News Article | April 25, 2017
PLANO, Texas--(BUSINESS WIRE)--Optimal Blue, architect and operator of the mortgage industry’s digital marketplace, announced today that the company has partnered with Total Expert Inc. to further increase productivity and drive revenue with robust contact and database management tools for every facet of a user’s business. This integration allows enterprise lenders that utilize Total Expert’s Marketing/CRM platform to increase efficiency and time savings by eliminating the need to navigate between multiple systems. In addition, users can further enhance marketing campaigns – including single property websites, pricing flyers, email campaigns, and more – with dynamic, real-time product eligibility and pricing data down to the loan officer level. LOs will also have access to their specific pricing across the country and be able to run various unique scenarios. The seamless connection between Total Expert and Optimal Blue’s new API also empowers lenders to deliver dynamic marketing content that is easily created, deployed, and tracked within Total Expert’s central system of record. “Optimal Blue is focused on developing technology partnerships with leading-edge companies equally determined to shape the future of the mortgage industry,” said Optimal Blue CEO, Scott Happ. “The CRM solutions provided by Total Expert allows for true end-to-end management and aligns perfectly with Optimal Blue’s vision to enable an ecosystem of technology innovators serving Optimal Blue customers.” “Many of our customers are considered ‘gamechangers’ who demand technology built for the future of the industry. The functionality enabled by the Total Expert-Optimal Blue partnership is a perfect example of how API based integrations will provide companies new ways to optimize their business through innovation. Total Expert is committed to integrating with best-in-class solutions like Optimal Blue as we continue to aggressively enhance our platform based on the needs of our customers,” said Total Expert CEO, Joe Welu. Total Expert Inc. provides the first modern, web based, enterprise-level sales and marketing software solution built specifically for mortgage and real estate. Sales, marketing, and compliance are aligned in a single system of record with tools including CRM, Marketing, and Co-marketing. Every marketing asset ever created, downloaded, or deployed is tracked with on-demand audit level reporting. The highly flexible profile based architecture provides precise permission controls and hierarchy settings for endless custom reporting and analytics options based on the unique preferences of the customer. Optimal Blue operates the mortgage industry’s digital marketplace, connecting lenders, investors and providers. At the center of our marketplace is a robust analytics engine containing the industry’s largest library of loan programs and real-time loan pricing data. The engine is surrounded by a workflow automation system enabling lenders to price, lock and sell mortgage loans while managing risk and maximizing efficiency. Through marketplace participation, investors gain access to the largest network of lenders and brokers in the industry while leveraging tools to automate data exchange, improve decision making and source new business. Value-added providers represent an important third group of marketplace participants, consuming pricing data to enhance the value of their solutions for Optimal Blue lenders. Together these lenders, investors and providers form a unique, multi-sided marketplace touching one of every four mortgage loans closed nationally each year.
News Article | September 15, 2017
Pattern recognition is identified as a key human skill that has supported the rise of people to become the dominant species. However, there are limits to this crucial ability, especially when confronted with masses of information that differ only slightly. Small, but significant, variations are more easily recognised by machines that can minutely inspect and compare differences without fatigue and with low margins of error. Humans are, thus, using machines to augment their pattern-recognition capability by teaching machines how to recognise patterns and correlate seemingly disparate data to gain new insights. “Specialised machine-learning algorithms are used to evaluate large quantities of data and derive and/or exploit relationships in the data,” says IBM Watson Advanced Cognitive Technology and Solutions data scientist Stefan van der Stockt. “The basic idea behind machine learning is that we want to learn relationships and corelationships between the different elements of the data, whether it be recognising a face or identifying a potentially cancerous lesion on an X-ray image,” says Council for Scientific and Industrial Research (CSIR) Mobile Intelligent Autonomous Systems unit principal researcher Dr Benjamin Rosman. Machine-learning algorithms are designed to determine which features best describe the data and thereby extract latent patterns, adds CSIR Mobile Intelligent Autonomous Systems unit data science senior researcher Nyalleng Moorosi. “Pattern-recognition algorithms are typically used for predictive analytics. The first phase is to determine the patterns and then, by projecting the patterns onto other similar data, predict the behaviour of the data,” she says. Two major categories of