SYSTEM AND METHOD FOR CROSS-PLATFORM SENTIMENT AND GEOGRAPHIC-BASED TRANSACTIONAL SERVICE SELECTION

In one exemplary embodiment, a method of a telehealth services system includes the step of providing a healthcare providers database. The healthcare providers database includes an healthcare provider schedule information, a healthcare provider specialty information and a healthcare provider pricing information. The method includes the step of providing a search engine. The search engine is configured to search the database of healthcare providers based on at least one keyword. The method includes the step of providing a scheduling module configured to schedule an appointment between a consumer and a healthcare provider. A negotiation module is provided to mediate a price negotiation between the consumer and the healthcare provider.

DETAILED DESCRIPTION

Disclosed are a system, method, and article of manufacture for cross-platform sentiment and geographic-based transactional service selection. The following description is presented to enable a person of ordinary skill in the art to make and use the various embodiments. Descriptions of specific devices, techniques, and applications are provided only as examples. Various modifications to the examples described herein may be readily apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other examples and applications without departing from the spirit and scope of the various embodiments.

Exemplary Environment and Architecture

FIG. 1shows a number of transactional services102, according to some embodiments. These transactional services102can include retail sites, personal services sites, insurance, health care transactional services, electronic commerce, or any other good or service that is bought or sold.

A client104can include one of a telematic client110(software that interacts with autos and transportation systems over mobile and satellite networks), an interactive TV-based client112, a mobile client113, a computer-based client114, a telephonic client116, a game platform client118, a wearable/embedded client120and a sensor based client121(such as a embedded microchip and/or device). Telematic client110can be a computing device that include functionalities for the integrated use of telecommunications and informatics (e.g. for application in vehicles and with control of vehicles on the move). Telematics can include, but is not limited to, Global Positioning System (GPS) technology integrated with computers and mobile communications technology in automotive navigation and/or other control/tracking systems. Interactive TV-based client112can include any form of media convergence technologies (e.g. adding data services to traditional television technology). Mobile clients113can operate in various mobile devices (e.g. smartphones, tablet computers, etc.). In some examples, a mobile device can include integrated data capture devices such as barcode, RFID and/or smart card readers. Computer-based client114can operate in a computing system (e.g. a laptop computer, a personal computer, an enterprise-level computing system, a wearable device, a robotic assistant virtual or physical in form, etc.). Telephonic client116can operate in various telecommunication services for the purpose of electronic transmission of voice, fax, or data, between distant parties. Game platform client118can operate in a video-game platform (e.g. the combination of electronic components and/or computer hardware which, in conjunction with software, allows a video game to operate). A wearable/embedded client120can operate in a wearable/body-borne computer (e.g. Google Glass®, heart-rate monitors, various smart-wearable systems, and the like). As used herein, a wearable/body-borne computer can include miniature electronic devices that are worn by the bearer under, with or on top of clothing. Wearable/body-borne computer can include various biosensors and/or environmental sensors, as well as computer networking interfaces. A sensor-based client121can include a functionality coupled with a sensor. Sensor-based client can include computer networking system(s) for communicating sensor data with a remote computer system over the Internet or mobile networks.

A discovery unit124receives inputs from one of the client(s)104. The discovery unit124determines a fit of one or more of the transaction services for a user at the client104. The discovery unit124can then provide suggestions or advertising based on this fit.

The discovery unit124can be cloud-based to allow for client data and applications to be stored in the network. This allows for the clients to be lightweight and for the applications, such as the discovery unit124, to scale up quickly to additional users and otherwise adjust resources to unpredictable business demands. The cloud architecture can be private, public, or a hybrid.

The discovery unit124can make a sentiment-based fit of which of the one or more transaction services are most useful or desirable for the user. A sentiment-based fit is the determination of a fit based on the identification of subjective information from source material associated with a user. This source material is analyzed to extract attitudes, judgments or emotions with respect to certain topics that are opinion minded by sentiment analysis of keyword, concept, context or other semantic analytics accessed by the system: including the sentiment analysis of social media access by the system.

The sentiment analysis is determined by evaluating attitudes which can be simple polarity judgments indicating positive, neutral or negative opinions. The determined attitudes can also be more precise determinations of associated emotions like happiness, indifference, disinterest, disgust, fear and anger. These determined attitudes can also be associated with an intensity value. For example, a range of intensity values can be set in an index and matched with various inputs from client104(e.g. with a hash function).

