This work proposes to measure the scope of a patent claim as the reciprocal of the self-information contained in this claim. A probability of occurrence of the claim is obtained from a language model and this probability is used to compute the self-information. Grounded in information theory, this approach is based on the assumption that an unlikely concept is more informative than a usual concept, insofar as it is more surprising. In turn, the more surprising the information required to defined the claim, the narrower its scope. Five language models are considered, ranging from simplest models (each word or character is assigned an identical probability) to intermediate models (using average word or character frequencies), to a large language model (GPT2). Interestingly, the scope resulting from the simplest language models is proportional to the reciprocal of the number of words or characters involved in the claim, a metric already used in previous works. Application is made to multiple series of patent claims directed to distinct inventions, where each series consists of claims devised to have a gradually decreasing scope. The performance of the language models is assessed with respect to several ad hoc tests. The more sophisticated the model, the better the results. I.e., the GPT2 probability model outperforms models based on word and character frequencies, which themselves outdo the simplest models based on word or character counts. Still, the character count appears to be a more reliable indicator than the word count.

Abstract--Unlike traditional robotic hands, underactuated compliant hands are challenging to model due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually performed based on visual perception. However, visual perception of the hand and object can be limited in occluded or partly-occluded environments. In this paper, we aim to explore the use of haptics, i.e., kinesthetic and tactile sensing, for pose estimation and in-hand manipulation with underactuated hands. Such haptic approach would mitigate occluded environments where line-of-sight is not always available. We put an emphasis on identifying the feature state representation of the system that does not include vision and can be obtained with simple and low-cost hardware. For tactile sensing, therefore, we propose a low-cost and flexible sensor that is mostly 3D printed along with the finger-tip and can provide implicit contact information. Taking a two-finger underactuated hand as a test-case, we analyze the contribution of kinesthetic and tactile features along with various regression models to the accuracy of the predictions. Visual perception is not available within the cabinet and, therefore, the hand must use haptic perception. To cope with the lack of an analytical solution, data-based modeling was shown I. HILE the ability to manipulate an object within the hand is a fundamental everyday task for humans, such intrinsically estimate model parameters that can be difficult problem remains challenging for robots.

Manuel Le Gallo's research will inspire a new generation of extremely dense neuromorphic computing systems. Inspired by the way the human brain functions, a team of scientists at IBM Research in Zurich, have imitated the way neurons spike, for example when we touch a hot plate. These so-called artificial neurons can be used to detect patterns and discover correlations in Big Data with power budgets and at densities comparable to those seen in biology, something which scientists strived to accomplish for decades. They can also learn, unsupervised at high speeds using very little energy. The paper entitled "Stochastic phase-change neurons," which appeared today on the cover of Nature Nanotechnology, outlines the research and its findings.