set of text generation prompts may be determined based on the factual assertions and a second text generation prompt template. The second set of text generation prompts may include an instruction to respond to the factual assertions. A response to the input text may be generated based on written responses provided by the remote text generation modeling system.

Abstract: Sorbent materials are described that have enhanced performance in removing chlorine and chloramine, among other toxic compounds. The sorbent materials are formed by a process which includes steps of oxidation using an oxidizing agent, adding a nitrogen-containing compound, and calcining the sorbent.

Abstract: A query request identifying a query and a plurality of text portions for determining an answer to the query may be received. Relevance scores corresponding with respective ones of the text portions may be determined based on application of one or more machine learning models to the respective text portion and the query. A subset of the text portions may be selected based on the relevance scores. A response message to the query request including an answer to the query in natural language text generated by a large language model based on the first subset of text portions may be determined.

Abstract: Relevance scores may be determined based on text included in a document. The text may be divided into a text portions, with the relevance scores being determined based on a comparison of a text portion of the plurality of text portions with a criterion specified in natural language. A subset of the plurality of text portions may be selected based on the plurality of relevance scores, with each of the subset of the plurality of text portions having a relevance score surpassing a threshold. A criteria evaluation prompt may be sent to a remote text generation modeling system via a communication interface. The criteria evaluation prompts may include an instruction to evaluate one or more of the subset of text portions against the criterion.

Abstract: Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

Abstract: A first set of text generation prompts may be determined based on an input document and a first text generation prompt template. The first set of text generation prompts may include an instruction to identify factual assertions in the input text. The prompts may be sent to a remote text generation modeling system, which may respond by identifying factual assertions in the input text. A second set of text generation prompts may be determined based on the factual assertions and a second text generation prompt template. The second set of text generation prompts may include an instruction to respond to the factual assertions. A response to the input text may be generated based on written responses provided by the remote text generation modeling system.

Abstract: A text generation prompt may be determined based on an input message from a client machine and a designated text generation prompt template. A text generation prompt message including the designated text generation prompt may be sent to a remote text generation modeling system via the communication interface. A text generation prompt response message may be received from the remote text generation modeling system. The text generation prompt response message may include novel text generated by a text generation model implemented at the remote text generation modeling system. The text generation prompt response message may be parsed to generate a response text based on the novel text.

Abstract: Relevance scores may be determined based on text included in a document. The text may be divided into a text portions, with the relevance scores being determined based on a comparison of a text portion of the plurality of text portions with a criterion specified in natural language. A subset of the plurality of text portions may be selected based on the plurality of relevance scores, with each of the subset of the plurality of text portions having a relevance score surpassing a threshold. A criteria evaluation prompt may be sent to a remote text generation modeling system via a communication interface. The criteria evaluation prompts may include an instruction to evaluate one or more of the subset of text portions against the criterion.

Abstract: A navigation tool assembly has a delivery wire having a distal end and a proximal end, a grip connected to the proximal end of the delivery wire, and a guide sleeve having a tapered distal end and a proximal end that is attached to the distal end of the delivery wire, the guide sleeve having a slit that extends from the proximal end of the guide sleeve to the distal end of the guide sleeve. The guide sleeve is used with an outer catheter having a shaft with a lumen extending therethrough, and either a guidewire or a microcatheter extending through the lumen. The guide sleeve is wrapped around the guidewire or the microcatheter and is retained inside the lumen at the distal end of the shaft of the outer catheter.

Abstract: A system may determine relevance prompts based on input documents and a relevance prompt template and may transmit the plurality of relevance prompts to a large language model for completion. The system may receive response messages including chunk relevance scores. The system may select a subset of the input documents based on the chunk relevance scores. The system may determine query response prompts including text from the selected input documents the natural language query, and a second set of natural language instructions to address the natural language query. The system may determine a response to the natural language query based on answers determined in response to the query response prompts.

Abstract: A regular expression prompt may be determined by combining a regular expression prompt template with input text from an input document. The regular expression prompt template may include a natural language instruction to identify one or more regular expressions from the input text and one or more fillable portions designated for filling with the input text. The regular expression prompt may be sent to a large language model for evaluation, and one or more regular expressions may be identified based on a response received from the large language model. The regular expressions may be used to disaggregate the input text, and the disaggregated text portions may be used to determine a structured document based on the input document. The structured document may be used to determine a response to a query of the input document.