machine learning algorithms are classification and clustering. Classification involves the program assigning data to specific categories, while clustering involves the program grouping data based on the similarity of characteristics. Both types are used to determine features and relationships in the data. Machine learning diverges into supervised and unsupervised learning. Unsupervised machine learning uses clustering, while supervised machine learning relies on human knowledge and classification, Moorosi adds. EXPERT FOUNDATIONS Supervised learning involves building expert models using hand-labelled examples to process data and then iteratively refining the accuracy of the models, says Van der Stockt. Supervised machine learning involves training the machine learning algorithm to recognise specific data characteristics and patterns, elaborates Moorosi. The main use is to solve for a known pattern or output. Supervised systems are typically given large amounts of data as part of the testing and training phase to refine the model and improve accuracy. This method of testing data against hypotheses, assessing the output and refining the model is what painstaking scientific research involves every day. “Machine learning systems provide the capabilities to do this at a much more massive scale and rapid pace than has been possible without detracting from robust scientific practices,” explains Rosman. A complex use of supervised machine learning systems is to support medical professionals and experts. By training the machine learning model’s accuracy on hundreds or thousands of X-ray images of known cancerous lesions, the system will be able to develop a highly refined model of the characteristics of cancerous lesions in X-ray images, says Rosman. “It can then compare new X-ray images of lesions against the model and provide an estimation of how strongly the new images correlate to images of known cancerous lesions, and thereby help an oncologist to make an informed and medically sound decision.” The IBM Watson for Oncology cognitive system is a pretrained decision support tool for oncologists, highlights IBM Watson Platform sales leader for IBM Middle East & Africa Andrew Quixley. “Watson for Oncology is trained on millions of pages of relevant information, including the most recently published papers and journals. This helps oncologists to ensure that their diagnosis of the patient’s condition and the treatment recommendations reflect the entire body of domain knowledge, as well as the specific information about the patient,” he explains. “Machine learning systems produce probabilities based on data associations and characteristics that can help humans to make more accurate, data-driven decisions. It helps to reduce the uncertainty of decisions by basing the decisions on large volumes of data,” says analytics company SAS Data Management business solutions manager Aneshan Ramloo. While machines can make decisions faster than humans, the analytics models and machine learning systems serve to support strategic decision-making and must still be deployed and used according to a business’s strategy and in accordance with legal and regulatory requirements, he says. “In healthcare, machine learning is used to improve diagnostics and treatments of patients – thereby improving patient out- comes – and to manage pharmaceutical supply chains, as well as for drug discovery research. The healthcare sector is a good example of how broadly applicable machine learning and analytics systems are,” says Ramloo. SKILLED USER Supervised and unsupervised methods depend heavily on analytics experience, as most of the work involves preparing, processing and analysing data, notes Van der Stockt. The use of machine learning systems and analytics by businesses requires human resources with a broad range of fundamental and softer skills, ranging from a thorough understanding of data structural and stochastic principles to social skills and team participation, highlights University of Pretoria Department of Statistics senior lecturer Dr Frans Kanfer. The growth in available data sources including unstructured data sources, increased parallel computing power and the lower cost of distributed data storage adds value only if the data are effectively analysed, he explains. Tertiary machine learning courses focus on the use of these systems as scientific and business tools and are, therefore, cross- disciplinary courses. Formal training in or combinations between statistics, computer science, informatics, mathematics, electronic and computer engineering are required data science training components, he says. “Courses currently available are intensive courses at master’s level and are designed to address the business case for and effective use of machine learning and analytics systems. General information about programming, statistics and computer science are included so that users are comfortable manipulating data to build predictive models and, thereby, effectively interrogating the data and deriving the expected value from machine learning and analytics systems,” he says. “Developing a supervised machine learning system requires a highly skilled expert who knows the expected output of the algorithm and a skilled algorithm writer knowledgeable in the mathematical principles underlying the algorithms,” emphasises Rosman. To develop the