PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications such as semantic search and automated patent classification. We create embeddings using Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the class assignment of the K patents with the highest p2p similarity. We thereby validate that the p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification based on text data. Furthermore, the presented classification framework is simple and the results easy to interpret and evaluate by end-users. In the out-of-sample model validation, we are able to perform a multi-label prediction of all assigned CPC classes on the subclass (663) level on 1,492,294 patents with an accuracy of 54% and F1 score > 66%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets.Comment: 18 pages, 7 figures and 4 Table

Splenic infarction: an update on William Osler\u27s observations.

BACKGROUND: Osler taught that splenic infarction presents with left upper abdominal quadrant pain, tenderness and swelling accompanied by a peritoneal friction rub. Splenic infarction is classically associated with bacterial endocarditis and sickle cell disease. OBJECTIVES: To describe the contemporary experience of splenic infarction. METHODS: We conducted a chart review of inpatients diagnosed with splenic infarction in a Jerusalem hospital between 1990 and 2003. RESULTS: We identified 26 cases with a mean age of 52 years. Common causes were hematologic malignancy (six cases) and intracardiac thrombus (five cases). Only three cases were associated with bacterial endocarditis. In 21 cases the splenic infarction brought a previously undiagnosed underlying disease to attention. Only half the subjects complained of localized left-sided abdominal pain, 36% had left-sided abdominal tenderness; 31% had no signs or symptoms localized to the splenic area, 36% had fever, 56% had leukocytosis and 71% had elevated lactate dehydrogenase levels. One splenectomy was performed and all patients survived to discharge. A post hoc analysis demonstrated that single infarcts were more likely to be associated with fever (20% vs. 63%, p \u3c 0.05) and leukocytosis (75% vs. 33%, P = 0.06) CONCLUSIONS: The clinical presentation of splenic infarction in the modern era differs greatly from the classical teaching, regarding etiology, signs and symptoms. In patients with unexplained splenic infarction, investigation frequently uncovers a new underlying diagnosis

Inclusive jet cross sections and dijet correlations in D∗±D^{*\pm} photoproduction at HERA

Inclusive jet cross sections in photoproduction for events containing a $D^*$ meson have been measured with the ZEUS detector at HERA using an integrated luminosity of $78.6 {\rm pb}^{-1}$. The events were required to have a virtuality of the incoming photon, $Q^2$, of less than 1 GeV$^2$, and a photon-proton centre-of-mass energy in the range $130<W_{\gamma p}<280 {\rm GeV}$. The measurements are compared with next-to-leading-order (NLO) QCD calculations. Good agreement is found with the NLO calculations over most of the measured kinematic region. Requiring a second jet in the event allowed a more detailed comparison with QCD calculations. The measured dijet cross sections are also compared to Monte Carlo (MC) models which incorporate leading-order matrix elements followed by parton showers and hadronisation. The NLO QCD predictions are in general agreement with the data although differences have been isolated to regions where contributions from higher orders are expected to be significant. The MC models give a better description than the NLO predictions of the shape of the measured cross sections.Comment: 43 pages, 12 figures, charm jets ZEU

Dissociation of virtual photons in events with a leading proton at HERA

The ZEUS detector has been used to study dissociation of virtual photons in events with a leading proton, gamma^* p -> X p, in e^+p collisions at HERA. The data cover photon virtualities in two ranges, 0.03<Q^2<0.60 GeV^2 and 2<Q^2<100 GeV^2, with M_X>1.5 GeV, where M_X is the mass of the hadronic final state, X. Events were required to have a leading proton, detected in the ZEUS leading proton spectrometer, carrying at least 90% of the incoming proton energy. The cross section is presented as a function of t, the squared four-momentum transfer at the proton vertex, Phi, the azimuthal angle between the positron scattering plane and the proton scattering plane, and Q^2. The data are presented in terms of the diffractive structure function, F_2^D(3). A next-to-leading-order QCD fit to the higher-Q^2 data set and to previously published diffractive charm production data is presented