Abstract: An embedded picture can be embedded with secret embedded payloads. A method is disclosed for performing trusted binding between a first participant and a second participant based on RSA cryptosystem using the embedded picture having a first area assigned to the first participant for a first secret embedded payload, and a second area assigned to the second participant for a second secret embedded payload. Multiple payloads may be embedded in the embedded picture by multiple participants. Each payload can only be encrypted and decrypted by its corresponding participant. Multiple participants may form a trusted network, in the network each participant can be recognized and validated digitally. Accordingly, various activities such as transaction of digital credit card, verification of digital identification (ID) card, transaction of digital currency note may be performed using the trusted network.

Abstract: Processes include a process for transmitting a payload embedded in a picture using payload-in-picture-encoding. The process may comprises transmitting a human recognizable picture by a first device, receiving the transmitted human recognizable picture by a second device, the received human recognizable picture is processed and transformed into a payload, wherein the payload is one of a 2D payload image, an encrypted 2D payload image, a text message, an encrypted text message, a file, and encrypted file.

Abstract: A reference 2D data is provided. The reference 2D data comprises a first plurality of pixels defined in 2D coordinates. The reference 2D data is transformed into a reference 1D data having a first 1D size. The reference 1D data comprises the first plurality of pixels in a transformed 1D order. A plurality of input 2D data are also provided. An input 2D data comprises a second plurality of pixels defined in 2D coordinates. The plurality of input 2D data are transformed into a plurality of input 1D data, which comprises transforming the input 2D data into an input 1D data. Transforming the input 2D data into the input 1D data is the same as transforming the reference 2D data into the reference 1D data. Finally, a transformed input 1D data from the plurality of input 1D data that matches the transformed reference 1D data is searched.

Abstract: An embedded picture can be embedded with secret embedded payloads. A method is disclosed for performing trusted binding between a first participant and a second participant based on RSA cryptosystem using the embedded picture having a first area assigned to the first participant for a first secret embedded payload, and a second area assigned to the second participant for a second secret embedded payload. Multiple payloads may be embedded in the embedded picture by multiple participants. Each payload can only be encrypted and decrypted by its corresponding participant. Multiple participants may form a trusted network, in the network each participant can be recognized and validated digitally.

Abstract: The present disclosure is directed to apparatuses, systems, and methods for automatically managing cost of sale (COS) bidding for merchants (alternatively referred to herein as “vendors”). Described herein are automated COS bidding processes (or automated COS bidding logic, modules, or engines) utilized such that merchants need not actively manage their bids (i.e., fees associated with displaying item listings). As described herein, embodiments automate merchant bids using a formula based, at least in part, on the price of the item, the cost of selling from the advertiser, and historical performance data.

Abstract: Processes include a process for transmitting a payload embedded in a picture using payload-in-picture-encoding. The process may comprises transmitting a human recognizable picture by a first device, receiving the transmitted human recognizable picture by a second device, the received human recognizable picture is processed and transformed into a payload, wherein the payload is one of a 2D payload image, an encrypted 2D payload image, a text message, an encrypted text message, a file, and encrypted file.

Abstract: A reference 2D data is provided. The reference 2D data comprises a first plurality of pixels defined in 2D coordinates. The reference 2D data is transformed into a reference 1D data having a first 1D size. The reference 1D data comprises the first plurality of pixels in a transformed 1D order. A plurality of input 2D data are also provided. An input 2D data comprises a second plurality of pixels defined in 2D coordinates. The plurality of input 2D data are transformed into a plurality of input 1D data, which comprises transforming the input 2D data into an input 1D data. Transforming the input 2D data into the input 1D data is the same as transforming the reference 2D data into the reference 1D data. Finally, a transformed input 1D data from the plurality of input 1D data that matches the transformed reference 1D data is searched.

Abstract: One embodiment of a non-word-based information retrieval system includes searching stock or image documents in a huge data source. A non-word-based document is first divided into a series of elements or an array of cells. Each element or cell is matched against a series of predefined token patterns, so that a match will generate a token having a name. The collection of the generated named tokens is a word-based representation of the non-word-based document. After tokens from all documents are collected in a master collection of tokens, the non-word-based documents can be efficiently and systematically searched in a manner analogous to a document search in a word-based search system.

Abstract: Documents are searched by a target document. The target document is tokenized into buta strings. The buta strings are decomposed into buta attribute values. A target buta attribute value is selected. A tolerance is given to the target buta attribute value. A buta attribute range is determined from the given tolerance. Buta attribute value suggestions are lookup results within the buta attribute range in dictionary of index II, which relates a buta attribute value to buta strings. Alternative buta strings are searched using the buta attribute value suggestions in dictionary of index II. Finally, documents can be searched using the alternative buta strings in dictionary of index I, which relates a buta string to documents.

Abstract: Documents are searched by a target document. The target document is tokenized into buta strings. The buta strings are decomposed into buta attribute values. A target buta attribute value is selected. A tolerance is given to the target buta attribute value. A buta attribute range is determined from the given tolerance. Buta attribute value suggestions are lookup results within the buta attribute range in dictionary of index II, which relates a buta attribute value to buta strings. Alternative buta strings are searched using the buta attribute value suggestions in dictionary of index II. Finally, documents can be searched using the alternative buta strings in dictionary of index I, which relates a buta string to documents.

Abstract: One embodiment of a non-word-based information retrieval system includes searching stock or image documents in a huge data source. A non-word-based document is first divided into a series of elements or an array of cells. Each element or cell is matched against a series of predefined token patterns, so that a match will generate a token having a name. The collection of the generated named tokens is a word-based representation of the non-word-based document.

Abstract: One embodiment of a non-word-based information retrieval system includes searching stock or image documents in a huge data source. A non-word-based document is first divided into a series of elements or an array of cells. Each element or cell is matched against a series of predefined token patterns, so that a match will generate a token having a name. The collection of the generated named tokens is a word-based representation of the non-word-based document. After tokens from all documents are collected in a master collection of tokens, the non-word-based documents can be efficiently and systematically searched in a manner analogous to a document search in a word-based search system.

Abstract: One embodiment of a non-word-based information retrieval system includes searching stock or image documents in a huge data source. A non-word-based document is first divided into a series of elements or an array of cells. Each element or cell is matched against a series of predefined token patterns, so that a match will generate a token having a name. The collection of the generated named tokens is a word-based representation of the non-word-based document.