algorithms, it is important to have a deep understanding of mathe- matical and statistical principles. It is also important to understand the trade-offs between, for example, the speed at which an actionable output can be provided (usually inverse to the number of data characteristics the model assesses and the complexity of the model) and the accuracy of the output, which typically improves as more variables are included, but takes longer to process, he adds. HUMACHINE EXPERTS Data scientists must have domain knowledge. A deep understanding of a business, industry or technical topic is necessary to effectively leverage machine-learning systems, avers Ramloo. South African machine learning start-up Snode believes that the most effective use of machine-learning systems is to augment the capabilities of people. “People can do many things that machines cannot, but, conversely, cannot do many things that machines can easily do. Machine-learning systems can, therefore, add significantly to a professional’s capabilities. We believe that this is the best way to apply these systems,” says Snode CIO Nithen Naidoo. Intelligence amplification, as Snode terms its machine-learning-based systems, requires three specific supporting elements: a fusion of data from many different data sources; interactive data visualisation systems, which also serve as human-machine interfaces; and machine-assisted analytics. “Machine learning is not the answer to everything and, from our perspective, is an advanced form of statistical analysis, albeit in a scalable, useable format,” Naidoo states. An effective hybrid human-machine system will provide the most value – for example by providing the resources and information to help an inexpert user perform to minimum standards or helping experts use their detailed knowledge to add more value to more processes and, thereby, improve their productive capabilities and strategic roles, he says. DATA SILHOUETTE A powerful use of machine-learning systems is to help researchers categorise and navigate vast amounts of data, as they enable them to create automated ways of detecting patterns and correlations in data, avers Rosman. Unsupervised learning algorithms aim to automatically discover new insights into the data and present these insights to humans, confirms Van der Stockt. Moorosi avers that machine-learning systems should not be restricted unnecessarily because they function best when given access to all available datasets to effectively fulfil their purpose of extracting patterns not recognised by people. “Machine-learning tools and the patterns they identify provide a ‘silhouette’ in the data to direct further investigation. Machine learning is, therefore, a powerful tool for discovery. This is typically what is meant by data mining,” avers Van der Stockt. Additionally, these systems can also help users identify voids in the data and, thereby, help them to determine what additional data they require to make an algorithm more accurate and effective, he highlights. Artificial intelligence systems – complex ensembles of supervised and unsupervised learning algorithms – are helping medical researchers to discover potentially effective new molecules and analyse genomic data. IBM Watson for Drug Discovery is helping researchers at Barrow Neurological Institute, in the US, identify new targets for amyotrophic lateral sclerosis research. Watson can accelerate the identification of novel drug candidates and novel drug targets by harnessing the potential of Big Data. Similarly, researchers at the New York Genome Center (NYGC), Rockefeller University and NYGC member institutions in July completed a proof of concept study that illustrated the potential of IBM Watson for Genomics to analyse complex genomic data from DNA sequencing of whole genomes. The study also showed that whole genome sequencing identified more clinically actionable mutations than the current standard of examining a limited subset of genes, known as a targeted panel. Whole genome sequencing requires significant manual analysis; therefore, artificial intelligence can help doctors identify potential therapies from whole genome sequencing for more patients in less time.
Crossent Inc., Expert Inc., Uenginessolutions Co., Hana Information & System Co., Meritz Financial Information Service Co. and Meritz Fire & Marine Insuance Co. | Date: 2012-11-30
One embodiment of the present invention provides a financial service hub system for providing an integrated financial service, comprising: a common financial service unit for providing a common service specialized for the financial service hub system, and a basic technology service and utility service required for the development of the financial service hub system; and a common product service unit for managing and providing financial information related to a financial product and a financial service, and recommending the financial product and financial service to a client based on client information. Thus, a customized financial product can be provided to the client.
News Article | October 31, 2016
WASHINGTON, Oct. 31, 2016 /PRNewswire/ -- Expert Drones, a subsidiary of Dronepire Inc., announced today its custom "Drone Racing Experience" will be featured at the U.S. Army All-American Bowl. The upcoming All-American Bowl will take place in San Antonio, TX at the Alamodome on 7 Jan...
