The Internet enables a user of a client computer system to identify and communicate with millions of other computer systems located around the world. A client computer system can identify each of these other computer systems using a unique numeric identifier for that computer called an “IP address.” When a communication is sent from a client computer system to a destination computer system, the client computer system typically specifies the IP address of the destination computer system in order to facilitate the muting of the communication to the destination computer system. For example, when a request for a World Wide Web page (“Web page”) is sent from a client computer system to a Web server computer system (“Web server”) from which that Web page can be obtained, the client computer system typically includes the IP address of the Web server.
In order to make the identification of destination computer systems more mnemonic, a Domain Name System (DNS) has been developed that translates a unique alphanumeric name for a destination computer system into the IP address for that computer. The alphanumeric name is called a “domain name.” For example, the domain name for a hypothetical computer system operated by Example Corporation may be “comp23.example.com”. Using domain names, a user attempting to communicate with this computer system could specify a destination of “comp23.example.com” rather than the particular IP address of the computer system (e.g., 198.81.209.25). Domain names may include character sets such as upper and lowercase letters a-z and digits 0-9. Internationalized Domain Names (IDN) are domain names that include characters used in the local representation of languages that are not written with the twenty-six letters of the basic Latin alphabet “a-z”. An IDN can contain Latin letters with diacritical marks, as required by many European languages, or may include characters from non-Latin scripts such as Arabic or Chinese. Many languages also use other types of digits than the European “0-9”. The basic Latin alphabet together with the European-Arabic digits are, for the purpose of domain names, termed “ASCII characters” (ASCII=American Standard Code for Information Interchange). These are also included in the broader range of “Unicode characters” that provides the basis for IDNs.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate, together with the description, examples of the present disclosure. In the figures:
With the introduction and increasing use of internationalized domain names (IDNs), registrants may encounter new challenges when registering a domain name. For example, in scripts that are new to the Internet Naming space, but used by a large population of users (i.e., Chinese, Cyrillic, Hangul, Arabic, etc.), a user may generate a domain name request utilizing one or more graphemes that, although they may visually appear to be the same, are not exactly the same as the one or more graphemes in a registered domain name. This may result in a request for a non-existent domain (NXD). NXD data, including the domain name request, may be stored in a storage for analysis.
DNS Registry operators are interested in Identifying domain names that match keywords for multiple reasons, Including NXD monitoring, drop catch notification, trademark monitoring and searching IDNs. While there are existing matching algorithms to conduct natural language searches, the existing matching algorithms may be deficient when conducting multilingual keyword matching with domain names across natural languages.
As discussed herein, a multilingual keyword matching service may be performed with domain names across natural languages. The multilingual, or language-independent, keyword matching service may assist in identifying variants of domain names across natural languages. Given Universal character set transformation format encoded keywords, for example, 8-bit (UTF8)-encoded keywords, in any language, the keyword matching service may return a plurality of highly relevant domain names across multiple TLDs matching a set of input keywords. The keywords can comprise keywords in a single natural language, or can include a mix of different natural languages.
The keyword matching service can operate synchronously or asynchronously, and can be tuned to provide responses at varying degrees of verbosity. The keyword matching algorithm may generally be implemented as a substring search. The keyword matching may incorporate a controlled vocabulary including a stopword list.
The processes discussed herein may utilize an in-memory n-gram index for fast lookups. This system features an indexing process that balances the need for a high degree of precision and recall across multiple languages while at the same time keeping index size to a manageable level. The process may include creating an inverted n-gram index, or a plurality of n-grams, given a set of domain names where n equals a range from a lower bound to an upper bound. These bounds are configurable on a per-language basis, and may be tuned to best meet the precision and recall goals of a given language. For example, in languages with a large number of more expressive characters (such as some Asian languages), the system may have a smaller lower bound than with most Latin-based languages. By comparing the n-grams created from the keywords with n-grams created from the domains, domains that match the keywords may be identified.