In one embodiment, the sentiment-based fit can be done by checking sentiments with respect to topics that are associated with certain transaction services. For example, positive sentiment with respect to the topic of “shoes” may make a site that sells shoes a good fit. In another embodiment, sentiment/topic pairs can be analyzed, by themselves or along with additional data to determine a fit. For example, historical data of transaction service use by previous users along with these previous users' extracted sentiment/topic pairs, by themselves or along with additional data, can be used as a training set to determine correlations between the sentiment/topic pairs and use of the transaction service.

Sentiment analysis can include a semantic analysis to use language to determine preferences and personalization. Such a semantic analysis can be dynamically captured and analyzed over electronic networks. In one embodiment, sentiment analytics, neuroscience and semantic analysis are combined over mobile and electronic networks for electronic commerce and/or transaction service fit determination. The integration of these analytic parts that make up the sentiment analysis contributes to the sentiment operational framework that provides meaning and fitness for matching preferences (products, services, solutions, certain knowledge products etc.) by the buyer with resources offered by the seller.

In one embodiment, the discovery unit124can make a sentiment- and geographic-based fit. Extracted sentiment and geographic data can be used to determine a fit between the user and a transaction service. The geographic information can include geospatial location, geospatial information, and geomapping information. For example, a user's location can be used help make the fit with the transaction service. People in certain locations can have a greater or lesser affinity for certain transaction services; and this can be reflected by the determined fit. Certain locations can be used to produce adjusted sentiment values or sentiment/topic pairs. Alternately, the historical training data can be restricted or weighted based on location.

In one embodiment, the discovery unit124can also use one, two or three or more of geospatial location; geospatial information, geomapping, data analysis, predictive analysis, near field communications data, interactive voice response analysis, short code data, and neuro-marketing information to make the sentiment-based fit.

Data analysis can include inspecting, cleaning, transforming, and modeling data by data mining, business, exploratory data analysis (EDA), confirmatory data analysis (CDA), text analytics and data modeling. Predictive analysis can use statistical modeling to analyze existing data to make predictions about future events.

Near field communications allow for Smartphone's to communicate with one another by touch or close physical proximity. Near field communications are used for purchases and data exchanges. Analysis of near field communication data can be used to determine personal networks and/or other information that can be used to improve a fit determination with respect to transactional services.

Interactive voice response (IVR) systems allow computers to interact with users through voice analysis. Interactive voice response data can be used to improve a fit determination with respect to transactional services.

Neuro-marketing is research that studies consumers' neurological response to marketing stimuli and products. Tools like functional magnetic resonance imaging (fMRI), electroencephalography (EEG) steady state topography (SST) and other sensors can be used to measure biometric information to learn how consumers make decisions they do, and what part of the brain is telling them to make such decisions. Such neuro-marketing information can be obtained from a user or other information can be used to match a user with a neurological profile.

Short codes are special telephone numbers shorter than full telephone numbers that are used to address text messages. Short codes are used for television program voting, ordering ringtones, charity donations and mobile services. Automatic programs handle responses to short code texts by responding to command words or prefixes. Short code data can be analyzed to determine the fit with a transactional service. For example, the use of certain short codes can indicate that a user is a good or bad fit for a specific transactional service.

The source material for the sentiment-based analysis can include material associated with a user. For example source material obtained from social media, such as blogs and social networks. The source material can also include short codes and/or near field communications data or any other data associated with a user.

In one embodiment, the discovery software124can use at least two (or at least three) of geospatial location, geospatial information, geomapping, data analysis, predictive analysis, near field communications data, interactive voice response analysis, and neuro-marketing information to make a sentiment-based fit.

The discovery unit124can use an agent or avatar, such as a personal agent or avatar. The personal avatar can be configurable with input from a user or by using sentiment, geographic or other information about a user. This avatar can guide the user through the selection of a transaction service. The avatar can provide customer service, confirm identity, conduct financial transactions, geo-locate information, shop, conduct commerce, compare product features, provide shipping, offer advice, conduct analytics, communicate to users and other agents across networks via voice, email, video and text over mobile networks, satellite, television, telecom and the Internet. In one example, other analysis can include predictive analytics functionalities such as those provided infra.

