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BACKGROUND: Over the last decade, telemedicine services have been introduced in the public health care systems of several industrialized countries. In Catalonia, the use of eConsulta, an asynchronous teleconsultation service between primary care professionals and citizens in the public health care system, has already reached 1 million cases. Before the COVID-19 pandemic, the use of eConsulta was growing at a monthly rate of 7%, and the growth has been exponential from March 15, 2020 to the present day. Despite its widespread usage, there is little qualitative evidence describing how this tool is used. OBJECTIVE: The aim of this study was to annotate a random sample of teleconsultations from eConsulta, and to evaluate the level of agreement between health care professionals with respect to the annotation. METHODS: Twenty general practitioners retrospectively annotated a random sample of 5382 cases managed by eConsulta according to three aspects: the type of interaction according to 6 author-proposed categories, whether the practitioners believed a face-to-face visit was avoided, and whether they believed the patient would have requested a face-to-face visit had eConsulta not been available. A total of 1217 cases were classified three times by three different professionals to assess the degree of consensus among them. RESULTS: The general practitioners considered that 79.60% (4284/5382) of the teleconsultations resulted in avoiding a face-to-face visit, and considered that 64.96% (3496/5382) of the time, the patient would have made a face-to-face visit in the absence of a service like eConsulta. The most frequent uses were for management of test results (26.77%, 1433/5354), management of repeat prescriptions (24.30%, 1301/5354), and medical enquiries (14.23%, 762/5354). The degree of agreement among professionals as to the annotations was mixed, with the highest consensus demonstrated for the question "Has the online consultation avoided a face-to-face visit?" (3/3 pr

Introduction - With the growth of digital patent collections, and increased open accessibility, the ability to automatically organize these collections had become desirable. Apart from the specific efforts of offices responsible for patenting activity, many relevant discussions can be found in the literature, presenting alternative ways to organize patent collections and to explore the knowledge embedded in patent databases. Method - This paper identifies and discusses four problems related to the patent classification process, reviews relevant literature for the identified problems and formulates research questions for future research. Results - The review finds that there is an ongoing effort to harmonize existing patent classification systems and to improve current systems, trying to develop specific techniques to retrieve the information embedded in patents and to transform it into a more usable format for users. Conclusions - This study contributes to the development of the field of patent management by discussing contemporary issues, open research questions and possible paths to solve related problems.

We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that intuitively document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.

In this paper, we investigate the influence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search.

Patents are used by legal entities to legally protect their inventions and represent a multi-billion dollar industry of licensing and litigation. In 2014, 326,033 patent applications were approved in the US alone -- a number that has doubled in the past 15 years and which makes prior art search a daunting, but necessary task in the patent application process. In this work, we seek to investigate the efficacy of prior art search strategies from the perspective of the inventor who wishes to assess the patentability of their ideas prior to writing a full application. While much of the literature inspired by the evaluation framework of the CLEF-IP competition has aimed to assist patent examiners in assessing prior art for complete patent applications, less of this work has focused on patent search with queries representing partial applications. In the (partial) patent search setting, a query is often much longer than in other standard IR tasks, e.g., the description section may contain hundreds or even thousands of words. While the length of such queries may suggest query reduction strategies to remove irrelevant terms, intentional obfuscation and general language used in patents suggests that it may help to expand queries with additionally relevant terms. To assess the trade-offs among all of these pre-application prior art search strategies, we comparatively evaluate a variety of partial application search and query reformulation methods. Among numerous findings, querying with a full description, perhaps in conjunction with generic (non-patent specific) query reduction methods, is recommended for best performance. However, we also find that querying with an abstract represents the best trade-off in terms of writing effort vs. retrieval efficacy (i.e., querying with the description sections only lead to marginal improvements) and that for such relatively short queries, generic query expansion methods help