As discussed herein, an input string may be accessed where the input string includes a keyword to be compared to one or more domains. A UTF-encoded input string may be generated from the input string. The UTF-encoded input string may be parsed via an n-gram parser to generate a plurality, for example, a list, of input string n-grams. A domain to be compared may be accessed and a UTF-encoded domain string may be generated from the accessed domain. The UTF-encoded domain string may be parsed to generate a plurality of domain string n-grams from the UTF-encoded domain string. The plurality of input string n-grams may be compared to the plurality of domain string n-grams. When one or more n-grams in the plurality of input string n-grams match one or more n-grams in the plurality of domain string n-grams, this may indicate that the input string matches the domain.
According to some examples, a relevance score may be generated for each of the identified matches. The relevance score may be calculated based on the number of input string n-grams that match the domain string n-grams in order to provide an indication of the degree of relevance of the match. A higher relevance score may indicate that a match of the domain is closer to the input string where a lower relevance score may indicate that a match of the domain is not as close to the input string.
As further discussed herein, a plurality of input string n-grams may be generated by accessing an input string and generating a UTF-encoded input string from the input string. The UTF-encoded input string may be parsed via an n-gram parser. A plurality of input string n-grams may be generated, where a length of each of the input string n-grams is based on a lower bound and upper bound. The generated plurality of Input string n-grams may be provided to determine matches between the plurality of input string n-grams with plurality of n-grams generated from domains.
According to some examples, a domain name input stream may be received where the domain name input stream includes a plurality of domain names. For each domain name in the domain name input stream, a UTF-encoded domain string may be generated and parsed to generate a plurality of domain string n-grams from the UTF-encoded domain string. A plurality of input string n-grams generated from a UTF-encoded input string may be accessed. The plurality of n-grams from the UTF-encoded input string may be compared to the plurality of n-grams of each of the UTF-encoded domain strings. Matches between the input string and domain names may be identified based on the comparison of the input string n-grams and the domain string n-grams. An alert may be generated including the matches of the input string and the domain names.
The matches of the input string and the domain names may indicate variants of the domain names. By determining variants of a domain name, a registrant of a domain name may have the opportunity to register variants of a domain name. This may ensure that other users access the intended registered domain, even if the domain name a user is trying to access includes one or more graphemes that are different from the graphemes in the registered domain name.
While the examples discussed herein are made with respect to UTF-8 encoding, it may be appreciated that other UTF encoding may be utilized, for example, UTF-16, UTF-32, or any other encoding that supports UTF.
As shown in
Device 102 may be implemented as a server, a mainframe computer, any combination of these components, or any other appropriate computing device or resource service, for example, a cloud, etc. Device 102 may be standalone, or may be part of a subsystem, which may, in turn, be part of a larger system. It may be appreciated that, while device 102 may be described as including various components, one or more of the components may be located at other devices (not shown) within system environment 100.
Client device 104 may be implemented as any computing device, for example, a desktop computer, laptop computer, portable computing device, etc. Client device 104 may enable communication with device 102, enable providing Input strings for matching, and receive indications of matches of input strings with domains, among other things as described herein.
Additionally, each of devices 102 and 104 includes the necessary hardware and/or software needed to communicate with the network 106 via a wired and/or a wireless connection. Device 102 and 104 may be embodied by a server computing device, desktop/laptop/handheld computers, wireless communication devices, personal digital assistants or any other similar devices having the necessary processing and communication capabilities. In an embodiment, the network 106 may comprise a public communication network such as the Internet or World Wide Web and/or a private communication network such as a local area network (LAN), wide area network (WAN), etc.
One or both of devices 102 and 104, which may comprise one or more suitable computing devices, implement the functionality as discussed herein.
