Abstract: A method for identity verification includes receiving a request for proof of identity from a service provider and receiving biometric information associated with a user of a communication device. The method also includes determining that the received biometric information matches a biometric profile that contains biometric information associated with a registered user of the communication device. The method also includes unlocking a private key associated with the registered user in response to determining that the received biometric information matches a biometric profile and sending a request for a digital certificate that is signed with the private key associated with the registered user. The method further includes receiving the digital certificate that includes a public key associated with the registered user and satisfies the request for proof of identity. The method also includes with forwarding the digital certificate to the service provider.

Abstract: The embodiments provide generating a dedicated virtual machine image (DVMI) including functionality for a target service provider and launching the DVMI in the host device as a dedicated virtual machine (DVM). A measurement of the DVMI and/or the launched DVM, as a Trusted Dedicated Virtual Machine (TDVM), is transmitted to the target service provider server. The target service provider determines a trust level for the TDVM, based upon the measurement and provides a level of service by the target service provider server to the TDVM, according to the trust level of the TDVM.

Abstract: Associating a semantic service description (SSD) with a service, wherein the SSD comprises a semantic description of the service, including a semantic description of a parameter of the service, according to a computer interpretable language, and, as a service grounding, a mapping between the computer interpretable language expressing the SSD and an interface, including an interface parameter, of the service. A dynamic composition of a task based upon a user selection of a plurality of known services is supported and a dynamic association of the task with a user interface of an application is supported, based upon associating the task with the user selection of a UI object, a UI display screen, and a UI event for the application.

Abstract: A computer-implemented method of defining a set of annotation elements to map a concept to electronic data as input data; generating a mapping rule, according to the set of annotation elements defined and a sample of the input data; mapping the concept to the input data by applying the mapping rule to the input data; and generating a semantic instance of the input data based upon the mapping of the concept to the input data. The set of annotation elements to map the concept to the input data are a selected ontology corresponding to the input data, a selected ontology concept from the selected ontology, a mapping of a word or word phrase in the sample input data to the selected ontology concept from the selected ontology, and a pattern of the mapped word or word phrase relative to a structure of the sample input data.

Abstract: A system and method uses wireless communication devices as beacons to determine the relative location of another target wireless communication device. The beacons transmit identifying information that the target device can use to determine the identity of the beacon. The target device can measure the received intensity of the beacon transmissions and determine an associated beacon that best satisfies a specified criteria (e.g., largest signal strength) using a procedure of the present invention that discriminates between multiple beacons.

Abstract: A computer-based system includes task computing enabling users to define tasks by combining available functionality and to execute such tasks. The computer-based system of includes available functionality which originates in devices, computing applications and electronic services available through local and remote procedure calls including Web Services, UPnP, CORBA, RMI, RPC, DCE, DCOM or comprises previously defined tasks. All available functionality is abstracted to the user as a service and each service is expressed in a service description language, and the services have a semantic description associated with them.

Abstract: In a facilitating agent apparatus, a message processing section and an element processing section replace knowledge transmitted from another agent apparatus by a superior concept, combine the knowledge with another knowledge, transform ontology, or the like, based on meta-knowledge accumulated in a meta-knowledge storing section, and transmit the resulting knowledge to still another agent apparatus. Furthermore, if transmitted knowledge is denied, the message processing section and the element processing section create a message for denying their own knowledge transmitted to another agent apparatus, based on the denied knowledge.

Abstract: If an intermediary agent device or a database agent device cannot uniquely reply to an inquiry message issued from a user agent device, it generates a conditional reply message and returns the generated message to the user agent device. When receiving the conditional reply, the user agent device presents the conditions required for the conditional reply or a reply to a user.

Abstract: In a facilitating agent apparatus, a message processing section and an element processing section replace knowledge transmitted from another agent apparatus by a superior concept, combine the knowledge with another knowledge, transform ontology, or the like, based on meta-knowledge accumulated in a meta-knowledge storing section, and transmit the resulting knowledge to still another agent apparatus. Furthermore, if transmitted knowledge is denied, the message processing section and the element processing section create a message for denying their own knowledge transmitted to another agent apparatus, based on the denied knowledge.

Abstract: In a facilitating agent apparatus, a message processing section and an element processing section replace knowledge transmitted from another agent apparatus by a superior concept, combine the knowledge with another knowledge, transform ontology, or the like, based on meta-knowledge accumulated in a meta-knowledge storing section, and transmit the resulting knowledge to still another agent apparatus. Furthermore, if transmitted knowledge is denied, the message processing section and the element processing section create a message for denying their own knowledge transmitted to another agent apparatus, based on the denied knowledge.