The avatar can be a software user interface that communicates, searches, transacts, locates services for users of electronic commerce and services over electronic networks. An expert system can be used to organize data, advise, recommend, analyze user interests and information and retrieve info on demand for users over electronic networks and for transacting electronic commerce.

An avatar can be downloaded as a software application, to electronic network devices (wearable, embedded or bio-implantable in humans), habitats, autos, robotics, virtual and physical domains such as television or Web channels to provide customized transportable services and transactions for users, such as commerce, analysis, health monitoring, disease management, energy management, training, security and situational awareness.

Avatar software can provide full range of social media, voice, video, text messaging and data communications capabilities to interact with humans, other avatars, robotics, electronic networks, e commerce and shopping enterprises online and off.

Avatar software can perform advisory services over electronic networks for humans including storage of data, compare value metrics, retrieve from memory, transact electronic commerce and communicate with humans, systems and products, services available online and offline.

Avatar software can provide mobile predictive sentiment analytics for both users and merchants of electronic commerce. Avatar form and features such as hair, skin color and voice and personality may by the use of software over mobile networks and the Internet be customized by users using licensed media personalities or generic design to match their preferences.

Avatars can provide a user interface to geolocate data, video, media, text, information or a genomic database. Avatars can provide sentiment and predictive analytics on the data and content of databases and have the capacity to interact and communicate.

A genomic database-based system can be used to include the use of synthetic biology and neuroscience related data science to formulate encryption software and for the security and protection of databases. A coded system using a computational biological framework can help secure the database and protecting data and privacy. As used herein, synthetic biology can include the design and construction of biological devices and systems for useful purposes. synthetic biology can combine biology and engineering, thus often overlapping with bioengineering and biomedical engineering. Synthetic biology can encompass a variety of different approaches, methodologies, and disciplines with a focus on engineering biology and biotechnology. Key technologies that can be utilized include, inter alia: DNA sequencing (e.g. determining the order of the nucleotide bases in a molecule of DNA); gene synthesis (creating artificial genes); modeling (e.g. with predict systems to predict behavior prior to fabrication); and quantitative measurements of relevant biological systems.

An alternate embodiment of the present invention is a system200for determining geographic data for users. A geo-locating component210can automatically geo-locate users and can produce geographic data for a user. The automatic geo-location can be done by analyzing data associated with the user.

A connection component, such as fit analysis unit212, can use the geographic data to connect the user with sellers of goods or services. For example, the extracted geographic information can be used to suggest nearby service providers. The geographic information can also be used as part of a sentiment-based fit determination as discussed above.

The connection component, such as fit analysis unit212, can connect the user to transact health, loans, or other services202. Intelligent agents can be used to transact at least some services over an Internet, television, satellite or a mobile platform. The client204for the user can be one of a variety of clients as discussed above.

The source material for the geolocation analysis can include material associated with a user. For example source material obtained from social media, such as blogs and social networks. The source material can also include short codes and/or near field communications data or any other data associated with a user.

Web topologies like mesh networks can generate multipoint networks that provide Internet access to communications that are sentiment analytic, geolocational between humans, between humans and things, and between things. The systems of the present invention can be used in such networks.

(The discovery units discussed above can interact over electronic networks with (medical) devices that can sense, capture information, interact and communicate location and sentiment analysis of objects, humans, devices computing and non-computing devices.

The Mobile Web is a central network for engagement across all platforms. Mobile and social media can be important for sentiment analysis. The system of the present invention can also be used with the semantic web.

Predictive analytics and data mining can also be used to capture and generate sentiment and semantic analysis for conducting customized and personalized automated electronic commerce.

One embodiment of the present invention may be implemented using a conventional general purpose or a specialized digital computer or microprocessor(s) programmed according to the teachings of the present disclosure, as can be apparent to those skilled in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art. The invention may also be implemented by the preparation of integrated circuits or by interconnecting an appropriate network of conventional component circuits, as will be readily apparent to those skilled in the art.

One embodiment includes a computer program product which is a storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the features presented herein. The storage medium can include, but is not limited to, any type of disk including floppy disks, optical discs, DVD, CD-ROMs, micro drive, and magneto-optical disks, ROMs, RAMs, EPROM's, EEPROM's, DRAM's, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.