As discussed herein, devices 102 and 104 include one or more processors in communication with one or more storage devices. The processor(s) may comprise a microprocessor, microcontroller, digital signal processor, co-processor or other similar devices known to those having ordinary skill in the art. In addition, the storage device(s) as discussed herein may comprise a combination of non-transitory, volatile or nonvolatile memory such as random access memory (RAM) or read only memory (ROM). Such storage devices may be embodied using any currently known media such as magnetic or optical storage media including removable media such as floppy disks, compact discs, etc. One or more storage devices has stored thereon instructions that may be executed by the one or more processors, such that the processor(s) implement the functionality described herein. In addition, or alternatively, some or all of the software-implemented functionality of the processor(s) may be implemented using firmware and/or hardware devices such as application specific Integrated circuits (ASICs), programmable logic arrays, state machines, etc.
N-gram parser 202 may be utilized by one or more of the exact match comparator 204 and the fuzzy match comparator 206 to generate a plurality of n-grams for a given input. N-gram parser 202 may generate a plurality of n-grams for Unicode strings. The n-gram parser 202 may utilize a lower and upper bound for n, for example, based on a particular Unicode range Including a first character of the string. According to some examples, the Unicode range may be determined based on a specific language. Thus, the system may utilize different lower and upper bounds for different languages.
According to some examples, the entire string may be added to the plurality of n-grams, as an n-gram, when the string length is less than the lower indexing bound or when the string is an exact match with a stop word from a pre-configured stop word list.
According to some examples, the n-gram parser 202 may add prefixes and/or suffixes of the input strings to the plurality of n-grams. The length of the prefix and/or suffix of an Input string may be equal to, for example, the lowerbound-m, where m is an integer, for example, 1, etc.
According to some examples, n-gram parser 202 may parse a string to add to the plurality of n-grams using the “-” character as a delimiter, regardless of the value of the lower or upper bound of n. Here, the string of characters, for example, all of the characters, before the “-” and the string of characters, for example, all of the characters, after the “-” may be added to the plurality of n-grams.
According to some examples, the n-gram parser 202 may add the entire string to the plurality of n-grams regardless of the value of the lower or upper bound of n.
Exact match comparator 204 may compare n-grams generated from input strings with n-grams generated from domains in order to identify exact matches. According to some examples, the exact match comparator 204 may compare ASCII and punycode IDNs with input strings, or keywords, comprising Unicode strings of different encoding, including for example UTF-encoded, by comparing n-grams generated therefrom. According to some examples, the exact match comparator 204 may utilize n-grams of a length that is bounded by a lower bound and an upper bound based on a particular Unicode range. If matches between one or more input string n-grams and domain string n-grams are found, matches between the input string and the domains may be identified. According to some examples, matches between the input string and the domains may be based on a relevance score generated by relevance score calculator 208 discussed below.
Fuzzy match comparator 206 may compare n-grams generated from input strings with n-grams from domains in order to identify fuzzy (non-exact) matches. Fuzzy matches may be based on, a comparison between n-grams utilizing, for example, an edit distance calculation, or other suitable calculations. According to some examples, the fuzzy match comparator 206 may compare ASCII and punycode IDNs to keywords comprising Unicode strings of different encoding, including for example UTF-8, by comparing n-grams generated therefrom. According to some examples, the fuzzy match generator may utilize n-grams of a length that is bounded by a lower bound and an upper bound based on a particular Unicode range. If matches between one or more input string n-grams and domain string n-grams are found, matches between the input string and the domains may be identified. According to some examples, matches between the input string and the domains may be based on a relevance score generated by relevance score calculator 208 discussed below.
Relevance score calculator 208 may calculate a relevance score for use by, for example, by the exact match comparator 204. The relevance score may be the sum of the number of input string n-grams matched with domain n-grams and a similarity score, for example, a dice coefficient similarity score between the domain n-grams and input string n-grams.
Network interface application 210 facilitates network communication between device 102 and device 104.
