Abstract:
Proposed are an automated, distinct triage or channel-based (81,82,83) automated mortality classification and measuring system (1) and a method for the automated assessment, measurement, and monitoring of life risks (9), wherein life risks associated with risk-exposed individuals (91, 92, 93) are transferable to a first insurance system (2) and/or from the first insurance system (2) to an associated second insurance system (3). The system (1) comprises a table (10) with retrievable stored risk classes (101, 102, 103) each comprising assigned, triggerable risk class criteria (110, 111, 112). Individual-specific parameters (911, 921, 931) of the risk-exposed individuals (91, 92, 93) are captured relating to criteria (110, 111, 112) of the stored risk classes (101, 102, 103) and a specific risk class (101, 102, 103) associated with the risk of the exposed individual (31, 32, 33) is identified and selected from said stored risk classes (101, 102, 103) based on the captured parameters (311, 321, 331). The individual-specific parameters (911, 921, 931) comprise at least parameters indicating a captured self-declaration of smoking or non-smoking. Upon triggering (71 /711) parameters (916, 926, 936) indicating a captured self-declaration of smoking (8111, 8112, 8113), the risk-exposed individual (91, 92, 93) is automatically assigned to a first triage channel (81). However, upon triggering (71 /712) of a captured self-declaration of non-smoking, the triggered individual-specific parameters (911, 921, 931) are processed by a machine learning- based pattern recognition module (8) automatically assigning risk-exposed individuals (91, 92, 93) with detected non-smoking patterns (8211, 8212, 8213) to a second triage channel (82) as predicted non-smokers, and automatically assigning risk-exposed individuals (91, 92, 93) with detected smoking patterns (8311, 8312, 8313) to a third triage channel (83) as predicted smokers. For risk-exposed individuals (91, 92, 93) in the third triage channel (83), laboratory-scaled individual-specific parameters (915, 925, 935) are measured by means of laboratory measuring devices (914, 924, 934), and the laboratory-scaled individual-specific parameters (915, 925, 935) are triggered for measured smoking and not-measured smoking.
Abstract:
Proposed area system and a method for a convertibly triggered insurance system (1)providing self-sufficient risk protection for a variable number of defined risk exposure components (21, 22, 23). The convertibly triggered system (1) comprises a first layer trigger structure (71) comprising an aggregation module (5) for aggregating captured loss parameters (92) over all risk exposure components (21, 22, 23) and all occurrences of risk events (501) within a predefined time period (713) by incrementing an associated stored aggregated loss parameter (93). When the aggregated loss parameter (93) exceeding a defined stop loss threshold value (712) is triggered via the first layer trigger structure (71), the occurred loss (92) exceeding the defined stop threshold value (712) is automatically covered by a second insurance system (10) based on the equitable, mutually aligned second risk transfer parameters (504). The system (1) comprises an intermediate layer trigger structure (73) with an associated switching device (3), wherein by triggering the aggregated loss parameter (93) exceeding said defined stop threshold value (712) by means of the first trigger module (711), a second layer trigger structure (72) is activated. The second layer trigger structure (72) comprises a second trigger module (721), wherein if the second layer trigger structure (72) is activated via the switching device (3)by triggering loss parameters (92) measuring the loss at the risk exposure components (21, 22, 23)not to be transmitted to the convertibly triggered system (1) within the predefined time period and falling outside a retention threshold value (722) of the first insurance system (10), the occurred loss exceeding said retention threshold value (722) is automatically covered by the second insurance system (12)based on the equitable, mutually aligned second risk transfer parameters (504).
Abstract:
Proposed are a parametric, event-driven critical illness insurance system based on a resource-pooling system (1) and method for risk sharing of critical illness risks associated with elderly persons by providing a dynamic self-sufficient risk protection for a variable number of risk exposure components (21, 22, 23) by means of the resource-pooling system (1). The resource-pooling system (1) comprises an assembly module (5) to process risk-related component data (211, 221, 231) and to provide the likelihood (212, 222, 232) of said risk exposure for one or a plurality of the pooled risk exposure components (21, 22, 23,...) based on the risk-related component data (211, 221, 231). The risk exposure components (21, 22, 23) are connected to the resource-pooling system (1) for the pooling of their risks and resources, and wherein the resource-pooling system (1) comprises an multiple event-driven core-engine (3) with critical illness triggers (31, 32, 33) triggering in a patient dataflow pathway (213, 223, 233) to provide risk protection for a specific risk exposure component (21, 22, 23) for the occurrence of acute and/or chronic critical illnesses, as e.g. dementia and/or heart attack. The operation of the resource pooling system (1) is further supported by a parametric multi-trigger stage risk-cover.