News Article | December 7, 2016
RICHARDSON, Texas--(BUSINESS WIRE)--Armor, a leading provider of managed cloud security, is partnering with EAP Expert Inc., a leading provider of technology and professional service solutions to both internal and external employee assistance programs, to provide cloud security services to complement IT infrastructure management services. EAP Expert Inc. is creating efficiencies by leveraging Armor’s proven experience and expertise so they can focus on primary services including IT management, implementation, training, data migration and geo-access reports. “Data security is a top-of-mind concern for our clients with many requiring 24/7 monitoring,” said Mark Palmer, VP, strategy and client experience, EAP Expert Inc. “Armor provides comprehensive solutions that can adapt to the unique needs of our user base while equipping our clients against the fast-moving threat landscape. While the marketplace is flooded with security tools, Armor differentiates with their elite talent and customer service team helping clients easily remediate any issues, so they can focus resources on driving profits and achieving business objectives.” Armor provides two PCI and HIPAA compliant solutions backed by more than 150 years of combined military cyber security experience. Armor Anywhere helps organizations that operate in virtual and public clouds, as well as internal IT environments, by delivering managed services from a team of threat intelligence experts. Users can also leverage Armor Complete, the world’s most secure cloud platform, to protect highly-sensitive data in Armor’s fully-managed environment. EAP Expert Inc. clients span the globe, including locations in the U.S., Canada, UK, Germany, Bermuda, Puerto Rico, South Africa, China and Australia. The company’s software handles routine day-to-day functions for users such as tracking cases, providing organizations’ ancillary services (orientations, CISDs, workshops, meetings etc.), and incorporating over 150 management reports. Through their software, EAP Expert Inc. helps organizations operate more efficiently, save time and money, and provide an improved user experience. Beyond software, they also offer customer surveys, customer portals, ProviderFiles, GeoAccess reports, and mobile EAP and SAP solutions, in addition to custom software development projects. “Working with organizations that manage a variety of sensitive employee data is an ideal partnership for Armor,” said Jared Day, president, Armor. “Our team’s ability to help align the ideal security solutions to fit unique user needs is a huge benefit for growth-oriented organizations like EAP Expert Inc. We appreciate the opportunity to protect their client base and look forward to being a part of their success.” EAP Expert Inc. is a leading provider of technology and professional service solutions to both internal and external EAPs around the world. They are an innovative organization focused on the delivery of high-value software that helps EAPs reduce costs, increase revenue, and provide a better, higher level of care. The leader in active cyber defense, Armor offers customer-centric security outcomes for retail and eCommerce enterprises, healthcare organizations, payment leaders and financial institutions. Armor protects highly sensitive data for the most security-conscious companies in the world. With its proven cybersecurity approach and proprietary cloud infrastructure built specifically for security, compliance and performance, responsible businesses choose Armor to reduce their risk. For more information, visit armor.com or call 1-844-682-2858. For more information, visit armor.com or follow @armor.
Expert Inc. | Date: 2014-04-21
Reverse-osmosis-based concentrators for automatedly concentrating the sugar content of liquids to a desired sugar content. In some embodiments, the concentrator includes a variable-pressure pumping system designed, configured and controlled to maintain a desired pressure within one or more reverse-osmosis units. In some embodiments the concentrator includes an automated concentrate bleed system designed and configured to automatedly control an amount of concentrate bled from the concentrator as a function of a predetermined sugar content of the liquid. Corresponding methods of concentrating sugar are also disclosed. In some methods, sugar is concentrated by automatedly controlling pressure of the liquid within one or more reverse-osmosis units. In some methods sugar is concentrated by automatedly controlling output of a concentrate from the one or more reverse-osmosis units as a function of a sugar content of the liquid.
Expert Inc. | Date: 2016-06-28
Provided are a system and method of providing chat-based technical support to smartphone users. The system and method include a cloud-based server connected to the internet, and a plurality of smartphone specialists connected to the server. The server is configured to select a group of top ranked specialists based on device tags and keyword tags. The device tags relate to the smartphone. The keyword tags relate to the smartphone user query.