Processor 212 may execute computer-readable instructions, stored in storage (not shown in
IDN processor 214 may be used by the n-gram parser 202 to convert, where needed, input strings of type ASCII, punycode, or Unicode to a normalized UTF-8 form. According to some examples, IDN processor 214 may strip out any top-level domain suffix.
According to some examples, device 200 may include data storage (not shown) to store domain information for use within device 200. As discussed herein, domain information relating to domains may include non-existent domain (NXD) data, registered domain name information, pending domain names that are to be deleted, or de-registered, registered domain name Information from one or more Top-Level Domains, registered domain name Information from one or more WHOIS databases, registered domain data from Trademark Clearing House Data, etc. NXD data may include domain names that were included in requests generated by users at client devices, where the domain name in the request resulted in a non-existent domain name.
According to some examples, device 200 may include a storage (not shown), to store input strings for use within device 200. As discussed herein, input strings may Include one or more keywords, one or more trademarks, one or more domains, one or more domain names, etc. According to some examples, the storage storing input strings may be the same, or different than the storage storing domain information.
Alert generator 216 may generate one or more alerts Including information generated by device 200. According to some examples, alert generator 218 may generate an alert including input string n-grams. According to some examples, alert generator 216 may generate an alert including one or more domains that match one or more input strings. According to some examples, alert generator may generate an alert including one or more domains that match one or more input strings together with a relevance score indicating a relevance of one or more of the domains. The alert generated by alert generator 216 may be output to an output device (not shown) of device 200, may be stored in a local storage, or may be transmitted to a device remote from device 200, for example, a client device, an administrative device, a storage device, etc.
According to some examples, the entire string may be used as an n-gram when the sting length is less than the lower bound or when the string is an exact match with a stop word from a pre-configured stop word list.
According to some examples, the n-gram parser 304 may add prefixes and/or suffixes of the input string to the plurality of n-grams. The length of the prefix and/or suffix of an input string may be of a length equal to the lowerbound −1.
According to some examples, n-gram parser 304 may parse the input string to produce n-grams to add to the plurality of n-grams by using the “-” character as a delimiter regardless of the value of the lower or upper bound of n.
According to some examples, the n-gram parser 304 may add the entire input string to the plurality of n-grams regardless of the value of the lower or upper bound of n.
Domain string 306 may be passed to n-gram parser 308. The domain string may be one or more domain strings in a UTF-8 format. N-gram parser 308 may parse the domain string to generate a plurality of domain string n-grams. The n-gram parser 308 may be generate n-grams of a length of n, n being an integer ranging from a lower bound to an upper bound equal to the length of the input string. According to some examples, the lower and upper bound for n may be based on a Unicode range of a first character of the domain string.
According to some examples, the entire string may be added to the plurality of n-grams as an n-gram when the string length is less than the lower indexing bound or when the string is an exact match with a stop word from a pre-configured stop word list.
According to some examples, the n-gram parser 308 may add prefixes and/or suffixes of the domain string to the plurality of n-grams. The length of the prefix and/or suffix of the domain string may be equal to the lowerbound −1.
According to some examples, n-gram parser 308 may parse the domain string to produce n-grams to add to the plurality of n-grams using the “-” character as a delimiter regardless of the value of the lower or upper bound of n.
According to some examples, the n-gram parser 384 may add the entire Input string to the plurality of n-grams regardless of the value of the lower or upper bound of n.
The plurality of input string n-grams and the plurality of domain string n-gram may be passed to match comparator 310 to identify matches. Match comparator 310 may utilize exact match comparator 204 and/or fuzzy match comparator 206 as discussed with regard to
Match comparator 310 outputs matches between the input string and the domain string based on matches between the plurality of input string n-grams and the plurality of domain string n-grams. According to some examples, a match may be determined if one or more input string n-grams matches one or more domain string n-grams.
