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US6A1 - Internet appliance system and method- Google Patents US6A1 - Internet appliance system and method- Google Patents Internet appliance system and methodInfo Publication number US6A1 US6A1 US11/467,915 US46791506A USA1 US 6 A1 US6 A1 US 6A1 US 46791506 A US46791506 A US 46791506A US A1 US A1 US A1 Authority US United States Prior art keywords user pp data image system Prior art date 1999-02-01 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Granted Application number US11/467,915 Other versions Inventor Steven Hoffberg Linda Hoffberg-Borghesani Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)HOFFBERG FAMILY TRUST 1STEVEN M HOFFBERG 2004-1 GRATBlanding Hovenweep LLCOriginal Assignee Blanding Hovenweep LLC Priority date (The priority date is an assumption and is not a legal conclusion.
The present application is a continuation of U.S. 11/363,393 filed on Feb. 27, 2006, U.S. 11/363,411 filed on Feb. 27, 2006, U.S. 11/363,412 filed on Feb.
27, 2006, U.S. 11/363,413 filed on Feb. 27, 2006, U.S.
11/363,431 filed on Feb. 27, 2006, each of which is Pending and claim the benefit of U.S. 10/693,759 filed on Oct.
24, 2003 (U.S. The present application claims benefit of priority through U.S. 10/693,759 to 10/162,079 filed on Jun.
03, 2002 (U.S. 6,640,145) and U.S. 09/241,135 filed on Feb. 01, 1999 (U.S. Each of the above applications is expressly incorporated herein by reference.COPYRIGHT RETENTION NOTICE. Intelligent or learning systems are also known. These systems are limited by the particular paradigm employed, and rarely are the learning algorithms general.
In fact, while the generic theory and systems which learn are well known, the application of such systems to particular problems requires both a detailed description of the problem, as well as knowledge of the input and output spaces. Even once these factors are known, a substantial tuning effort may be necessary to enable acceptable operation. Therefore, the present invention builds upon the prior art, which defines various problems to be addressed, intelligent systems and methods, tuning paradigms and user interfaces. Therefore, as set forth below, and in the attached appendix of references (including abstracts), incorporated herein by reference, a significant number of references detail fundamental technologies which may be improved according to the present invention, or incorporated together to form a part of the present invention. To the some extent, these technologies are disclosed and are expressly incorporated herein by reference to avoid duplication of prior art teachings. However, the disclosure herein is not meant to be limiting as to the knowledge of a person of ordinary skill in the art.
Recitation hereinbelow of these teachings or reference to these teachings is not meant to imply that the inventors hereof were necessarily in any way involved in these references, nor that the particular improvements and claimed inventions recited herein were made or conceived after the publication of these references. Thus, prior art cited herein is intended to (1) disclose information related to the application published before the filing hereof; (2) define the problem in the art to which the present invention is directed, (3) define prior art methods of solving various problems also addressed by the present invention, (4) define the state of the art with respect to methods disclosed or referenced herein, and/or (5) detail technologies used to implement methods or apparatus in accordance with the present invention. Aspects of the present invention provide an advanced user interface. The subject of man-machine interfaces has been studied for many years, and indeed the entire field of ergonomics and human factors engineering revolves around optimization of human-machine interfaces. Typically, the optimization scheme optimizes the mechanical elements of a design, or seeks to provide a universally optimized interface. Thus, a single user interface is typically provided for a system. In fact, some systems provide a variety of interfaces, for example, novice, intermediate and advanced, to provide differing balances between available control and presented complexity.
Further, adaptive and/or responsive human-machine computer interfaces are now well known. However, a typical problem presented is defining a self-consistent and useful (i.e., an improvement over a well-designed static interface) theory for altering the interface. Therefore, even where, in a given application, a theory exists, the theory is typically not generalizable to other applications. Therefore, one aspect of the present invention is to provide such a theory by which adaptive and/or responsive user interfaces may be constructed and deployed. In a particular application, the user interface according to the present invention is applied to general-purpose-type computer systems, for example, personal computers.