FIG. 3illustrates a sample computing environment300which can be utilized in some embodiments. The system300further illustrates a system that includes one or more client(s)302. The client(s)302can be hardware and/or software (e.g., threads, processes, computing devices). The system300also includes one or more server(s)304. The server(s)304can also be hardware and/or software (e.g., threads, processes, computing devices). One possible communication between a client302and a server304may be in the form of a data packet adapted to be transmitted between two or more computer processes. The system300includes a communication framework310that can be employed to facilitate communications between the client(s)302and the server(s)304. The client(s)302are connected to one or more client data store(s)306that can be employed to store information local to the client(s)302. Similarly, the server(s)304are connected to one or more server data store(s)308that can be employed to store information local to the server(s)304.

FIG. 4depicts an exemplary computing system400that can be configured to perform any one of the above-described processes. In this context, computing system400may include, for example, a processor, memory, storage, and I/O devices (e.g., monitor, keyboard, disk drive. Internet connection, etc.). However, computing system400may include circuitry or other specialized hardware for carrying out some or all aspects of the processes. In some operational settings, computing system400may be configured as a system that includes one or more units, each of which is configured to carry out some aspects of the processes either in software, hardware, or some combination thereof.

FIG. 4depicts computing system400with a number of components that may be used to perform the above-described processes. The main system402includes a motherboard404having an I/O section406, one or more central processing units (CPU)408, and a memory section410, which may have a flash memory card412related to it. The I/O section406is connected to a display424, a keyboard414, a disk storage unit416, and a media drive unit418. The media drive unit418can read/write a computer-readable medium420, which can contain programs422and/or data.

Example Processes

FIGS. 5A-B illustrate an example process500of a telehealth services system, according to some embodiments. Process500can enable consumers to search, locate and communicate with health care providers over mobile and/or Internet communication platforms (e.g. e-mail, voice over Internet Protocol (VoIP), augmented-reality messaging, a videotelephony software application and the like), according to some embodiments. The telehealth services platform can include a database of health-care service providers, consumer profiles and/or health-care server information.

In step502, a consumer can search for any type of telehealth service provider according to a set of telehealth service related categories such as zip code, geolocation and/or specialty of provider service (e.g. a medical doctor specialty type). For example, telehealth services system can maintain a web server to provide at least one web page. The telehealth services web page can include a search functionality that enables consumers to perform various search operations relating to step502. The telehealth services web server can include one or more search engines that can search a database of telehealth services. It is noted that a consumer can upload any type of biometric screening/reading/test to the telehealth services system. For example, the consumer can upload blood sugar levels, blood pressure readings, chronic obstructive pulmonary disease (COPD) information, blood thickness/thinness readings (coagulations) and the like. These values can then be forwarded by the telehealth services system (e.g. via e-mail, text message, microblog post, etc.) to at least one primary care doctor. The telehealth services system can prioritize these messages. For example, telehealth services can include a functionality that reads the values as well as other information such as a user's health profile and determines that the particular reading's value warrants an alert that requires the consumer's primary-care physician's attention. In such an example, the telehealth services system can generate a special text message that can be indicated (e.g. both visually and/or aurally) that the incoming message requires the physician's attention. It is further noted that a consumer's particular historical and/or substantially current readings can be formatted as search terms to be utilized in step502of process500.

In step504, the consumer can schedule an appointment with a selected telehealth service provider. For example, the telehealth service web page can present a scheduling dashboard to the consumer that can be accessed via a web browser functionality in a client device104operated by the consumer. In step506, the telehealth service can enable a consumer to pre-pay and/or pre-negotiate a price of the telehealth service with a selected telehealth service provider (e.g. via the telehealth service website, via short codes and/or via SMS messages with key words). In step508, the telehealth service can also enable a consumer to set up various telehealth services (such as radiology treatments, specialists visits, etc.) based on pre-negotiated pricing. A consumer can also prepay for the telehealth service.

In step510, the telehealth service system can enable a consumer to determine pricing options from different providers based on at least a type of procedure(s) for the consumer. In step512, the telehealth service system can communicate to one or more relevant telehealth service providers via a telemedicine platform. In step514, the telehealth service system can communicate digital reminders to providers and/or consumers. In step516, the telehealth service system can communicate prescription refill reminders (e.g. with advertisements, coupons, discounts, offers, etc.) to a consumer's computing device.

