Abstract: Systems and methods are presented for finding similar funds to a target fund in an investor's financial portfolio. In some embodiments, a method is presented. The method may include accessing target fund characteristics data describing former characteristics of a target fund.

Abstract: A machine may be configured as a recommendation machine that makes one or more recommendations to one or more users. The machine may provide a recommendation based on one or more psychometric profiles that correspond to a user, a product, a manager for the product, or any suitable combination thereof. The user may be a potential buyer of the product, and the manager for the product may fully or partially manage the product and consequently affect the value of the product. The user's psychometric profile may describe market understanding and risk tolerance possessed by the user, and the manager's psychometric profile may describe market understanding and risk tolerance held by the manager. Such psychometric profiles may be used to make recommendations of products. In addition, a psychometric profile for the product itself may be used in the making of such recommendations.

Abstract: A machine may be configured as a recommendation machine that makes one or more recommendations to one or more users. The machine may provide a recommendation based on one or more psychometric profiles that correspond to a user, a product, a manager for the product, or any suitable combination thereof. The user may be a potential buyer of the product, and the manager for the product may fully or partially manage the product and consequently affect the value of the product. The user's psychometric profile may describe market understanding and risk tolerance possessed by the user, and the manager's psychometric profile may describe market understanding and risk tolerance held by the manager. Such psychometric profiles may be used to make recommendations of products. In addition, a psychometric profile for the product itself may be used in the making of such recommendations.

Abstract: A method and system are presented where a server system is able to handle a majority of the processing work in determining and booking available schedule appointments between a patient's schedule and the schedules of the required resources (e.g., personnel, examination rooms, equipment, services, etc.). The server system includes local memory that stores a portion of patient and resource schedules from a database and handles task requests from a client system. Because the majority of the processing work is done at the server, the bandwidth of the connection between the client and server may be low (e.g., a 28.8 K modem), and the client does not necessarily need extensive memory and processing capabilities (e.g., a standard personal computer or PDA).