Abstract: A method of collecting, storing, and retrieving knowledge includes the steps of collecting knowledge information from information-source terminals via a network, the knowledge information including information regarding research and development, problems, and solutions of the problems, storing the collected knowledge information in libraries by classifying the collected knowledge information based on a type and an attribute of the collected knowledge information, organizing the stored knowledge information based on importance and frequency of use thereof, storing the organized knowledge information in the libraries after standardizing the organized knowledge information so as to be usable by others, and retrieving the standardized knowledge information from a library in response to a retrieval request so as to provide the retrieved information for a requesting party which made the retrieval request.

Abstract: A multi-dimensional Gaussian distribution generating portion obtains one vector from a multi-dimensional Gaussian probability distribution and outputs it as an initial momentum. An approximated energy function differential generating portion generates a gradient of an approximated energy function. A Hamilton equation integral portion integrates a Hamilton equation using a gradient of an approximated energy function for a predetermined integral period with initial conditions of an initial state and an initial momentum. The result of the integration as the state of the next alternative is output from a next alternative generating apparatus. The state is used with a high probability corresponding to the difference of approximated energy calculated by an approximated energy difference calculating portion and the difference between a value of an original energy in the present state and that in the next state.

Abstract: A document retrieval system of the present invention includes an agent system that facilitates document retrieval by mediating a search request from a user to databases provided with search engines that search for a document with a keyword. When a facilitating agent is supplied with a search request containing a keyword, the facilitating agent refers to a facilitating database that stores keyword information indicating the relationship between the keyword and the databases provided with search engines for each keyword so as to determine a database agent to which the search request is to be sent out.

Abstract: If an intermediary agent device or a database agent device cannot uniquely reply to an inquiry message issued from a user agent device, it generates a conditional reply message and returns the generated message to the user agent device. When receiving the conditional reply, the user agent device presents the conditions required for the conditional reply or a reply to a user.

Abstract: A multi-dimensional Gauss distribution generating portion obtains one vector from a multi-dimensional Gauss probability distribution and outputs it as an initial momentum. An approximated energy function differential generating portion generates a gradient of an approximated energy function. A Hamilton equation integral portion integrates a Hamilton equation using a gradient of an approximated energy function for a predetermined integral period with initial conditions of an initial state and an initial momentum. The result of the integration as the state of the next alternative is output from a next alternative generating apparatus. The state is used with a high probability corresponding to the difference of approximated energy calculated by an approximated energy difference calculating portion and the difference between a value of an original energy in the present state and that in the next state.

Abstract: A learning system is used in a data processing apparatus for learning an input pattern by obtaining an internal-state value necessary for realizing a desired data conversion by performing a pattern conversion defined by the internal-state value and calculating an output pattern corresponding to the input pattern.

Abstract: A system for designing a placement of a placement element comprises a placement element storage unit for storing placement element data on placement elements, a placement set unit for setting a placement of the placement element and a placement output unit for outputting the result of setting the placement. The placement set unit comprises a unit area data storage unit, a plurality of intra-virtual-placement-area placement element position storage units, a pointer storage unit and a placement control unit. A unit area data storage unit stores unit area data on unit areas each for placing a placement element. An intra-virtual-placement-area placement element position storage unit stores placement element data on placement elements in virtual placement areas. A pointer storage unit specifies placement element data stored in the intra-virtual-placement-area placement element position storage unit to which the unit areas split from the placement area correspond.

Abstract: A learning system is used in a data processing apparatus for learning an input pattern by obtaining an internal-state value necessary for realizing a desired data conversion by performing a pattern conversion defined by the internal-state value and calculating an output pattern corresponding to the input pattern.

Abstract: A learning process system is provided for a neural network. The neural network is a layered network comprising an input layer, an intermediate layer and an output layer formed of basic units. In the basic units, a plurality of inputs is multiplied by a weight signal and the products are accumulated, thereby supplying the sum of products. An output signal is obtained using a threshold value function in response to the sum of products. An error signal is generated by an error circuit in response to a difference between the output signal obtained from the output layer and a teacher signal. A weight updating signal is determined in a weight learning circuit by obtaining a weight value in which the sum of the error values falls within an allowable range. Thus, the learning is performed in the layered neural network through use of a back propagation method.

Abstract: A learning system is used in a data processing apparatus for learning an input pattern by obtaining an internal-state value necessary for realizing a desired data conversion by performing a pattern conversion defined by the internal-state value and calculating an output pattern corresponding to the input pattern.