It is noted that the systems ofFIGS. 1-4can be modified to perform the various steps of process500. In some examples, the telehealth service system can use varies digital communication methodologies such as text messages (e.g. SMS, MMS) and/or mobile device applications for real-time communication between a patient (e.g. the consumer) and the telehealth service provider (e.g. a physician).

Example Use Cases

In one example, process500can be modified to include short code functionalities. For example, the telehealth service system can enable patients to find doctors, book appointments, pay for services and submit needs for surgery online, through mobile applications, through designated short codes. The telehealth service system can include a health care services video-network that an analytics platform can serve on-demand to consumers (e.g. with a video-sharing website managed by the telehealth service system). The videos can include educational health information. The telehealth service system can maintain and provide a automated mobile self-help system. The automated mobile self-help system can capture a consumer's personalized disease state and/or prescription history. In this way, this information can be viewed on the telehealth service system website and/or with a mobile application. This information can be integrated into a consumer's personal digital health record. Integrated laboratory results and other healthcare tests into our proprietary member portal through the web or mobile application that can be populated by predictive analytics. The telehealth service system can create a digital library of diseases and/or clinical applications that consumers can access on demand (e.g. using the search engine functionality provided for supra). The telehealth service system can maintain a national database of physicians (e.g. include various specialties/procedures, physician ratings, locations, experience levels, and the like). The telehealth service system can generate and maintain an automated-targeted mobile marketing advertisement based on targeted and/or personalized disease state, surgery requests, surgeries, conditions, actual prescriptions that can be used to build a predictive analytics and data mining platform. The telehealth service system can create a social media connection for members to share their experience with other potential members by leveraging big-data analytics to predict treatments and disease behaviors. Process500(an in some embodiments, the systems ofFIGS. 1-4) can utilize machine-learning algorithms. Example machine-learning algorithms, such support vector machines (SVM), can include statistical classification analysis algorithms, statistical regression analysis algorithms, and the like. For example, discovery unit124can include an SVM module (not shown). SVM module can supervise learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The SVM module can take a set of input data and predict, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, the SVM module can build a model that assigns new examples into one category or the other. The SVM module can include a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples can then be mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

In another example, the system ofFIGS. 1-4can utilize a neuromemetic network to form a neuromemetic model (e.g. a neural and/or cognitive computing model). This computing model can be implemented in a cloud-computing accessible database. It can characterize social media inputs, and directory resources from users (e.g. requests and interaction information). The neuromemetic model can imitate biological and/or neurological functionality regarding fitness, priorities of attention, effectiveness and/or demand logic measured by frequency of request. As the system grows in use, in usage, with larger databases, user experiences, vendor services, the system can establish and/or adapts as an expert system of emergent rules (e.g. by identifying patterns that arise out of a multiplicity of interactions observed in the operation of the neuromemetic model). Emergent rules can govern metrics such as efficacy, priority, accuracy and value. The neuromemetic network can automatically capture conversations threads from mobile interactions (e.g. text, video, audio, voice, geolocation pin, etc.) and retain memories. For example, a process operating in discovery unit124can obtain and parse conversation thread content. These memories can be utilized to form new neuronal memories as a classification system, associated with certain transactions such as a search for information, diseases, medicine, treatments or doctors and other care providers. This system can utilize software and/or hardware (e.g. neuromemetic processors, neuromorphic integrated circuits, and the like) and/or a hybrid technique to simulate neuron models in a biological system (e.g. silicon neurons can be based on a Hodgkin-Huxley formalism and optimized for reproducing a large variety of neuron behaviors utilizing tunable parameters). An example of neuromorphic computer hardware is the Neurogrid board built by the Brains in Silicon group at Stanford University. This example can integrate various neuromorphic computing and/or neuromorphic engineering techniques. As used herein, neuromorphic computing can include the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or sensory integration). As used herein, cloud-computing can include a variety of different computing concepts that involve a large number of computers that are connected through a real-time communication network (typically the Internet). Cloud computing can include various techniques for distributed computing over a network and the ability to run a program on many connected computers at the same time. This example can be applied to process500. For example, automatic predictive analytics can be implemented over cloud computer networks to enable functionalities of discovery unit124to analyze patient's health issues and/or access information as well as access medical resources.

