Abstract:
A system and a method for detecting content similarities in different emails employing selective truncation are disclosed. In one embodiment, a method comprises generating a first token value dependent on a first subset of characters at a beginning portion of a first email document, generating a second token value dependent on a second subset of characters at an ending portion of a first email document, and depending upon the first and second token values, selectively generating one or more hash values corresponding to a sequence of characters between the first subset and the second subset. The method further comprises generating a third token value dependent on a third subset of characters at a beginning portion of a second email document, generating a forth token value dependent on a forth subset of characters at an ending portion of a second email document, depending upon the first and second token values, and selectively generating one or more hash values corresponding to a sequence of characters between the first subset and the second subset. The method finally comprises comparing the one or more hash values corresponding to the sequence of characters between the first subset and the second subset with the one or more hash values corresponding to the sequence of characters between the third subset and the fourth subset.

Description:
[0001]    This application claims priority to U.S. provisional patent application Ser. No. 60/976,455, entitled “System And Method For Detecting Content Similarity Within Emails Documents Employing Selective Truncation”, filed Sep. 30, 2007. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]    1. Field of the Invention 
         [0003]    This invention relates to email systems, and more particularly to the detection of similarities within email documents. 
         [0004]    2. Description of the Related Art 
         [0005]    Frequently, it is desired to efficiently find similar emails located in a database. Often, emails may be near duplicates because an email is forwarded or replied to without much added text. However, searching through an extensive database and comparing emails to determine potentially similar emails can be a problematic process. One approach for comparing emails for similarity is to compute a hash value from the content of differing emails and then compare the hash values for equality. Unfortunately, such approaches would typically only identify emails that are exact duplicates, since any differences in the emails would typically result in the generation of different hash values. Another possible approach is to compare every word of an email against the words of another to determine similarity. However, such an approach is typically very computationally intensive. 
       SUMMARY 
       [0006]    A system and a method for detecting content similarities in different emails employing selective truncation are disclosed. In one embodiment, a method comprises generating a first token value dependent on a first subset of characters at a beginning portion of a first email document, generating a second token value dependent on a second subset of characters at an ending portion of a first email document, and depending upon the first and second token values, selectively generating one or more hash values corresponding to a sequence of characters between the first subset and the second subset. The method further comprises generating a third token value dependent on a third subset of characters at a beginning portion of a second email document, generating a forth token value dependent on a forth subset of characters at an ending portion of a second email document, depending upon the first and second token values, and selectively generating one or more hash values corresponding to a sequence of characters between the first subset and the second subset. The method finally comprises comparing the one or more hash values corresponding to the sequence of characters between the first subset and the second subset with the one or more hash values corresponding to the sequence of characters between the third subset and the fourth subset. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  is a block diagram of a computer system suitable for implementing a similarity detection mechanism, according to one embodiment. 
           [0008]      FIG. 2  is a flowchart of one embodiment of a method to compare email documents. 
           [0009]      FIG. 3  depicts content of two exemplary emails. 
           [0010]      FIG. 4  depicts two exemplary emails with extraneous content removed. 
           [0011]      FIG. 5A  depicts an example of tokenizing two words. 
           [0012]      FIG. 5B  depicts a list of tokenized words from the two exemplary emails. 
           [0013]      FIG. 6  depicts exemplary sliding windows. 
           [0014]      FIGS. 7 and 8  depict exemplary subsets from the two exemplary emails. 
           [0015]      FIG. 9  depicts the exemplary hashed character sequences. 
       
    
    
       [0016]    While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. It is noted that the word “may” is used throughout this application in a permissive sense (i.e., having the potential to, being able to), not a mandatory sense (i.e., must). 
       DETAILED DESCRIPTION 
       [0017]    Turning now to  FIG. 1 , a block diagram of one embodiment of a computer system  100  is shown. Computer system  100  includes a storage subsystem  110  coupled to a processor subsystem  150 . Storage subsystem  110  is shown storing an email database  120  and similarity detection code  130 . Computer system  100  may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop or notebook computer, mainframe computer system, handheld computer, workstation, network computer, a consumer device such as a mobile phone, pager, or personal data assistant (PDA). Computer system  100  may also be any type of networked peripheral device such as storage devices, switches, modems, routers, etc. Although a single computer system  100  is shown in  FIG. 1 , system  100  may also be implemented as two or more computer systems operating together. 
         [0018]    Processor subsystem  150  is representative of one or more processors capable of executing similarity detection code  130 . Various specific types of processors may be employed, such as, for example, an x86 processor, a Power PC processor, an IBM Cell processor, or an ARM processor. 
         [0019]    Storage subsystem  110  is representative of various types of storage media, also referred to as “computer readable storage media.” Storage subsystem  110  may be implemented using any suitable media type and/or storage architecture. For example, storage subsystem  110  may be implemented using storage media such as hard disk storage, floppy disk storage, removable disk storage, flash memory, semiconductor memory such as random access memory or read only memory, etc. It is noted that storage subsystem  110  may be implemented at a single location or may be distributed (e.g., in a SAN configuration). 