According to some examples, the matches may be analyzed to calculate a relevance score, via relevance score calculator 208. The relevance score may be based on keyword and Input string n-gram matches with domain string n-gram matches. The relevance score may be the sum of the number of input string n-grams matched with domain string n-grams and a similarity score, for example, a dice coefficient similarity score between the domain string n-grams and input string n-grams. The relevance scores may be associated with the respective matches and output. According to some examples, a minimum threshold value for the relevance score may be predefined, where only matches having a score at or above the predefined minimum threshold may be output.
According to some examples, an alert may be generated, for example, by alert generator 216, the alert including the matches between the input strings and the domains and may be passed, for example, to an output device, to a remote device, etc. An alert may be generated when one or more matches is found between an input string and a domain string.
According to some examples, the entire string may be added to the plurality of n-grams as an n-gram when the string length is less than the lower indexing bound or when the string is an exact match with a stop word from a pre-configured stop word list.
According to some examples, the n-gram parser 404 may add prefixes and/or suffixes of the input string to the plurality of n-grams. The length of the prefix and/or suffix of the input string may be equal to the lowerbound −1.
According to some examples, n-gram parser 404 may parse the input string to add to the plurality of n-gram using the “-” character as a delimiter regardless of the value of the lower or upper bound of n.
According to some examples, the n-gram parser 404 may add the entire input string to the plurality of n-grams regardless of the value of the lower or upper bound of n.
Domain string 406 may be passed to n-gram parser 408. The domain string may be one or more domain strings in a UTF-8 format. N-gram parser 408 may parse the domain string to generate a plurality of domain string n-grams. The n-gram parser 408 may be generate n-grams of a length of n, n being an integer, from a lower bound to the length of the input string. According to some examples, the lower and upper bound for n may be based on a Unicode range of a first character of the domain string.
According to some examples, the entire string may be added to the plurality of n-grams as an n-gram when the string length is less than the lower Indexing bound or when the string is an exact match with a stop word from a pre-configured stop word list.
According to some examples, the n-gram parser 408 may add prefixes and/or suffixes of the domain string to the plurality of n-grams. The length of the prefix and/or suffix may be equal to the lowerbound −1.
According to some examples, n-gram parser 408 may parse the domain string to add to the plurality of n-grams using the “-” character as a delimiter regardless of the value of the lower or upper bound of n.
According to some examples, the n-gram parser 408 may add the entire input string to the plurality of n-grams regardless of the value of the lower or upper bound of n.
The plurality of n-grams generated by n-gram parser 408 may be passed to 410 where the system loops through each domain string n-gram. Each domain n-gram may be passed to 412 where the system loops through each input string n-gram. Each domain n-gram is compared to each input n-gram and an edit distance is calculated 414. A predefined edit distance factor is utilized to determine if the calculated edit distance is greater than or less than a predetermined edit distance factor. This comparison determines the degree of similarity between the two n-grams, where a small edit distance indicates a large degree of similarity and a larger edit distance indicates a smaller degree of similarity. If the edit distance is less than the edit distance factor (414, YES), indicating an acceptable degree of similarity between the domain n-gram and the input n-gram, then the matched domain name is stored 416. If the edit distance is greater than the edit distance factor (414, NO), then the domain string is discarded 418.
Process 400 proceeds to process n-grams as described until al of the generated domain n-grams are compared to all of the input string n-grams.
According to some examples, an alert may be generated, for example, by alert generator 216, the alert including the stored matched domain names and may be passed, for example, to an output device, to a remote device, etc.
As can be seen in
As can be seen in
If the input string is in the stopword list, the entire input string is added to the n-gram index and the process is completed.
If the Input string is not in the stopword list, and if the length of the input string is less than the lower bound, the input string is added to the n-gram index and the process is completed. If the length of the input string is less than or equal to the upper bound, then the upper bound is set as the length of the input string. If the length of the input string is not less than or equal to (i.e., greater than) the upper bound, then the input string is added to the n-gram index. Further n-grams are indexed for the input string from the lower bound to the upper bound. For each n-gram, if the n-gram does not contain a “-”, the n-gram is added to the n-gram index. Further, prefixes and suffixes for the input string are indexed where the length of the prefix of the input string is equal to the lower bound −1, and the length of the suffix is equal to the length of the input string—(lowerbound −1). If the input string Includes a “-”, “-” is used as a delimiter where in the input string is split into multiple strings, for example, all characters before the “-” may be one substring, and all of the characters after the “-” may be another substring. Each of the substrings may be added to the n-gram index if they are not already in the n-gram index. The process is completed.