In yet another example, the systems ofFIGS. 1-4can include mobile software agents (not shown) (e.g. digitally engineered software personality (DEPS)). DEPS, software agents and network swarms of agents can search, find, negotiate and/or transact about medical services and/or medical information offered by providers, brokers, insurance companies, government agencies and care givers. For example, an electronic-work flow can be created to connect patients, doctors, clinics and/or health care professionals together. The electronic-work flow can enable more effective communications, collaboration and health care. Discover unit124can use predictive analytics software to search medical databases and to provide decision analysis of the health care professionals and clinics that provide the “best fit” for patients who are searching for health care for specific illnesses. Connection over mobile electronic mobile networks can be implemented where patients and health care providers can interact, collaborate, transact and set up appointments for care using telehealth, video enabled communications over the mobile network or Internet. A directory information and assistance service for connecting individuals (e.g. clients, consumers, professionals such as attorneys, physicians, etc.) can be generated and managed by the discovery unit124. The directory assistance service can maintain a mobile teleservices business that provides information (e.g. contact information, practice information, demographic information, etc.) and markets various services. A national data warehouse (e.g. that includes an analytics platform) can be generated and maintained by the discovery unit124. The national data warehouse can examine patterns about the use of consumers of legal, medical and other services to market to third-party enterprises. In some embodiments, DEPS can be utilized to implement the avatars provided supra. For example, an avatar can be utilized to contact a user and mediate a response from the system to the user.

In one example, the systems provided herein can optimize the accuracy rate between a prospective buyer and seller of products and services using an integrative system of sentiment analysis, computational biology and mobile web services. Sentiment analysis can be captured by mining and analyzing semantics, to determine likes, dislikes, desire and objectives. The system(s) can use various linguistic, neuroscience, geo-location and computational biological resources to develop sentiment value scores that rank the solutions and or fit with the individuals search. The compositional sentiment and the contextual sentiment can be part of the sentiment values score that determines the buyers' fit with the automated presentation of the results. Interaction with the system (by the user and/or buyer) can refine the accuracy fit of the information and enhances meaning and utilitarian value. The acceptance of the presented information can one objective where sentiment mining results in a buyer selection of a product and/or service.

Example Embodiment

A mobile knowledge management system can be provided (MKM). The MKM system can be designed to predict, communicate, transact, connect and access personalized and/or relevant information that matches buyers to professional vendors (e.g. doctors, health professionals, lawyers) of various services, over mobile networks or electronic networks, such as the Internet and/cellular networks. The MKM system can utilize analytics, cloud computing, agents and ‘big data’ management to enable implementation. The MKM system can be an automated system that, inter alia: analyzes service use patterns, builds a database of service behavior, and/or optimizes matching criteria between its internal data resources with external vendor and consumer interactions.

MKM system can be designed to simplify the accuracy and effectiveness of service offerings, especially in professional services such as health care, where there is a demand for more accurate and simplified access demand for care and health care information. The MKM system ue use the rising consumer adoption of the mobile platform as a popular and widespread delivery channel to deliver services.

The MKM system can handles various inputs (e.g. geographic, geospatial, geo-intelligent, geo-data, social media, frequency of service use, demand, condition, service request, semantic, service type, semantic and search frequency) over a mobile or electronic network (e.g. Internet) that include the input stream. The MKM system can estimates the probability of new values that fit the specific consumer and/or vendor interactions requested and/or fulfilled over mobile inputs. The MKM system can automatically correlate and/or extrapolate based on service use, what the probabilities and patterns of probabilities are that can produce effective transaction results. This analysis can be conducted in cloud computing networks via wireless networks.

The actual predictive values selection process can include an underlying model that can be extended and/or scaled (e.g. 100 exabyte plus of memory) to predict multiple value scenarios. Value scenarios can include consumer use cases collected by the MKM system to determine system effectiveness and/or to learn about the metrics of service effectiveness. The modeling of these Value Scenarios, are constantly being updated by consumer usage and vendor input and analyzed by the MKM system to optimize system performance. This is one of the internal inputs inside the MKM system that influences service optimization and knowledge management.

