Canon Kabushiki Kaisha

Canon Inc.

Michael G Roberts

Harry T Garland

Kenneth M Hunter

Roger D Melen

Gerald A May

Hadar I Avi-Itzhak

Ken Hunter

Jonathan DeVito

System and method for archiving and retrieving documents based on attributes of the document. The attributes are located by a processor that searches the document for a predefined set of attributes. The attributes may be particular words, word locations, font sizes, or other properties that can be located by a computer processor. The values associated with the attributes are stored in an index along with the document location. An attribute search may be performed on the index enabling quick location of documents with similar attributes to the search. A file provided the list of documents in order of similarity is provided as a result of the search. The system and method may further be used to retrieve similar documents by locating documents with similar attribute values to a provided document creating a file linked to the similar documents.

Archival and retrieval of similar documents

A method and system for enhancing a digital image stores the original image and a series of codes describing the manipulations performed on the image. Each time the image is retrieved from an original image archive (,) for viewing on a display (,), corresponding codes are retrieved from an original sequence archive (,). If the codes have previously been optimized, the image processing system (,) applies the optimized codes to the image in order to recreate the enhanced image. If the codes have not been optimized, an optimizer (,) combines and reorders the image enhancement codes in light of certain criteria, including minimizing information loss. A strategy used by the optimizer (,) to minimize information loss separates the codes into sequences and then commutes and combines the codes based on the code types. The optimized codes are applied to the image and produce the image that is displayed on the display (,) and saved in the enhanced image cache memory (,).

Optimized enhancement of digital images

Transformation system 10A transforms target data from data base 11B into target' data by the execution of transform functions on the target data. Transform register 13 stores transformation UIDs (target UIDs and function UIDs and target' UIDs) for providing UID transformation instructions to effect the transformation of target data into target' data. The transform register has target column of rows 13T which store target UIDs identifying target data to be transformed. The transform register also has function column of rows 13F which store function UIDs. The function UID identifies a transform function for execution on the target data identified by the target UID stored in the target column along the same row as the function UID. The transform register also has a target' column of rows 13T' which store target' UIDs. The target' UID identifies target' data transformed from the target data by the execution of the transform function identified by UIDs stored along the same row as the target' UID. The target' data may then be stored in data base 11B and retrieved as required. Transform function roster 11R holds the transform functions (O, P, T . . . ), and is responsive to UID dispatcher 11D and the function UIDs stored in the function column of the transform register for providing the transform function identified thereby. Function execution unit 11U is responsive to the transform functions held in the transform function roster and to the target data identified by the target UIDs, for executing the transform functions on the target data to provide the target' data.

Transformation system and method for transforming target data

A system of data storage tables (10A) include an extendable subject table (12) for storing subject UID entries, and an extendable element table (16) for storing UID element entries and aggregate UID entries and role UID entries. The UIDs stored in the tables specify relationships between the UIDs, and collectively define a tangle (10B) of connecting paths. The tangle spreads out between subject nodes corresponding to subject UIDs, and aggregation nodes corresponding to aggregate UIDs, and role nodes corresponding to role UIDs. Each subject node and aggregation node and role node is path connected through the tangle to other subject UIDs and aggregate UIDs and role UIDs defining the relationship between the UIDs in the tables.

Extendable data storage system and method for storing relationships

A method and system for storing and selectively retrieving information, such as words, from a document set. The method includes generating an image data set representative of the information contained in the document set. The method also involves generating a text data set representative of a text portion of the information contained in the document set. A text-image correspondence (TIC) table is generated that includes data representative of coordinates information corresponding to each phrase of the document set. A search phrase is identified in response to user-specified search criteria and the search phrase is identified in the text image data set. Then, the TIC table is used to identify the coordinates information corresponding to the search phrase identified in the text data set. A display of the portion of the page containing the search phrase is generated using the coordinates information.

Selective document retrieval method and system

Method and system for selectively retrieving information contained in a stored document set using a metric-based or 'fuzzy' finite-state non-deterministic automaton. An automaton is constructed (501) corresponding to a text string query, text strings are read (502) from storage and corresponding dissimilarity values are generated (505). Those strings resulting in values less than a given threshold are recorded (508) and listed for the user. Dissimilarity values are determined based on penalties associated with missing characters, extra characters, incorrect characters, and other differences between the text string query and a text string read from storage.

Non-literal textual search using fuzzy finite non-deterministic automata

Method and system for selectively retrieving information contained in a stored document set using a non-literal, or 'fuzzy', search strategy. A text string query is transmitted (200) to a computer processor, and a dissimilarity value D.sub.i is assigned (208) to selected ones of stored text strings representative of information contained in a stored document set, based upon a first set of rules (106). A set of retrieved text strings representative of stored information and related to the text string query is generated (212), based upon a second set of rules (107). Each of the retrieved text strings has an associated dissimilarity value D.sub.i, which is a function of at least one rule R.sub.n from the first set of rules (106) used to retrieve the text string and a weight value w.sub.n associated with that rule R.sub.n. The retrieved text strings are displayed (216) preferably in an order based on their associated dissimilarity value D.sub.i. Once one or more of the retrieved text strings is chosen, the weight value w.sub.n associated with at least one rule of the first set of rules (106) is adjusted (220) and stored.

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Waikoloa, HI, United States of America

Michael G Roberts