Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: A location of a sensor is determined by: (1) receiving time series data including components in a plurality of dimensions, wherein the time series data correspond to measurements of the sensor that is applied to a subject; (2) determining a plurality of subsequences associated with the time series data, wherein each of the plurality of subsequences represents a characteristic pattern projected along one of the plurality of dimensions; (3) identifying a correlated subset of the plurality of subsequences as at least one instance of an activity of the subject; and (4) based on features of the correlated subset, determining the location of the sensor as applied to the subject.

Abstract: A haptic sensor for performing palpation includes a deformable membrane having a reflective surface, a light source, a camera, and a processor. When the sensor is pressed against an object on a body, the deformable membrane deforms to contour to the shape of the object, light is reflected off the reflective surface, and captured by a camera. The reflected light is processed to reconstruct a 3-D image of the object. The rendered image can show abnormalities such as cysts, tumors, or other abnormalities, as well as arterial pressure pulses. In different embodiments, the sensor illuminates the deformed membrane from multiple directions, using shape-from-shading or grayscale mapping, or using video streams to provide more accurate images. The sensor is able to be included as part of a mobile device, such as a mobile phone, thereby making it compact and portable.