A determination is made whether the input string is an IDN 604. For example, it may be determined whether the input string has an “XN” prefix. If the input string has an “XN” prefix, the string may be an IDN domain. If not, then the string is not an IDN domain. If it is determined that the input string is an IDN (604, YES), then the TLD name is stripped from the IDN input string and the punycode for the remainder is decoded to a UTF-8 string 606. The UTF-8 string is then output. If the input string Is not an IDN (604, NO), then processing proceeds to 610.
At 610, a determination is made whether the input string is a domain string. For example, it may be determined if the Input string is a non-IDN, ASCII domain. If the input string is a domain string (610, YES), then the TLD is stripped from the input string and the remainder of the input string is converted to UTF-8 612. The UTF-8 string is then output 608. If the input string is not a domain string (610, NO), then processing proceeds to 612.
At 612, the Input string is decoded to UTF-8. The UTF-8 string is then output 608.
According to some examples, the UTF-8 string 608 is utilized as the input string and/or the domain that is input to n-gram parsers as more fully discussed herein.
The computing apparatus 700 includes one or more processors 702, such as the processor(s) 212. The processor(s) 702 may be used to execute some or all of the steps, operations and functions described in the processes, methods and systems depicted in
The removable storage drive 710 may read from and/or write to a removable storage unit 714 in a well-known manner. User input and output devices 716 may Include a keyboard, a mouse, a display, etc. A display adaptor 718 may interface with the communication bus 704 and the display 720 and may receive display data from the processor(s) 702 and convert the display data into display commands for the display 720. In addition, the processor(s) 702 may communicate over a network, for Instance, the Internet, LAN, etc., through a network adaptor 722.
As shown in
Keyword matching service 908 matches domains to keywords, as more fully discussed above, and returns results to the user interface as HTML, XML, RSS, or JSON.
Keyword matching service 1010 receives a plurality of keyword n-grams, generated by the n-gram parser 1016 from a pre-configured set of keywords 1018, and a plurality of domain n-grams, generated by the n-gram parser 1014 from a domain input stream 1012.
Keyword matching service 1010 determines one or more matching domains and writes the one or more matching domains to a message queue 1008. The message queue 1008 disseminates the one or more matching domains, for example, via SMS message 1002, email 1004, EPP poll message 1006, or other channels of communication (not shown).