One aspect of the present invention thus relates to a programmable device that comprises a menu-driven interface in which the user enters information using a direct manipulation input device. Such a type of interface scheme is disclosed in Verplank, William L., “Graphics in Human-Computer Communication: Principles of Graphical User-Interface Design”, Xerox Office Systems. See the references cited therein: Foley, J.
D., Wallace, V. L., Chan, P., “The Human Factor of Computer Graphics Interaction Techniques”, IEEE CG&A, November 1984, pp.
13-48; Koch, H., “Ergonomische Betrachtung von Schreibtastaturen”, Humane Production, 1, pp. 12-15 (1985); Norman, D A., Fisher, D., “Why Alphabetic Keyboards Are Not Easy To Use: Keyboard Layout Doesn't Much Matter”, Human Factors 24(5), pp. 509-519 (1982); Perspectives: High Technology 2, 1985; Knowlton, K, “Virtual Pushbuttons as a Means of Person-Machine Interaction”, Proc. Computer Graphics, Pattern Recognition and Data Structure, Beverly Hills, Calif., May 1975, pp. 350-352; “Machine Now Reads, enters Information 25 Times Faster Than Human Keyboard Operators”, Information Display 9, p. 18 (1981); “Scanner Converts Materials to Electronic Files for PCs”, IEEE CG&A, December 1984, p. 76; “New Beetle Cursor Director Escapes All Surface Constraints”, Information Display 10, p.
12, 1984; Lu, C., “Computer Pointing Devices: Living With Mice”, High Technology, January 1984, pp. 61-65; “Finger Painting”, Information Display 12, p. 18, 1981; Kraiss, K F., “Neuere Methoden der Interaktion an der Schnittstelle Mensch-Maschine”, Z. Arbeitswissenschaft, 2, pp.
65-70, 1978; Hirzinger, G., Landzettel, K, “Sensory Feedback Structures for Robots with Supervised Learning”, IEEE Conf. On Robotics and Automation, St. Louis, March 1985; Horgan, H., “Medical Electronics”, IEEE Spectrum, January 1984, pp. A directional or direct manipulation-type sensor based infrared remote control is disclosed in Zeisel, Tomas, Tomaszewski, “An Interactive Menu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEE Transactions on Consumer Electronics, Vol.
3, 814-818 (1988), which relates to a control for programming with the West German Videotext system. This implementation differs from the Videotext programming system than described in Bensch, U., VPV—“VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions on Consumer Electronics, Vol. 3, 788-792 (1988), which describes the system of Video Program System Signal Transmitters, in which the VCR is programmed by entering a code for the Video Program System signal, which is emitted by television stations in West Germany. Each separate program has a unique identifier code, transmitted at the beginning of the program, so that a user need only enter the code for the program, and the VCR will monitor the channel for the code transmission, and begin recording when the code is received, regardless of schedule changes. The Videotext Programs Recorder (VPV) disclosed does not intelligently interpret the transmission, rather the system reads the transmitted code as a literal label, without any analysis or determination of a classification of the program type. User input devices may be broken down into a number of categories: direct inputs, i.e.
Touch-screen and light pen; indirect inputs, i.e. Trackball, joystick, mouse, touch-tablet, bar code scanner (see, e.g., Atkinson, Terry, “VCR Programming: Making Life Easier Using Bar Codes”), keyboard, and multi-function keys; and interactive input, i.e.
Voice activation/instructions (see, e.g., Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”, PC Magazine, Oct. 27, 1987, 261-308); and eye tracker and data suit/data glove (see, e.g. Tello, Ernest R., “Between Man And Machine”, Byte, September 1988, 288-293; products of EXOS, Inc; Data Glove).
Each of the aforementioned input devices has advantages and disadvantages, which are known in the art. Studies suggest that a “direct manipulation” style of interface has advantages for menu selection tasks. This type of interface provides visual objects on a display screen, which can be manipulated by “pointing” and “clicking” on them. For example, the popular Graphical User Interfaces (“GUIs”), such as Macintosh and Microsoft Windows, and others known in the art, use a direct manipulation style interface. A device such as a touch-screen, with a more natural selection technique, is technically preferable to the direct manipulation method.
