Abstract: A system includes at least one processor, and at least one memory storing instructions. The instructions, when executed by the processor(s), cause the system to: access a in-vivo images captured by a capsule endoscope where the in-vivo images are designated as containing a pathology indicator; form a connected graph component based on the in-vivo images where the connected graph component includes at least two images of the in-vivo images connected based on at least one indication that the at least two images contain the same occurrence of the pathology indicator; process the connected graph component with a machine learning model to provide a score indicative of whether the at least two images contain the same occurrence of the pathology indicator; and associate the score with the at least two images for presentation to a reviewer of the at least two images.

Abstract: The present disclosure relates to systems and methods for determining whether two images of a gastrointestinal tract (GIT) contain the same occurrence of an event indicator or different occurrences of an event indicator. An exemplary processing system includes at least one processor and at least one memory storing instructions. When the instruction are executed by the processor(s), they cause the processing system to access a first image and a second image of a portion of a GIT, where the first image and the second image contain at least one occurrence of an event indicator, and to classify the first image and the second image by a classification system configured to provide an indication of whether the first image and second image contain a same occurrence of the event indicator or contain different occurrences of the event indicator.

Abstract: In accordance with aspects of the present disclosure, a system includes at least one processor and at least one memory storing instructions which, when executed by the processor(s), cause the system to access images of a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device; for each of the images, provide, by a deep learning neural network, scores for classifying the image to each of consecutive segments of the GIT; classify each image of a subset of the images, whose scores satisfy a confidence criterion, to one of the consecutive segments of the GIT; refine the classifications of the images in the subset by processing a signal over time corresponding to the classifications of the images in the subset; and estimate, among the images in the subset, a transition (1010) between two adjacent segments of the GIT based on the refined classifications of the images in the subset.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.

Abstract: Method, system, and computer program product are provided for predicting diagnosis of a patient performed by a computerized device. The method may include: modeling data from a group of successfully diagnosed patients, wherein the data is modeled as treatment paths of patients including referrals to medical practitioners; and predicting diagnosis for a current patient by comparing a treatment path of the current patient with the modeled treatment paths of successfully diagnosed patients, including calculating a probability of a given diagnosis from the modeled treatment paths. The method may include: defining a set of medical entities including medical practitioners to which a patient has been referred; and gathering treatment paths of successfully diagnosed patients, wherein the treatment path links medical entities in a directional route.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.

Abstract: A method, apparatus, and computer program product for generating a social network data structure, the method comprising: receiving a corpus comprising one or more communication indications for one or more customers, each communication indication indicating start time and end time of a communication of the customer; and generating a social network data structure indicating connections between customers based upon the start time and end time indications of communication indication comprised in the communication indications.

Abstract: A method, apparatus, and computer program product for generating a social network data structure, the method comprising: receiving a corpus comprising one or more communication indications for one or more customers, each communication indication indicating start time and end time of a communication of the customer; and generating a social network data structure indicating connections between customers based upon the start time and end time indications of communication indication comprised in the communication indications.

Abstract: Systems and methods for optimizing a virtualized communication network are provided. The method comprises monitoring traffic among nodes in a virtualized communication network to determine one or more relationships among the nodes, wherein the nodes include physical and logically defined components; determining whether one or more edges connecting the nodes in the communications network satisfy a rule; grouping the nodes connected by the one or more edges that satisfy the rule into at least one group; ranking the nodes in the group in accordance with a parameter; and implementing a policy to optimize the virtualized communication network in accordance with information determined from the ranking or the grouping of the nodes.

Abstract: Method, system, and computer program product are provided for predicting diagnosis of a patient performed by a computerized device. The method may include: modeling data from a group of successfully diagnosed patients, wherein the data is modeled as treatment paths of patients including referrals to medical practitioners; and predicting diagnosis for a current patient by comparing a treatment path of the current patient with the modeled treatment paths of successfully diagnosed patients, including calculating a probability of a given diagnosis from the modeled treatment paths. The method may include: defining a set of medical entities including medical practitioners to which a patient has been referred; and gathering treatment paths of successfully diagnosed patients, wherein the treatment path links medical entities in a directional route.

Abstract: Techniques for comparing a first document to one or more second documents are provided. At least one weight is assigned to one or more elements in the first document. A weighted document is generated in accordance with the at least one assigned weight. One or more comparison scores are computed by comparing each of the one or more elements in the first document to each of one or more elements in a given second document in accordance with one or more comparison rules. The one or more comparison rules determine if a given element in the first document and a given element in the given second document are compared using one or more language hierarchies and/or one or more similarity ranges. A similarity score is generated in accordance with the generated weighted document and the one or more computed comparison scores. The one or more second documents are retrieved in accordance with the generated similarity score.

Abstract: Method, system, and computer program product for load estimation in a user-based environment. The method includes: inputting a set of time-dependent, raw operational indicators of the environment; creating a load function according to the specific needs of the environment; displaying an estimated load; receiving user feedback on the estimated load; and applying a dynamic learning mechanism to generated a user-tuned load function for estimating load on the environment. The dynamic learning mechanism may be an informative mechanism that supports backtracking to solve user-adaptability problems.

Abstract: Techniques for comparing a first document to one or more second documents are provided. At least one weight is assigned to one or more elements in the first document. A weighted document is generated in accordance with the at least one assigned weight. One or more comparison scores are computed by comparing each of the one or more elements in the first document to each of one or more elements in a given second document in accordance with one or more comparison rules. The one or more comparison rules determine if a given element in the first document and a given element in the given second document are compared using one or more language hierarchies and/or one or more similarity ranges. A similarity score is generated in accordance with the generated weighted document and the one or more computed comparison scores. The one or more second documents are retrieved in accordance with the generated similarity score.