         [0020]    Email database  120  contains a plurality of email messages, each referred to herein as an email document, associated with one or more email system users. It is noted that various email documents within email database  120  may be duplicates of one another or may contain substantially similar content to that of other emails in the database (e.g., an initial email and a corresponding response email containing the initial email). 
         [0021]    As will be described in further detail below, similarity detection code  130  includes instructions executable by processor subsystem  150  to identify email documents in database  120  that may be similar to one another (i.e., contain similar content). In various embodiments, email documents identified by similarity detection code  130  as being potentially similar may be reported to a user. In some embodiments, emails identified as being potentially similar may be further evaluated. For example, upon identification, potentially similar email documents may be analyzed or compared by additional code to determine and/or verify the extent of their similarity. Execution of similarity detection code  130  may allow efficient filtering of dissimilar email documents within email database  120 . 
         [0022]      FIG. 2  is a flow diagram illustrating operations that may be carried out in accordance with execution of one embodiment of similarity detection code  130 . Operations illustrated in  FIG. 2  will be discussed in conjunction with an exemplary situation illustrated by  FIG. 3 , which shows two possible email documents  301 A and  301 B. As shown, email document  301 B is a response to email document  301 A. In this example, it is noted that the email documents  301 A and  301 B contain different email headers (e.g., the From, To, and Subject portions). It is also noted that an ending portion of email document  301 B contains the sequence “The dog was sleeping.”, which is not included in email document  301 A. 
         [0023]    In step  210 , extraneous email content in an email document being processed is removed or disregarded. This extraneous content may include common, reoccurring phrases found in typical email documents such as, “From [Name], To [Name], Subject [TITLE], On [DATE], at [TIME], [NAME] wrote:”, “Begin forwarded message:”, “- - - Original Message - - - ”, etc. An example of a result from this step is depicted in  FIG. 4 , where the headers have been removed from email documents  301 A and  301 B. In various embodiments, the extraneous email content removed/disregarded from each email document during step  210  may be predetermined or pre-selected words or phrases (e.g., phrases generally common to email documents). In other embodiments, the extraneous email content that is removed/disregarded may be controlled or specified by input from a user. It is noted that in some embodiments step  210  may be omitted. 
         [0024]    In step  220 , the remaining content within the email documents being processed are converted to token values. A token value, as described in this disclosure, is a numerical value representative of or generated from a sequence of selected characters (e.g., a word, a sentence, a paragraph, or portion of a word). For example,  FIG. 5A  illustrates an example of generating token values according to one embodiment. In this example, the character sequences “John” and “Jane” are converted to the token values “47” and “25” by summing the alphabetic positions of characters in the words. For example, the character “J” is the 10 th  letter in the alphabet and the character “o” is the 15 th  letter. Thus, a token value of “47” is generated based on the sum of the alphabetic positions of the characters in the word “John”. Token values for other words (e.g., “Jane”) are created in a similar manner.  FIG. 5B  illustrates exemplary token values that may be generated for each of the words found in email documents  301 A and  301 B. 
         [0025]    It is noted that token values may be generated in a variety of other ways during step  220 . For example, in one alternative embodiment, ASCII character ordinal values, which associate numerical values with alphabetic characters or symbols, may be summed to create a token value for each word (in a similar manner as the embodiment described above). In other embodiments, other predetermined functions (e.g., hash functions), as desired, may be applied to values corresponding characters of a character sequence. It is noted that in some embodiments, the sequential ordering of characters in a character sequence may affect the value of a generated token value. For example, in such embodiments the word “top” may result in the generation of a token value that is different from that generated from the word “pot.” 
         [0026]    In step  230 , generated token values are selected as truncation points from a beginning portion of the email document being processed. In one embodiment, token values are selected based on the minimum token value in a sliding window that moves across a beginning portion of the email document (e.g., the sliding window is incrementally moved upon successive iterations such that the token values of different subsets of words (or other sequences of characters) are selected upon each iteration for evaluation).  FIG. 6  illustrates, such an example, where sliding windows  601 A and  601 B incrementally slide across beginning portions of email documents  301 A and  301 B. In this example, the token value  25  (Jane) is selected as a minimum value within sliding window  601 A at the top positioning from the possible token values  47  (John),  25  (Jane), and  45  (Fox). As sliding window  601 A moves downward, the token value  33  (the) is selected as another minimum value. Additional token values are selected in a similar manner as the sliding window proceeds through the beginning portion. 
         [0027]    In step  240 , generated token values are selected as truncation points from an ending portion of the email document being processed. In one embodiment, this operation is performed in the same manner as step  230 ; however, it is performed in an ending portion as opposed to a beginning portion. For example in one embodiment, shown in  FIG. 6 , sliding windows  602 A and  602 B are used to select tokens from an ending portion of email documents  301 A and  301 B, respectively. 