However, the accuracy limitations and relatively high cost make other inputs more commercially practical. Further, for extended interactive use, touchscreens are not a panacea for office productivity applications. In addition, the user must be within arms' length of the touch-screen display. In a cursor positioning task, Albert (1982) found the trackball to be the most accurate pointing device and the touch-screen to be the least accurate when compared with other input devices such as the light pen, joystick, data tablet, trackball, and keyboard. Epps (1986) found both the mouse and trackball to be somewhat faster than both the touch-pad and joystick, but he concluded that there were no significant performance differences between the mouse and trackball as compared with the touch-pad and joystick.
Information describing BroadVision One-to-One Application System: “Overview,” p. 1; Further Resources on One-To-One Marketing, p. 1; BroadVision Unleashes the Power of the Internet with Personalized Marketing and Selling, pp. 1-3; Frequently Asked Questions, pp. 1-3; Products, p. 1; BroadVision One-To-One(.TM.), pp.
1-2; Dynamic Command Center, p. 1; Architecture that Scales, pp. 1-2; Technology, pp. 1; Creating a New Medium for Marketing and Selling BroadVision One-To-One and the World Wide Web a White Paper, pp. 1-15; www.broadvision.com (1996, January-March). Fractals are a relatively new field of science and technology that relate to the study of order and chaos.
While the field of fractals is now very dense, a number of relevant principles are applicable. First, when the coordinate axes of a space are not independent, and are related by a recursive algorithm, then the space is considered to have a fractional dimensionality. One characteristic of such systems is that a mapping of such spaces tends to have self-similarity on a number of scales. Interestingly, natural systems have also been observed to have self-similarity over several orders of magnitude, although as presently believed, not over an unlimited range of scales. Therefore, one theory holds that images of natural objects may be efficiently described by iterated function systems (IFS), which provide a series of parameters for a generic formula or algorithm, which, when the process is reversed, is visually similar to the starting image. Since the “noise” of the expanded data is masked by the “natural” appearance of the result, visually acceptable image compression may be provided at relatively high compression ratios.
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This theory remains the subject of significant debate, and, for example, wavelet algorithm advocates claim superior results for a more general set of starting images. It is noted that, on a mathematical level, wavelets and fractal theories have some common threads. 5,347,600, incorporated herein by reference, relates to a method and apparatus for compression and decompression of digital image data, using fractal methods. According to this method, digital image data is automatically processed by dividing stored image data into domain blocks and range blocks. The range blocks are subjected to processes such as a shrinking process to obtain mapped range blocks.
The range blocks or domain blocks may also be processed by processes such as affine transforms. Then, for each domain block, the mapped range block which is most similar to the domain block is determined, and the address of that range block and the processes the blocks were subjected to, are combined as an identifier which is appended to a list of identifiers for other domain blocks. The list of identifiers for all domain blocks is called a fractal transform and constitutes a compressed representation of the input image.
To decompress the fractal transform and recover the input image, an arbitrary input image is formed into range blocks and the range blocks processed in a manner specified by the identifiers to form a representation of the original input image. A fractal-processing method based image extraction method is described in Kim, D. H., Caulfield, H. J.; Jannson, T.; Kostrzewski, A; Savant, G, “Optical fractal image processor for noise-embedded targets detection”, Proc. SPIE—The International Society for Optical Engineering, Vol. 144-9 (1993) (SPIE Conf: Photonics for Processors, Neural Networks, and Memories 12-15 July 1993, San Diego, Calif., USA).
According to this paper, a fractal dimensionality measurement and analysis-based automatic target recognition (ATR) is described. The ATR is a multi-step procedure, based on fractal image processing, and can simultaneously perform preprocessing, interest locating, segmenting, feature extracting, and classifying. See also, Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edge detection with fractal dimension”, Transactions of the Institute of Electronics Information and Communication Engineers D-II, J76D-II(11): 2459-63 (1993); Hayes, H.
