DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-3, 10-16 are pending.
Response to Arguments
101 Rejection: Applicant’s arguments, see Remarks page 1, filed 09/30/2025, with respect to 1-3, 10-16 have been fully considered and are persuasive. Claim 1 (and similar claim 14) recites “executing the data labeling software program to label all of the data packets such that, for each data packet, one of the first plurality of labels is associated therewith if the data packet includes one or more keywords to which one rule of the first set of rules applies” which integrates the judicial exception into a technological improvement disclosed in the specification (Para 0001, it would be beneficial to have each transaction labeled or categorized with one or more descriptive words in order to easily identify which data is of interest to a particular analysis). Accordingly, the 101 rejection of claims 1-3, 10-16 has been withdrawn.
103 Rejection: Applicant’s arguments with respect to claims 1-3, 10-16 have been fully considered and are persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of
rejection is made in view of newly cited reference Husain.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 10-16 are rejected under 35 U.S.C. 103 as being unpatentable over Husain et al (US 20170308583 A1) hereafter Husain in view of Hunter et al (US 20170032463 A1) hereafter Hunter further in view of Schuetze et al (US 20030074369 A1) hereafter Schuetze
Regarding claim 1, Husain teaches a method of creating a data labeling software program, the method comprising: receiving a plurality of data packets, each data packet including a plurality of words that describe an event; identifying a first set of keywords which are words that are common or repeated in the first portion of data packets, including at least one of performing n-gram analysis of the text of each data packet of the first portion, performing regular expression analysis of the text of each data packet of the first portion, and performing fuzzy matching of the text of each data packet of the first portion (Para 0066, the social-networking system 160 may identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user); identifying a second set of keywords which are words that are common or repeated in a second portion of the data packets that did not include any keywords of the first set to which one rule of the first set of rules applies, including at least one of performing n-gram analysis of the text of each data packet of the second portion, performing regular expression analysis of the text of each data packet of the second portion, and performing fuzzy matching of the text of each data packet of the second portion (Para 0057, The social-networking system 160 may identify an additional set of candidate keyword phrases matching one or more n-grams of the inputted text query. Each of this additional set of candidate keyword phrases comprises one or more n-grams extracted from content associated with a native content object interacted with by the querying user).
Husain does not appear to explicitly teach creating a first set of rules for the data labeling software program, each rule of the first set associating one of a first plurality of labels with one or more of the first portion of the data packets according to one or more keywords in each data packet; executing the data labeling software program to label all of the data packets such that, for each data packet, one of the first plurality of labels is associated therewith if the data packet includes one or more keywords to which one rule of the first set of rules applies; and creating a second set of rules for the data labeling software program, each rule of the second set associating one of a second plurality of labels with at least one of the data packets according to one or more keywords of the second set in each data packet.
In analogous art, Hunter teaches creating a first set of rules for the data labeling software program, each rule of the first set associating one of a first plurality of labels with one or more of the first portion of the data packets according to one or more keywords in each data packet (Para 0351, filtered clusters are grouped according to the indicated tag type. Clusters having a same value of the tag type are grouped together) ("indicated tag type" teaches "a first set of rules"); executing the data labeling software program to label all of the data packets such that, for each data packet, one of the first plurality of labels is associated therewith if the data packet includes one or more keywords to which one rule of the first set of rules applies (Para 0351, if the clusters are grouped by "trader", two clusters both tagged with "trader: trader 1" will be grouped together)("trader" teaches "one of the first plurality of labels"); and creating a second set of rules for the data labeling software program, each rule of the second set associating one of a second plurality of labels with at least one of the data packets according to one or more keywords of the second set in each data packet (Para 0351, if the clusters are grouped by "cluster strategy", two clusters both tagged with "cluster strategy: out-of-hours trades" will be grouped together). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Husain in view of Hunter does not appear to explicitly teach randomly selecting a first portion of the data packets.
In analogous art, Schuetze teaches randomly selecting a first portion of the data packets (Para 0135, the classical form of k-means clustering selects initial clusters by way of random selection from the objects that are to be clustered). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain in view of Hunter to include the teaching of Schuetze. One of ordinary skill in the art would be motivated to implement this modification in order to perform data clustering, as taught Schuetze (Para 0133, As is well known in the art, k-means clustering is a partitioning method that usually begins with k randomly selected objects as cluster centers).