News Article | November 1, 2016
MONTRÉAL, QUÉBEC--(Marketwired - Nov. 1, 2016) - Sphinx Resources Ltd. ("Sphinx" or the "Corporation") (TSX VENTURE:SFX) is pleased to announce new platinum group elements ("PGE") targets on its 100% owned Green Palladium project located in the Pontiac Regional County Municipality of southern Quebec, including the extension of the stratabound PGE reef discovered in 2015. The targets have been identified following the recent completion of a rock and soil sampling program. The original Green Palladium discovery horizon, which returned 3.44 g/t Pd+Pt+Au (Pd 2.46 g/t, Pt 0.23 g/t, Au 0.25 g/t) over 40 cm (true width could not be determined) in drillhole GPd-15-01 (see the Corporation's press release of June 18, 2015) is now extended over a strike length of 800 metres. A new second target area has been identified 250 m northwest of the original Green Palladium PGE reef discovery. New airborne magnetic survey data from the helicopter-borne geophysical survey conducted this summer over the Green Palladium and Calumet-Sud projects was disclosed in the Corporation's press release dated September 7, 2016. The Corporation's interpretation of these data suggests: In September and October 2016, prospecting, soil geochemical sampling and rock sampling programs were performed. A total of 16 rock and 692 soil samples were taken. Anomalous copper, nickel, palladium and platinum values in soil and rock samples show a strong spatial correlation with the magnetic trends identified during the 2016 heliborne geophysical survey (see appended Figure 1). This recent work has led to the identification of new drill targets that can be tested along the 800-metre long PGE reef including the deepening of certain 2015 drillholes that did not reach the PGE reef. Exploration in the project area is low cost and benefits from excellent infrastructure and community support. Funding for the program included funds provided by the Société d'investissement dans la diversification de l'exploration (SIDEX) as part of its "Field Action 2016" program. Field work has been conducted under the supervision of Dr. Michel Gauthier (géo, ing. and a director of the Corporation). Rock samples were analyzed by Laboratoire Expert Inc., Rouyn-Noranda, Quebec. Gold, platinum and palladium values were determined by 30-gram fire assay with DCP-1 finish. Silver, copper and nickel values were determined by partial acid digestion followed by AAT-7 finish. The laboratory follows an internal quality control program utilizing a system of blanks, standards and duplicates. A hand-held Niton XRF analyzer provided geochemical readings for a wide range of metallic elements including nickel and copper. Readings were made at the soil sample surface. Several readings are used to generate an averaged value. Values obtained using the Niton XRF analyzer are being used only for exploration planning. The Green Palladium project comprises of 231 claims with a surface area of about 136 km2 and covers approximately half of the Obwondiag layered complex. The remainder of the complex is covered by the Calumet-Sud project which is under an option and joint venture agreement between Sphinx and SOQUEM. The technical information presented in this press release has been approved by Normand Champigny, President and Chief Executive Officer of Sphinx, and a Qualified Person as defined by NI 43-101. Sphinx is engaged in the generation and acquisition of precious metals exploration projects in Québec, a Canadian province which is recognized as an attractive mining jurisdiction worldwide. For further information, please consult Sphinx's website. Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release. This press release may contain forward-looking statements that are subject to known and unknown risks and uncertainties that could cause actual results and activities to vary materially from targeted results and planning. Such risks and uncertainties include those described in Sphinx's periodic reports including the annual report or in the filings made by Sphinx from time to time with securities regulatory authorities.