         [0028]    It is also noted that size of the beginning and ending portions, upon which a sliding window is applied, may vary. In one embodiment, token values may be selected from only a small initial portion such as the email header. In some other embodiments, the size of the beginning and ending portions may be defined by some predetermined value or provided by a user input. Additionally, the size of a sliding window may vary from the embodiment depicted in  FIG. 6 . 
         [0029]    In step  250 , one or more hash values are generated from character sequences that are contained in the email document between the selected beginning and ending token values (i.e., a token value selected from a beginning portion forms a truncation point at the beginning of the email document and a token value selected from an ending portion forms a truncation point at the ending of the email document such that a hash is generated from the contents contained between the beginning and ending truncation points).  FIG. 7  illustrates exemplary character sequences from possible combinations of beginning and ending tokens (i.e., beginning and ending truncation points). For example, truncated character sequence  701 A found in email  301 A is created using beginning token  25  (Jane) and ending token  26  (dog). Similarly, truncated character sequence  701 B is created using beginning token  33  (The) and ending token  26  (dog). This example is further illustrated in  FIG. 8  where truncated character sequences  701  A, B, C, and D are underlined within email documents  301 A and  301 B. As depicted, truncated character sequences  701 B and  701 D contain the same words, while truncated character sequences  710 A and  710 C do not. It is noted that other truncated character sequences (not depicted in  FIG. 7  and  FIG. 8 ) can be created using the various beginning and ending tokens. It is also noted that in some embodiments, hash values may be generated including the words (or character sequences) that created the truncation points, while others may not. 
         [0030]    One embodiment for generating hash values in step  250  is depicted in  FIG. 9 . In this example, the token values (generated in step  220 ) of words that are contained between beginning and ending truncations points (generated in step  230  and  240 ) are summed to create hash values. Generally speaking, a “hash function” is any function that has a mapping of an input to a number (i.e., hash value). Thus, in various embodiments, specific hashing algorithms such as an MD5 hash, a SHA-1 hash, etc may be used. Accordingly, in some embodiments, the hash values generated in step  250  may be based upon a function that is independent of the token values generated in step  220 . 
         [0031]    In step  260 , the hash values generated in step  250  are compared for equivalency. As shown in  FIG. 9 , truncated character sequences  701 B and  701 D have the same content, and thus an equivalent hash value (e.g., “464” in this example) is generated for each. On the other hand, truncated character sequences  701 A and  710 C do not contain the same content, and thus different hash values (e.g., “534” and “640”, respectively) are generated. Based on this hash value comparison, a similarity indication is generated. 
         [0032]    It is noted that similarity detection code  130  may generate the similarity indication in a variety of ways. In some embodiments, the similarity indication may indicate that the email documents being analyzed are similar (or possibly similar) if any hash values resulting from the truncated character sequences in the different documents match. In yet other embodiments, the similarity indication may indicate that a similarity exists only if all hash values generated for the truncated character sequences in the different documents match. In various embodiments, similarity detection code  130  may be programmable by a user who can specify by input a minimum number of hash values that must match to cause an indication of similarity to be output. It is noted that the similarity indication may alternatively indicate that a dissimilarity exists between documents based on the result of the comparison performed in step  260 . 
         [0033]    Although in the embodiment described above token values and beginning/ending truncation points are generated on a word-by-word basis, other embodiments are also possible. For example, token values and truncation points may be created for other predetermined sequences of characters, such as each sentence, paragraph, or any other grouping of characters. It is noted that the term “character” as used herein is not limited to a letter; it may include numbers, symbols, punctuation, etc. Thus, in some embodiments, token values may be generated for character sequences that include punctuation or other symbols. 
         [0034]    It is noted that in other embodiments beginning/ending truncations points may be generated in steps  230  and  240  using different techniques. For example, in one embodiment, rather than selecting a truncation point based on a minimum token value, other sliding window based functions may be applied. In yet other embodiments, other methodical functions may be applied to the token values to yield truncation points based on resultant values (e.g. generating truncation points based on odd numbered token values in beginning and ending portions of an email document). 
         [0035]    Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even where only a single embodiment is described with respect to a particular feature. Examples of features provided in the disclosure are intended to be illustrative rather than restrictive unless stated otherwise. The above description is intended to cover such alternatives, modifications, and equivalents as would be apparent to a person skilled in the art having the benefit of this disclosure. 
         [0036]    The scope of the present disclosure includes any feature or combination of features disclosed herein (either explicitly or implicitly), or any generalization thereof, whether or not it mitigates any or all of the problems addressed by various described embodiments. Accordingly, new claims may be formulated during prosecution of this application (or an application claiming priority thereto) to any such combination of features. In particular, with reference to the appended claims, features from dependent claims may be combined with those of the independent claims and features from respective independent claims may be combined in any appropriate manner and not merely in the specific combinations enumerated in the appended claims.