The MKM system analyzes various values via a prediction algorithm. The prediction algorithm can extract more precise meanings of certain search requests and thus connect buyers with sellers using a texting and proprietary short code mobile platform that is interoperable with the MKM cloud computing databases of service providers.

Predictive forecasts can be a function and/or output of the MKM system. Predictive forecasts can be triggered when prospective consumers of services use a mobile proprietary short code texting service (e.g. My 800 Doctor; *69 doctor) to search for, access specific vendor information, schedule an appointment, transact, purchase or communicate with professionals via the MKM system.

Predictive Forecasts can match up consumer requests via the MKM system with professional vendors, service providers, via accessing automatically in real-time, the cloud computing electronic directories and/or databases integrated into the MKM system's ‘back end’ information technology infrastructure and correlating that with the geographic and consumer search request.

The predicative analytics can include predictive forecasting. Predictive forecasting can automatically optimize the knowledge management outputs, providing outputs of service recommendations to consumers, collecting use case information and providing pattern recognition of service features such as treatment correlation, service types, social media interactions, consumer buying frequency, effective and ineffective service data, effective or popular service providers. Predictive forecasts can be implemented over mobile networks with an unlimited access to data storage and network connectivity to provide scalable inputs and data information relevant to consumers to be able to communicate, search, interact and transact with vendors and one another. Predicative forecasts analytics can be transacted and/or emerge from the interaction of internal information, inputs collected from vendors, system administrators and consumers that's been collected by, and resides inside the MKM system. This is then be used to optimize the efficiencies of knowledge management in the implementation of the MKM system.

Predictive forecasts can implement matching based on location by service provider and/or consumer. In some examples, matching based on location can use more complex information exchanges between service provider, consumer and/or the MKM system. These may include certain important, relevant or priority prediction values that influence the predictive forecast such as price, location, and type of service, specialty, availability, urgency of need, etc. The predictive forecast can also include the semantic analysis methods for matching predictive values a best fit between vendor and consumer. This personalized matching, can include the most accurate predictive values fit, such that the MKM system can act as a collaboration engine automatically and in real-time to connect consumers over the MKM system to vendors that comprise the best predictive values fit. This process can be automatically iterated by the MKM system until a match is found. At that point, a fixed prediction can be determined and communicated. In this way, the MKM system can be predictive, self-reflective and/or self-learning. The machine-learning capabilities of the MKM system can provide useful services for consumers.

The MKM System can automatically categorize various medical diagnoses and correlate them with genomic, epigenetic, physiological, cardiovascular and/or epidemiological information about a medical patient or a number of patients in a database. The MKM system can include a heuristic tool for assisting physicians determine disease states, prevention strategies, prediction and/or treatment modalities. To act as a decision analysis program to assist in more accurate medical diagnosis. Another example can include improved prediction and/or management of various risk factors. Accordingly, safer and more effective care for patient populations and individual care can be provided.

In another example, buyers can be connected with vendors who offer relevant services. The MKM system can provide faster and/or more accurate mobile connectivity between consumers and vendors.

DEPS and/or other agent based typology can be implemented by the MKM system. The use of an agent-based typology, such DEPS, can be used to collect analysis, communicate the analysis, search query, or access similar data to both the MKM system administrators and/or the consumers or service providers, external to the market but connected via the MKM system in commercial usage. The DEPS can communicate via video, text, voice networks with consumers and/or vendors as a collaboration facilitator, to communicate the match up of buyer and seller, this is an automated software application of the MKM system.

The DEPS can be a user interface that can communicate, transact, search and/or interact with humans, data, systems and networks. DEPS can interact via computer and/or human languages. DEPS are personalities that enable the output of the MKM system to have a personal user interface (e.g. DEPS may appear in a human like or surreal form via software design graphics) that can communicate and transact information that may appear human-like and can be customized by the user (e.g. vendor such as a health professional or consumer) to assist in decision analysis, search, transaction, communications while using the MKM system.

DEPS can assist disabled, elderly and/or ill patients to perform various tasks such as medicine consumption, setting an appointment, obtaining for medical assistant in an emergency, contacting a health professional, interacting with a patient, doctor and/or hospital administrator, accessing information, sensing and analyzing treatment effectiveness, communicating wireless information about a patient to a physician and/or MKM system.

CONCLUSION