Regarding claim 2, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, wherein identifying the first set of keywords includes sorting the first portion of the data packets into groups such that each group of data packets includes the same or similar keywords (Hunter, Para 0351, filtered clusters are grouped according to the indicated tag type. Clusters having a same value of the tag type are grouped together). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 3, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, wherein identifying the second set of keywords includes sorting the second portion of the data packets into groups such that each group of data packets includes the same or similar keywords (Hunter, Para 0351, filtered clusters are grouped according to the indicated tag type. Clusters having a same value of the tag type are grouped together). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 10, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, wherein creating the first set of rules and creating the second set of rules each includes specifying a priority for each rule such that rules with a higher priority are applied before rules with a lower priority (Hunter, Para 0351, the system may employ a fuzzy matching algorithm to determine tag values that are sufficiently close to each other that the respective associated clusters may be grouped together). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 11, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, wherein creating the first set of rules and creating the second set of rules each includes specifying additional conditions for each rule for the data packet to meet before the associated rule is applied (Hunter, Para 0324, The analyst may dynamically view clusters grouped according to different tags and/or tag types). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 12, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, wherein creating the first set of rules and creating the second set of rules each includes specifying variations of spellings of one or more keywords for at least a portion of each set of rules (Hunter, Para 0351, respective associated clusters may be grouped together to account for, for example, typos and/or other errors in the tags). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 13, Husain in view of Hunter further in view of Schuetze teaches the method of claim 1, further comprising integrating the first set of rules and the second set of rules into the data labeling software program (Hunter, Para 0351, Clusters having a same value of the tag type are grouped together). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain to include the teaching of Hunter. One of ordinary skill in the art would be motivated to implement this modification in order to efficiently analyze data, as taught by Hunter (Abs, Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst).
Regarding claim 14, Husain teaches a method of creating a data labeling software program, the method comprising: receiving a plurality of data packets, each data packet including a plurality of words that describe an event; identifying a first set of keywords which are words that are common or repeated in the first portion of data packets, including at least one of performing n- gram analysis of the text of each data packet of the first portion, performing regular expression analysis of the text of each data packet of the first portion, and performing fuzzy matching of the text of each data packet of the first portion(Para 0066, the social-networking system 160 may identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user); identifying a second set of keywords which are words that are common or repeated in a second portion of the data packets that did not include any keywords of the first set to which one rule of the first set of rules applies, including at least one of performing n-gram analysis of the text of each data packet of the second portion, performing regular expression analysis of the text of each data packet of the second portion, and performing fuzzy matching of the text of each data packet of the second portion (Para 0057, The social-networking system 160 may identify an additional set of candidate keyword phrases matching one or more n-grams of the inputted text query. Each of this additional set of candidate keyword phrases comprises one or more n-grams extracted from content associated with a native content object interacted with by the querying user).
Husain does not appear to explicitly teach creating a first set of rules for the data labeling software program, each rule of the first set associating one of a first plurality of labels with one or more of the first portion of the data packets according to one or more keywords in each data packet; executing the data labeling software program to label all of the data packets such that, for each data packet, one of the first plurality of labels is associated therewith if the data packet includes one or more keywords to which one rule of the first set of rules applies; creating a second set of rules for the data labeling software program, each rule of the second set associating one of a second plurality of labels with at least one of the data packets according to one or more keywords of the second set in each data packet.
In analogous art, Hunter teaches creating a first set of rules for the data labeling software program, each rule of the first set associating one of a first plurality of labels with one or more of the first portion of the data packets according to one or more keywords in each data packet (Para 0351, filtered clusters are grouped according to the indicated tag type. Clusters having a same value of the tag type are grouped together) ("indicated tag type" teaches "a first set of rules"); executing the data labeling software program to label all of the data packets such that, for each data packet, one of the first plurality of labels is associated therewith if the data packet includes one or more keywords to which one rule of the first set of rules applies (Para 0351, if the clusters are grouped by "trader", two clusters both tagged with "trader: trader 1" will be grouped together) ("trader" teaches "one of the first plurality of labels"); creating a second set of rules for the data labeling software program, each rule of the second set associating one of a second plurality of labels with at least one of the data packets according to one or more keywords of the second set in each data packet (Para 0351, if the clusters are grouped by "cluster strategy", two clusters both tagged with "cluster strategy: out-of-hours trades" will be grouped together).
Husain in view of Hunter does not appear to explicitly teach randomly selecting a first portion of the data packets.
In analogous art, Schuetze teaches randomly selecting a first portion of the data packets (Para 0135, the classical form of k-means clustering selects initial clusters by way of random selection from the objects that are to be clustered). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Husain in view of Hunter to include the teaching of Schuetze. One of ordinary skill in the art would be motivated to implement this modification in order to perform data clustering, as taught Schuetze (Para 0133, As is well known in the art, k-means clustering is a partitioning method that usually begins with k randomly selected objects as cluster centers).
Regarding claim 15, Husain in view of Hunter further in view of Schuetze teaches the method of claim 14, wherein identifying the first set of keywords includes sorting the first portion of the data packets into groups such that each group of data packets includes the same or similar keywords (Husain, Para 0066, At step 620, the social-networking system 160 may identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user).
Regarding claim 16, Husain in view of Hunter further in view of Schuetze teaches the method of claim 14, wherein identifying the second set of keywords includes sorting the second portion of the data packets into groups such that each group of data packets includes the same or similar keywords (Husain, Para 0066, At step 620, the social-networking system 160 may identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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