Abstract: An improved method and mechanism is disclosed for updating rules used by a rule serve. An embodiment is directed to a method and mechanism for connecting one or more rule service agents to a rule server, in which rules may be changed and put into operation without taking the rule server offline.

Abstract: A cortronic neural network defines connections between neurons in a number of regions using target lists, which identify the output connections of each neuron and the connection strength. Neurons are preferably sparsely interconnected between regions. Training of connection weights employs a three stage process, which involves computation of the contribution to the input intensity of each neuron by every currently active neuron, a competition process that determines the next set of active neurons based on their current input intensity, and a weight adjustment process that updates and normalizes the connection weights based on which neurons won the competition process, and their connectivity with other winning neurons.

Abstract: An improved mechanism for intelligently presenting questions through an electronic interface is disclosed. A rule server receives information identifying a set of answers from a user corresponding to a first set of questions presented to that user. In response, analysis is performed upon the set of received answers. A first determination is then made of whether any questions from the first set of questions need to be answered again based upon rules governing responsive answers. Next, a second determination is made of the composition of a second set of questions. The first determination and second determination may be performed at a rule server or an application tier. The second set of questions is then presented to a user through an electronic interface. Suggested answers to the second set of questions may also be presented to the user through an electronic interface contemporaneously with the second set of questions.

Abstract: An automated system and method detects fraudulent transactions using a predictive model such as a neural network to evaluate individual customer accounts and identify potentially fraudulent transactions based on learned relationships among known variables. The system may also output reason codes indicating relative contributions of various variables to a particular result. The system periodically monitors its performance and redevelops the model when performance drops below a predetermined level.

Abstract: A system, method, and software product provide for unsupervised identification of complex, nonlinear subspaces in high dimensional data. The system includes a vector quantization module, a weighted topology representing graph module, and an encoding module. The vector quantization module takes vector data inputs and extracts a group of inputs about a number of cluster centers, using a globally optimized clustering process. The weighted topology representing graph module creates a weighted graph of the vector space, using the cluster centers as nodes, weighting edges between nodes as a function of the density of the vectors between the linked nodes. The encoding module uses the weighted graph to recode the input vectors based on their proximity to the cluster centers and the connectedness of the graph. The recoded vectors are reinput into the vector quantization module, and the process repeated until termination, for example at a limited number of cluster centers.

Abstract: Image features are generated by performing wavelet transformations at sample points on images stored in electronic form. Multiple wavelet transformations at a point are combined to form an image feature vector. A prototypical set of feature vectors, or atoms, is derived from the set of feature vectors to form an “atomic vocabulary.” The prototypical feature vectors are derived using a vector quantization method (e.g., using neural network self-organization techniques) in which a vector quantization network is also generated. The atomic vocabulary is used to define new images. Meaning is established between atoms in the atomic vocabulary. High-dimensional context vectors are assigned to each atom. The context vectors are then trained as a function of the proximity and co-occurrence of each atom to other atoms in the image. After training, the context vectors associated with the atoms that comprise an image are combined to form a summary vector for the image.

Abstract: An automated system and method detects fraudulent transactions using a predictive model such as a neural network to evaluate individual customer accounts and identify potentially fraudulent transactions based on learned relationships among known variables. The system may also output reason codes indicating relative contributions of various variables to a particular result. The system periodically monitors its performance and redevelops the model when performance drops below a predetermined level.

Abstract: A system and method for generating context vectors for use in storage and retrieval of documents and other information items. Context vectors represent conceptual relationships among information items by quantitative means. A neural network operates on a training corpus of records to develop relationship-based context vectors based on word proximity and co-importance using a technique of "windowed co-occurrence". Relationships among context vectors are deterministic, so that a context vector set has one logical solution, although it may have a plurality of physical solutions. No human knowledge, thesaurus, synonym list, knowledge base, or conceptual hierarchy, is required. Summary vectors of records may be clustered to reduce searching time, by forming a tree of clustered nodes. Once the context vectors are determined, records may be retrieved using a query interface that allows a user to specify content terms, Boolean terms, and/or document feedback.

Abstract: A system, method, and product provide rapid explanations for the scores determined by a neural network for new observations input into the neural network. The explanations are associated with a table of percentile bins for each of the input variables used to define the observation. The table contains for each input variable a number of percentile bins. Each percentile bin contains an expected score for values of the input variable containing with the percentile bin. The expected score in each percentile bin is determined from historical observation data. Preferably each percentile bin is associated with an explanation that describes the meaning of the value of the input variable falling within the percentile bin. During observation processing, a new observation is scored. The value of each input variable in the new observation is compared with the percentile bins for the input variable in the table.