Abstract:
Proposed area parametric, event-driven critical illness insurance system based on a resource-pooling system (1) and method for risk sharing of critical illness risks of a variable number of risk exposure components (21, 22, 23) by providing a dynamic self-sufficient risk protection for the risk exposure components (21, 22, 23) by means of the resource-pooling system (1). The resource-pooling system (1) comprises an assembly module (5) to process risk-related component data (211, 221, 231) and to provide the likelihood (212, 222, 232) of said risk exposure for one or a plurality of the pooled risk exposure components (21, 22, 23, …) based on the risk-related component data (211, 221, 231). The risk exposure components (21, 22, 23) are connected to the resource-pooling system (1) for the pooling of their risks and resources, and wherein the resource-pooling system (1) comprises an event-driven core-engine (3) with critical illness triggers (31, 32, 33) triggering in a patient data flow pathway (213, 223, 233) to provide risk protection for a specific risk exposure component (21, 22, 23). The operation of the resource pooling system (1) is supported by a parametric draw-down risk-cover which can additionally be related to multiple occurrences of critical illness parameters 71,72,73 triggered in the related patient data flow pathway (213, 223, 233).
Abstract:
The invention relates to a resource-pooling system (1) and to a corresponding method for risk sharing of a variable number of risk exposure components (21, 22, 23,...) by providing a self-sufficient risk protection for the risk exposure components (21, 22, 23,...) by means of the resource-pooling system (1). The risk exposure components (21, 22, 23,...) are connected to the resource-pooling system (1) by means of a plurality of payment receiving modules (2) configured to receive and store payments from risk exposure components (21, 22, 23,...) for the pooling of their risks. The total risk of the pooled risk exposure components (21, 22, 23,...) comprises a first risk contribution (211) associated to risk exposure in relation to loan losses, and a second risk contribution (212) associated to risk exposure based on emergency expenses. The pooled risk is divided in a parameterizable risk part (11) and a non-parameterizable risk part (21) by means of an indexing module. In case of triggering a loss by means of a trigger module, the suffered loss is covered by releasing associated loans and emergency expenses of the risk exposure components (21, 22, 23,...) based on the parameterizable risk part (11) from the connected loss coverage system (3) and based on the non-parameterizable risk part (12) from the received and stored payments from risk exposure components (21, 22, 23,...).
Abstract:
Responsive to a user activating in a resource (1A, 1 B) of a computer- implemented application (A, B) a link (32A, 32B) , e.g. a hypertext link, referring to an information space (31 ), a computer system receiving the request, e.g. through Hypertext Transfer Protocol, determines a referrer address (33A, 33B) associated with the resource (1A, 1 B) and returns to the user context specific information (34A, 34B) based on the referrer address (33A, 33B). The computer system determines the context specific information (34A, 34B) by generating a pointer to the context specific information, e.g. a Uniform Resource Locator, based on the link (32A, 32B) and the referrer address (33A, 33B). Consequently, activating the same link (32A, 32B) in different applications (A, B) or resources (1A, 1 B) results in different responses, context specific to the referrer (33A, 33B). Thus, a user activating the same label or hypertext link will receive different (context specific) information, depending on what application (A, B) or resource (1A, 1 B) he is using.
Abstract:
Sprach- und Textanalysevorrichtung und Verfahren zur Bildung eines Such- und/oder Klassifizierungskataloges, wobei die Vorrichtung basierend auf einer linguistischen Datenbank (22) eine Taxonomie-Table (21 ) mit variablen Taxonknoten umfasst, wobei die Sprach- und Textanalysevorrichtung ein Gewichtungsmodul (23) umfasst, wobei jedem Taxonknoten zusätzlich ein Gewichtungsparameter zum Erfassen von Auftrethäufigkeiten von Begriffen innerhalb der zu klassifizierenden und/oder sortierenden Sprach- und/oder Textdaten (10) zugeordnet abgespeichert ist, wobei die Sprach- und/oder Textanalysevorrichtung ein Intergrationsmodul (24) zum Bestimmen einer vordefinierbaren Anzahl Agglomerate basierend auf den Gewichtungsparametern der Taxonknoten in der Taxonomie-Table (21 ) umfasst, und wobei die Sprach- und/oder Textanalysevorrichtung mindestens ein neuronalen Netzwerkmodul (26) zum Klassifizieren und/oder Sortieren der Sprach- und/oder Textdaten (10) basierend auf den Agglomeraten in der Taxonomie-Table (21 ) umfasst.