Abstract: Described herein is a computer-implemented system and method for processing one or more alternate symbols associated or linked to a base symbol. A base symbol is a symbol to which at least one alternate symbol is linked. A base symbol commonly appears on a key of a keyboard or a display of a keyboard, or is a handwritten symbol recognized by handwriting entry software. Examples of alternate symbols are accented characters and punctuation marks that do not appear on a keyboard or are not commonly recognized by a handwriting entry program, and short symbol sequences. An example of a common short symbol sequence is an emoticon used in e-mail messages to convey tone or feelings. An example of a computer device that may embody the system or method is a hand-held computing device.

Abstract: A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy.

Abstract: A method and apparatus for an organizer that may receive a cellular portion to form a cellular telephone is provided. An organizer comprises an organizer component, including a microphone. The organizer further includes an area for receiving an additional component. The area is designed to receive a cellular component, and a connection element is designed to mate the microphone in the organizer component with the cellular component, to use the organizer as a part of a cellular telephone.

Abstract: A method and apparatus for an organizer that may receive a cellular portion to form a cellular telephone is provided. An organizer comprises an organizer component, including a microphone. The organizer further includes an area for receiving an additional component. The area is designed to receive a cellular component, and a connection element is designed to mate the microphone in the organizer component with the cellular component, to use the organizer as a part of a cellular telephone.

Abstract: A handheld computer having a housing with a midframe construction is described. The housing includes a front shell and a back shell coupled to a midframe. The handheld computer also includes two accessory slots for a stylus or other devices on the left and right sides of the housing. The handheld computer also includes an infrared port which is an integral portion of the housing.

Abstract: A system having a distributed web site is described. The web site is distributed between a client, a server and a web server. The client stores a set of predefined applications that correspond to a part of the web site. The applications are formatted according to a first markup language. From the set of predefined applications, the client can generate queries. The server receives the queries and generates new, related queries. The new queries correspond to a second query protocol. The second query protocol is used by the web server. The web server generates responses to the new queries and sends these responses to the server. The responses are formatted according to a second markup language. These responses correspond to the second portion of the web site. The server then converts the responses into new responses that the client can use.

Abstract: Embodiments relate to recommending actions based on costs or benefits associated with predictions generated by a spatial and temporal memory system. The spatial and temporal memory system may generate a prediction indicating that more than one value or state may take place in the future where each value or state is associated with different costs and benefits. A configuration assistant facilitates a user to define costs and benefits associated with the more than one value or state. The spatial and temporal memory system uses the likelihood distribution in prediction, and its associated costs and benefits to recommend an action.

Abstract: A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy.

Abstract: Embodiments relate to making predictions for values or states to follow multiple time steps after receiving a certain input data in a spatial and temporal memory system. During a training stage, relationships between states of the spatial and temporal memory system at certain times and spatial patterns of the input data detected a plurality of time steps later after the certain time steps are established. Using the established relationships, the spatial and temporal memory system can make predictions multiple time steps into the future based on the input data received at a current time.

Abstract: Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states.

Abstract: A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.

Abstract: An integrated device provides functionality of both a PDA and cellular telephone. Features include a power button offering control of both the computing and telephony functions of the device; a lid that turns the device on and off and controls additional telephony functions; a jog rocker that activates the device and is used to select from a variety of menu options; application buttons that offer direct access to applications stored on the device, and which can be configured to operate in conjunction with secondary keys to offer added functionality; a keyboard that enables data input into the device; an automatic word completion function that verifies and corrects a user's typing in real time; and a simplified keyboard navigation system that allows the navigation of menus using keyboard shortcuts.

Abstract: A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.

Abstract: A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Abstract: On a device having keys with multiple values, a user may perform either an iterative directory filtering operation or a direct-dial operation, without specifying which of the operations is intended. Hence, a user is allowed to perform any of a number of different operations to specify a number to be dialed or to identify a directory record, without requiring the user to specify a mode. A user's intended operation is determined by analyzing the entered keystrokes, and activates the appropriate feedback and functionality for the determined operation. In one embodiment iterative directory filtering is performed on multiple fields concurrently, including derived fields and combination fields.

Abstract: A hierarchical temporal memory (HTM) based system may be provided as a software platform. The software platform includes: a runtime engine arranged to run an HTM network; a first interface accessible by a set of tools to configure, design, modify, train, debug, and/or deploy the HTM network; and a second interface accessible to extend a functionality of the runtime engine.

Abstract: An integrated device provides functionality of both a PDA and cellular telephone. Features include a power button offering control of both the computing and telephony functions of the device; a lid that turns the device on and off and controls additional telephony functions; a jog rocker that activates the device and is used to select from a variety of menu options; application buttons that offer direct access to applications stored on the device, and which can be configured to operate in conjunction with secondary keys to offer added functionality; a keyboard that enables data input into the device; an automatic word completion function that verifies and corrects a user's typing in real time; and a simplified keyboard navigation system that allows the navigation of menus using keyboard shortcuts.

Abstract: A temporal pooler for a Hierarchical Temporal Memory network is provided. The temporal pooler is capable of storing information about sequences of co-occurrences in a higher-order Markov chain by splitting a co-occurrence into a plurality of sub-occurrences. Each split sub-occurrence may be part of a distinct sequence of co-occurrences. The temporal pooler receives the probability of spatial co-occurrences in training patterns and tallies counts or frequency of transitions from one sub-occurrence to another sub-occurrence in a connectivity matrix. The connectivity matrix is then processed to generate temporal statistics data. The temporal statistics data is provided to an inference engine to perform inference or prediction on input patterns. By storing information related to a higher-order Markov model, the temporal statistics data more accurately reflects long temporal sequences of co-occurrences in the training patterns.

Abstract: A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.

Abstract: A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.