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SAGE mouse – NLU and RPA

SAGE mouse – NLU and RPA

SAGE mouse – NLU and RPA

L SAGE Company has developed a unique technology for optimizing computer learning , which mimics the way the human brain learns, creates the most intelligent robots and computers in the world and includes a huge library of ready-made components and interfaces that automatically run search engines, ERP, CRM, OCR, ASR, EXCEL , ML, BI, DM, GOOGLE AI, WINDOWS AI, WhatsAPP, SP, OneNote, Power Automate, Noise removal Robots for transport and collection, Image recognition, Image repair & enhancement, NLU and dictionaries as well as a fast interface that allows the mouse operating software to run on the fly all the softwares in the library. SAGE mouse (automatically activates NLU) learns to automatically record and run (without typing) any software (mouse, keyboard, digital pen, touch screen, icons, buttons, text boxes, scrolling lists) and any process (automatically or through natural language interaction with Computer – AI + Robotic Process Automation), operate robots and manage Purchasing, Accounting, Customer Service, Sales, Marketing, HR, Inventory, Maintenance, Automated Warehouse, Import, Export, Treasury, Risk Hedging, Investment, Knowledge Management and Documents , Operation, production, quality control, software development and testing and DBA. SAGE computer (activate SAGE mouse) learns to read and understand (humanly) documents, data, content, images, natural language, scanned documents (OCR), handwriting (ICR, also Digital pens), PDF, HTML, meaning of words by virtue of their organization in sentence and paragraph, FREE TEXT, requirements of seekers (conducting a short dialogue when necessary, refining the question and giving an accurate, intelligent and unambiguous answer), emails, databases, messages and rejects (from RPA); search for information in the organization and on the Internet according to business rules and grammatical rules, separate the wheat from the chaff,  capture content in documents, photos, emails, free text and forms (If an unsolved field remains, SAGE mouse is placed on it, the next time a similar document arrives it is automatically decoded); Decipher and understand sound and images (from all types of cameras, including IR, terahertz, X-ray and scanners); Search for images by their parts and contents related to the images; Automatically translate texts and simplify them linguistically and conceptually (ATS – individual language matching, choosing appropriate vocabulary, formulating short sentences in simple, clear, unambiguous and easy-to-understand language for computers and each person); Automatically understand and translate natural language definitions of rules (business, mathematical, logical and linguistic – vocabulary, word relations, homonymy, morphology) into software language, use them for the benefit of all applications in the organization and network, allow anyone to develop software without code (including RPA, queries , Interfaces, AI) and replace human intuition with artificial intelligence and mathematical and statistical algorithms. SAGE Computers learn to automatically understand unstructured information, perform a semantic and accurate search of structured and unstructured information, optimize databases (search and query capability and retrieval time); Optimize and upgrade any application, add screens, queries, RPA and AI (in natural language and without code) to applications, automatically clear noises, optimize all NLU and OCR, create the most accurate Natural language understanding and OCR in the world and make information and knowledge accessible to computers and people (millions of applications And billions of documents online). SAGE uses artificial neural networks, which include a large number of “neurons” arranged in layers. Each neuron can communicate with a number of other neurons in the system. Each neuron is able to perform simple computational operations and in turn pass on the information it has learned to the other neurons. In this way, as the learning progresses, SAGE turns raw information into valuable information and learns to accurately identify images and content. The first neurons knew how to recognize straight lines, the next in line knew how to recognize simple corners or patterns, the ones after them already knew how to recognize the contours of the letters and finally the last neurons knew how to understand the text or image. In order to derive optimal value from learning, a maximum amount of examples and information must be revealed to SAGE. The training is performed automatically, without code, repeatedly. At each stage SAGE compares its results to existing information and checks whether it was indeed correctly deciphered. If not, it slightly changes the parameters in the relevant neurons until optimal accuracy is obtained.

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