40 results about "Collaborative knowledge" patented technology

The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms during various stages of medical disease management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting. The invention enables both synchronous and asynchronous multiparty collaboration with multichannel, multiplexed streaming imagery data, including interactive curation, multisensory annotation and metadata tagging, as well as multi-formatted encapsulation, saving and sharing of collaborated imagery data as packetized augmented intelligence. The invention acquires both live stream and archived medical modality imagery from network-connected medical devices, cameras, signals and sensors, as well as multiomic data [phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic and radiogenomic] maps and clinical data sets from structured reports and clinical documents, including biometric maps and movies, hapmaps, heat maps and data stream visualizations. The invention also acquires both medical and non-medical streaming imagery data from image data repositories, documents and structured reports, workstations and mobile devices, as well as from wearable computing, signals and sensors. The invention enables networked teams to interactively communicate, concurrently collaborate and bi-directionally exchange multichannel multiplexed imagery data streams, singly or together, in real time or asynchronously, generally by curating, annotating and tagging imagery information objects. The invention encapsulates and saves collaborated imagery data, together with multisensory annotations and metadata tags, in standard file formats as packetized augmented intelligence. The invention enables recursive cognitive enrichment of clinical cognitive vismemes, and saves packetized imagery information objects, multisensory annotations and metadata tags in native file formats [PDF, MPEG, JPEG, XML, XMPP, QR,TIFF, RDF, RDF / XML, SVG and DAE] as well as in formats compliant with standards for digital communications in medicine [DICOM]. The invention enables live stream multicasting of multimodal cognitive instruction and collaborative knowledge exchange with multisensory [visual, auditory, haptic] annotation of streaming imagery data, as well as secure, encrypted transmission of streaming augmented intelligence across file sharing data networks for informatics-enabled learning, specialist skills acquisition and accelerated knowledge exchange.

The invention provides a recommendation system based on a non-sampling collaborative knowledge graph network. The recommendation system comprises an embedding module which is set to obtain an initial embedding vector of a triple in a knowledge graph; a non-sampling knowledge graph convolution module which is set as a single-layer convolution network comprising a plurality of linear aggregators, and is used for performing non-sampling pre-calculation on an initial embedded vector to obtain deep information of a triple, and combining the embedded vector and the deep information as an updated embedded vector; a cooperative propagation module which is set to simultaneously encode cooperative signals in user and project interaction as initial preferences of a user and a project, and is combined with the updated embedded vector to serve as an input vector of the prediction module; a prediction module which is configured to obtain a recommendation result according to the input vector. According to the method, only by designing a relatively complex propagation matrix and pre-calculation operation, the performance which is not worse than that of a depth model, the speed is higher, and a more accurate prediction result is obtained.

Provided is a system that offers user with various automated guidance which are identified intelligently based on current analysis path of the user to provide automatic recommendations for analysis path so as to ease the big data analysis. The recommendations are based on other expert user's analysis already performed. The user chooses analysis path recommendations to arrive at the final result inless time and with ease. The system can learn continuously from the analysis path of the other users on similar data. The system utilizes the collaborative knowledge form all users for giving recommendations. The system and / or an apparatus (800) has a receiving module (808), a user interaction profiler module (810), a user profile matcher module (812), and a recommendation module (814) to provideautomatic recommendations to the users.

The invention provides a medical care and health care combined knowledge service system construction method for elderly care, which supports and provides a high-precision elderly care knowledge graph by combining cross-media knowledge unified representation, cross-media knowledge entity extraction, semantic analysis and man-machine collaborative knowledge graph auditing. And an end-to-end elderly care knowledge service of risk grading, intelligent question and answer, scheme making, emergency early warning, rehabilitation and care recommendation is provided. According to the method, the cross-media knowledge graph is used for replacing a traditional relational database which is insufficient in flexibility and small in scale, and an omnibearing knowledge data set in the elderly care ecosphere is generated. Compared with an existing medical and health care service system based on a traditional database, the medical and health care service system provides more comprehensive, accurate and flexible domain knowledge integration on the basis of multi-source and multi-dimensional information, provides an information and service management scheme based on a knowledge engineering technology, provides a more automatic, personalized and intelligent elderly care solution, and improves the medical and health care efficiency. The cost can be effectively reduced; and the efficiency is improved.

The invention discloses a collaborative knowledge perception enhanced network recommendation method, and mainly relates to the technical field of computer application. Comprising the following steps: S1, constructing a knowledge graph; s2, seamlessly combining explicit coded collaborative information in interaction of the user and the project with associated knowledge in the knowledge graph, and fusing propagation information of the user and the project in the knowledge graph through a knowledge perception nerve attention mechanism; s3, constructing a cross compression unit, obtaining project original embedding through the cross compression unit, and fusing the project original embedding with knowledge graph propagation information; s4, performing score prediction; the invention can better adapt to the problems of data sparseness and user cold start, so that the model performance and generalization are improved.

The invention provides a knowledge-based cooperative method of complex product development process. The method comprises five steps: (1) establishing a project task model, a research staff model and a knowledge model; (2) establishing a key word library; (3) matching cooperative tasks with research staffs on the base of knowledge; (4) perception and analysis of activity situation; (5) multi-dimensional visualization of cooperative environmental situation and related knowledge. According to the invention, knowledge is taken as a core of cooperative development process, task is taken as a drive of cooperative development process, distributions of tasks are accomplished by matching degree of cooperative tasks and research staffs, knowledge bodies and knowledge units which play supporting roles to cooperative process are acquired by analysis of cooperative situation, cooperative environment and related knowledge units are presented by multi-dimensional visualization method. The method provided by the invention has a wide application prospect in the fields of knowledge management, computer and cooperative technology.