News Article | November 1, 2016
MONTRÉAL, QUÉBEC--(Marketwired - 1 nov. 2016) - Ressources Sphinx ltée (« Sphinx » ou la « Société ») (TSX CROISSANCE:SFX) est heureuse d'annoncer de nouvelles cibles pour les platinoïdes sur son projet Green Palladium situé dans la MRC du Pontiac au sud du Québec y compris l'extension de l'horizon stratoïde ('reef') découvert en 2015. Le projet appartient entièrement à la Société. Les cibles ont été identifiées à la suite de la réalisation récente d'un programme d'échantillonnage de roches et de sols. La découverte initiale de l'horizon de Green Palladium, qui a retourné une teneur de 3,44 g/t Pd+Pt+Au (Pd 2,46 g/t, Pt 0,23 g/t, Au 0,25 g/t) sur 40 cm (l'épaisseur réelle ne peut pas être déterminée) dans le sondage GPD-15-01 (voir le communiqué de presse de la Société du 18 juin 2015) est maintenant prolongé sur une longueur de 800 mètres. Une seconde zone cible est identifiée à 250 m au nord-ouest de la découverte initiale de la découverte initiale de l'horizon Green Palladium. Les nouvelles données magnétiques du levé géophysique héliporté effectué durant l'été dernier sur les projets Green Palladium et Calumet-Sud, ont été divulguées dans le communiqué de presse de la Société du 7 septembre 2016. L'interprétation de ces données suggère : En septembre et octobre 2016, un programme de prospection, de géochimie de sols et d'échantillonnage de roches a été réalisé. Un total de 16 échantillons de roche et de 692 échantillons de sols ont été prélevés. Des valeurs anomales en cuivre, nickel, palladium et platine dans des échantillons de sols et de roche montrent une forte corrélation spatiale avec les corridors magnétiques identifiés lors du levé géophysique héliporté de 2016 (voir la Figure 1 ci-jointe). Ces travaux récents ont conduit à l'identification de nouvelles cibles de forage qui peuvent être testées le long du 'reef' d'une longueur de 800 m, y compris l'approfondissement de certains sondages réalisés en 2015 qui n'ont pas atteint l'horizon à platinoïdes. L'exploration dans le secteur du projet est réalisable à faible coût et bénéficie d'une excellente infrastructure et de l'appui de la communauté. Le financement du programme inclus des fonds fournis par la Société d'investissement dans la diversification de l'exploration (SIDEX) dans le cadre de son programme « Action-Terrain 2016 ». Le travail de terrain a été mené sous la direction de Michel Gauthier, Ph.D. (géo, ing. et un administrateur de la Société). Les échantillons de roche ont été analysés par Laboratoire Expert Inc., de Rouyn-Noranda, Québec. Les valeurs en or, platine et palladium ont été déterminées par pyroanalyse sur des prélèvements de 30 grammes avec une finition avec la méthode DCP-1. Les valeurs en argent, en cuivre et en nickel ont été déterminées par une digestion partielle avec acide suivie d'une finition par la méthode AAT-7. Le laboratoire adopte un programme de contrôle de qualité interne en utilisant un système d'insertion d'échantillons stériles, standards certifiés et de doublons. Un analyseur portatif XRF Niton a fourni des lectures géochimiques pour un large éventail d'éléments métalliques, y compris le nickel et le cuivre. Les lectures ont été faites à la surface des échantillons de sols. Plusieurs lectures sont utilisées pour générer une valeur moyenne. Les valeurs obtenues à l'aide de l'analyseur XRF Niton sont utilisés seulement pour la planification de l'exploration. Le projet Green Palladium est constitué de 231 claims couvrant une superficie d'environ 136 km2 couvrant approximativement la moitié du complexe igné lité d'Obwondiag. Le reste du complexe est couvert par le projet Calumet-Sud qui est sujet à un contrat d'option et de coentreprise entre Sphinx et SOQUEM. Les données techniques qui figurent dans le présent communiqué ont été approuvées par Normand Champigny, président et chef de la direction de Sphinx et personne qualifiée au sens du Règlement 43-101. Sphinx est une société axée sur la génération et l'acquisition de projets d'exploration de métaux précieux au Québec, province canadienne qui est reconnue à l'échelle mondiale comme une juridiction minière attrayante. Pour plus de renseignements, veuillez consulter le site web de Sphinx. Ni la Bourse de croissance TSX ni son fournisseur de services de réglementation (au sens attribué à ce terme dans les politiques de la Bourse de croissance TSX) n'acceptent de responsabilité quant au caractère adéquat ou à l'exactitude du présent communiqué. Le présent communiqué peut contenir des énoncés prospectifs qui sont assujettis à des risques et à des incertitudes connus et inconnus qui pourraient faire en sorte que les activités et les résultats réels diffèrent considérablement des résultats attendus et des activités prévues. Ces risques et incertitudes comprennent ceux qui sont décrits dans les rapports périodiques de Sphinx, y compris le rapport annuel, ou les documents que Sphinx dépose à l'occasion auprès des autorités en valeurs mobilières.