I.; Solka, J. L.; Priebe, C. E.; “Parallel computation of fractal dimension”, Proc. SPIE—The International Society for Optical Engineering, 1962:219-30 (1993), Priebe, C. E.; Solka, J. L.; Rogers, G.
W., “Discriminant analysis in aerial images using fractal based features”, Proc. SPIE—The International Society for Optical Engineering, 1962:196-208(1993). See also, Anson, L., “Fractal Image Compression”, Byte, October 1993, pp. 195-202, “Fractal Compression Goes On-Line”, Byte, September 1993. Methods employing other than fractal-based algorithms may also be used.
See, e.g., Liu, Y., “Pattern recognition using Hilbert space”, Proc. SPIE—The International Society for Optical Engineering, 1825:63-77 (1992), which describes a learning approach, the Hilbert learning. This approach is similar to Fractal learning, but the Fractal part is replaced by Hilbert space.
Like the Fractal learning, the first stage is to encode an image to a small vector in the internal space of a learning system. The next stage is to quantize the internal parameter space. The internal space of a Hilbert learning system is defined as follows: a pattern can be interpreted as a representation of a vector in a Hilbert space. Any vectors in a Hilbert space can be expanded. If a vector happens to be in a subspace of a Hilbert space where the dimension L of the subspace is low (order of 10), the vector can be specified by its norm, an L-vector, and the Hermitian operator which spans the Hilbert space, establishing a mapping from an image space to the internal space P. This mapping converts an input image to a 4-tuple: t in P=(Norm, T, N, L-vector), where T is an operator parameter space, N is a set of integers which specifies the boundary condition. The encoding is implemented by mapping an input pattern into a point in its internal space.
The system uses local search algorithm, i.e., the system adjusts its internal data locally. The search is first conducted for an operator in a parameter space of operators, then an error function delta (t) is computed. The algorithm stops at a local minimum of delta (t). Finally, the input training set divides the internal space by a quantization procedure. See also, Liu, Y., “Extensions of fractal theory”, Proc.
SPIE—The International Society for Optical Engineering, 1966:2). Fractal methods may be used for pattern recognition.
See, Sadjadi, F., “Experiments in the use of fractal in computer pattern recognition”, Proc. SPIE—The International Society for Optical Engineering, 1960:2). According to this reference, man-made objects in infrared and millimeter wave (MMW) radar imagery may be recognized using fractal-based methods.
The technique is based on estimation of the fractal dimensions of sequential blocks of an image of a scene and slicing of the histogram of the fractal dimensions computed by Fourier regression. The technique is shown to be effective for the detection of tactical military vehicles in IR, and of airport attributes in MMW radar imagery. In addition to spatial self-similarity, temporal self-similarity may also be analyzed using fractal methods. See, Reusens, E., “Sequence coding based on the fractal theory of iterated transformations systems”, Proc. SPIE—The International Society for Optical Engineering, 2094(pt.1):1).
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This reference describes a scheme based on the iterated functions systems theory which relies on a 3D approach in which the sequence is adaptively partitioned. Each partition block can be coded either by using the spatial self similarities or by exploiting temporal redundancies. Fractal compression methods may be used for video data for transmission. See, Hurtgen, B.; Buttgen, P., “Fractal approach to low rate video coding”, Proc.
SPIE—The International Society for Optical Engineering, 2094(pt.1):1). This reference relates to a method for fast encoding and decoding of image sequences on the basis of fractal coding theory and the hybrid coding concept. The DPCM-loop accounts for statistical dependencies of natural image sequences in the temporal direction. Those regions of the original image where the prediction, i.e. Motion estimation and compensation, fails are encoded using an advanced fractal coding scheme, suitable for still images, and whose introduction instead of the commonly used Discrete Cosine Transform (DCT) -based coding is advantageous especially at very low bit rates (8-64 kbit/s). In order to increase reconstruction quality, encoding speed and compression ratio, some additional features such as hierarch.