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Architecture of a hierarchical temporal memory based systemRelated Patent Categories: Data Processing: Artificial Intelligence, Knowledge Processing System, Knowledge Representation And Reasoning Technique, Reasoning Under Uncertainty (e.g., Fuzzy Logic)Architecture of a hierarchical temporal memory based system description/claimsThe Patent Description & Claims data below is from USPTO Patent Application 20070192267, Architecture of a hierarchical temporal memory based system. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO RELATED APPLICATIONS [0001] The present patent application contains subject matter that may be related to subject matter described in one or more of the following patent applications (each of which is assigned to the same entity to which the present patent application is assigned): U.S. patent application Ser. No. 11/010,243, filed on Dec. 10, 2004 and entitled "Methods, Architecture, and Apparatus for Implementing Machine Intelligence and Hierarchical Memory Systems"; U.S. patent application Ser. No. 11/147,069, filed on Jun. 6, 2005 and entitled "Trainable Hierarchical Memory System and Method"; and U.S. Provisional Patent Application filed on Feb. 10, 2006 and entitled "Hierarchical Temporal Memory" (Attorney Docket No. N004001). BACKGROUND [0002] Generally, a "machine" is a system or device that performs or assists in the performance of at least one task. Completing a task often requires the machine to collect, process, and/or output information, possibly in the form of work. For example, a vehicle may have a machine (e.g., a computer) that is designed to continuously collect data from a particular part of the vehicle and responsively notify the driver in case of detected adverse vehicle or driving conditions. However, such a machine is not "intelligent" in that it is designed to operate according to a strict set of rules and instructions predefined in the machine. In other words, a non-intelligent machine is designed to operate deterministically; should, for example, the machine receive an input that is outside the set of inputs it is designed to recognize, the machine is likely to, if at all, generate an output or perform work in a manner that is not helpfully responsive to the novel input. [0003] In an attempt to greatly expand the range of tasks performable by machines, designers have endeavored to build machines that are "intelligent," i.e., more human- or brain-like in the way they operate and perform tasks, regardless of whether the results of the tasks are tangible. This objective of designing and building intelligent machines necessarily requires that such machines be able to "learn" and, in some cases, is predicated on a believed structure and operation of the human brain. "Machine learning" refers to the ability of a machine to autonomously infer and continuously self-improve through experience, analytical observation, and/or other means. [0004] Machine learning has generally been thought of and attempted to be implemented in one of two contexts: artificial intelligence and neural networks. Artificial intelligence, at least conventionally, is not concerned with the workings of the human brain and is instead dependent on algorithmic solutions (e.g., a computer program) to replicate particular human acts and/or behaviors. A machine designed according to conventional artificial intelligence principles may be, for example, one that through programming is able to consider all possible moves and effects thereof in a game of chess between itself and a human. [0005] Neural networks attempt to mimic certain human brain behavior by using individual processing elements that are interconnected by adjustable connections. The individual processing elements in a neural network are intended to represent neurons in the human brain, and the connections in the neural network are intended to represent synapses between the neurons. Each individual processing element has a transfer function, typically non-linear, that generates an output value based on the input values applied to the individual processing element. Initially, a neural network is "trained" with a known set of inputs and associated outputs. Such training builds and associates strengths with connections between the individual processing elements of the neural network. Once trained, a neural network presented with a novel input set may generate an appropriate output based on the connection characteristics of the neural network. SUMMARY [0006] According to one aspect of one or more embodiments of the present invention, a system comprises: an HTM network executable at least in part on a CPU; and a first entity arranged to manage a communication between a user application and the part of the HTM network executable on the CPU. [0007] According to another aspect of one or more embodiments of the present invention, a software platform comprises: a runtime engine arranged to run an HTM network; a first interface accessible by a set of tools to at least one of configure, design, train, debug, modify, and deploy the HTM network; and a second interface accessible to extend a functionality of the runtime engine. [0008] According to another aspect of one or more embodiments of the present invention, a method of performing operations comprises: accessing, via an interface, a computer system capable of running an HTM network; and at least one of creating, designing, training, modifying, debugging, and deploying the HTM network dependent on the accessing. [0009] Other aspects of the invention will be apparent from the following description and the appended claims. BRIEF DESCRIPTION OF DRAWINGS [0010] FIG. 1 shows a flow of data between an object and a human. [0011] FIG. 2 shows an HTM in accordance with an embodiment of the present invention. [0012] FIG. 3 shows a node in accordance with an embodiment of the present invention. [0013] FIG. 4 shows a flow process in accordance with an embodiment of the present invention. [0014] FIG. 5 shows an operation of a sequence learner in accordance with an embodiment of the present invention. [0015] FIG. 6 shows a flow process in accordance with an embodiment of the present invention. [0016] FIGS. 7A-7E show representations in accordance with an embodiment of the present invention. [0017] FIG. 8 shows a representation in accordance with an embodiment of the present invention. [0018] FIG. 9 shows a representation in accordance with an embodiment of the present invention. [0019] FIG. 10 shows at least a portion of an HTM-based system in accordance with an embodiment of the present invention. [0020] FIG. 11 shows a flow process in accordance with an embodiment of the present invention. 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