The following is an example of an input strings processed by the functionality discussed with regard to
The following is what the entire inverted n-gram index looks like after the aforementioned examples have been indexed (format [gram]=[domain1][domain2][etc.]:
YVE=MYVERISIGNTV.TV
ERIS=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
NAM=SHORT-NAME.CC
F=XN-PCKPZO4A2F8ETH.TV
SIG=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
ERISI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
WERED=POWERED-BY-VERISIGN.COM
BY=POWERED-BY-VERISIGN.COM
YVERI=MYVERISIGNTV.TV
POWER=POWERED-BY-VERISIGN.COM
=XN-PCKPZO4A2F8ETH.TV
RIS=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
MYV=MYVERISIGNTV.TV
ISIGNTV=MYVERISIGNTV.TV
MYVERIS=MYVERISIGNTV.TV
-PCKPZO4A2F8ETH.TV
RISI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
SIGN=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
WER=POWERED-BY-VERISIGN.COM
VERISIG=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
-=XN-PCKPZO4A2F8ETH.TV
GN=POWERED-BY-VERISIGN.COM
VERISI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
POWERED=BY-VERISIGN-POWERED-BY-VERISIGN.COM
MYVERISIGN=MYVERISIGNTV.TV
RISIGNTV=MYVERISIGNTV.TV
XN-PCKPZO4A2F8ETH.TV
SHORT=SHORT-NAME.CC
-=XN-PCKPZO4A2F8ETH.TV
RISIGN=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
YVERISI=MYVERISIGNTV.TV
PO=POWERED-BY-VERISIGN.COM
RISIGNT=MYVERISIGNTV.TV
VERIS=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
=XN-PCKPZO4A2F8ETH.TV
=XN-PCKPZO4A2F8ETH.TV
ISIGN=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
IGN=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
IGNTV=MYVERISIGNTV.TV
AND=AND.COM
MYVERISIG=MYVERISIGNTV.TV
OWERED=POWERED-BY-VERISIGN.COM
ERISIG=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
MYVER=MYVERISIGNTV.TV
MY=MYVERISIGNTV.TV
SHORT=NAME-SHORT-NAME.CC
POWERED=POWERED-BY-VERISIGN.COM
ERISIGNT=MYVERISIGNTV.TV
ISI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
-=XN-PCKZO4A2F8ETH.TV
HORT=SHORT-NAME.CC
RED=POWERED-BY-VERISIGN.COM
YVER=MYVERISIGNTV.TV
ISIG=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
YVERISIGNTV=MYVERISIGNTV.TV
YVERISIG=MYVERISIGNTV.TV
OWERE=POWERED-BY-VERISIGN.COM
=XN-PCKPZO4A2F8ETH.TV
POWE=POWERED-BY-VERISIGN.COM
ME=SHORT-NAM E.CC
YVERISIGNT=MYVERISIGNTV.TV
-=XN-PCKPZO4A2F8ETH.TV
SIGNTV=MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
-=XN-PCKPZO4A2F8ETH.TV
XY=XY.COM
ERISIGN=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
IGNT=MYVERISIGNTV.TV
NAME=SHORT-NAME.CC
POWERE=POWERED-BY-VERISIGN.COM
NTV=MYVERISIGNTV.TV
SH=SHORT-NAME.CC
ERED=POWERED-BY-VERISIGN.COM
=XN-PCKPZO4A2F8ETH.TV
GNT=MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
ERE=POWERED-BY-VERISKGN.COM
ERISIGNTV=MYVERISIGNTV.TV
OWER=POWERED-BY-VERISIGN.COM
GNTV=MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
OWE=POWERED-BY-VERISIGN.COM
POW=POWERED-BY-VERISIGN.COM
ERI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
MYVERISI=MWVERISIGNTV.TV
SHOR=SHORT-NAME.CC
MYVE=MYVERISIGNTV.TV
RISIG=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
HOR=SHORTNAME.CC
YVERISIGN=MYVERISIGNTV.TV
VERI=POWERED-BY-VERISIGN.COM MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
VERISIGNTV=MYVERISIGNTV.TV
-=XN-PCKPZO4A2F8ETH.TV
MYVERISIGNTV=MYVERISIGNTV.TV
SIGNT=MYVERISIGNTV.TV
TV=MYVERISIGNTV.TV
MYVERISIGNTV=MYVERISIGNTV.TV
-=XN-PCKPZO4A2F8ETH.TV
VERISIGNT=MYVERISIGNTV.TV
YVERIS=MYVERISIGNTV.TV
MYVERI=MYVERISIGNTV.TV
=XN-PCKPZO4A2F8ETH.TV
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The following is an example of a Simple Exact Match Search Scenario utilizing the Index created in the six examples set forth above. The process for performing the exact match search is discussed, for example, in
Other embodiments of the Invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is Intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It may be appreciated that other encodings may be used, for example, UTF-16, UTF-32, other encodings that support Unicode, etc.
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Entry |
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Number | Date | Country | |
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20150278188 A1 | Oct 2015 | US |