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 Rejections - 35 USC §103
2. 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al (Pub. No. US 2020/0206920 A1; hereinafter Ma) in view of Kim et al (Pub. No. US 2018/0113780 A1; hereinafter Kim)
Consider claims 1, and 12, Ma clearly show and disclose a system of reducing noise for event sequence visualization (decreasing the coverage feature may helpfully reduce or eliminate noise, e.g. unrelated events/segments) (paragraphs: 0190, 0250, 0292), a method of reducing noise for event sequence visualization, the method comprising: identifying, by a computing system, patterns of events from an event sequence dataset, wherein the event sequence dataset includes data for a plurality of event sequences, and wherein each event sequence of the plurality of events sequences includes at least one event (“Event streams” may be conceptualized as a series of events, the term “segment” is referring to a particular sequence of events within an event stream, “Event streams” are stored, preferably in one or more tables in a central or distributed database, and may be recalled at any time; record a plurality of event streams, each event stream corresponding to a human user interacting with a computing device to perform one or more tasks; concatenate the event streams; segment some or all of the concatenated event streams to generate one or more individual traces performed by the user interacting with the computing device, each trace corresponding to a particular task; cluster the traces according to a task type) (paragraphs: 0022, 0024, 0067, 0108, and 0141); encoding, by the computing system, each event sequence of the plurality of event sequences into a respective vector embedding based on the identified patterns of events to generate a plurality of vectors (he feature vectors are calculated using a known autoencoders and yield dense feature vectors) (paragraphs: 0067, 0162); executing, by the computing system, a clustering algorithm on the plurality of vectors to generate a plurality of clusters (application traces may be evaluated and corresponding feature vectors generated using any suitable technique described herein (e.g. autoencoders), and the feature vectors are clustered) (paragraphs:0186 and 0217, 0219); assigning, by the computing system, each event sequence of the plurality of event sequences to a respective cluster of the plurality of clusters (clustering one or more segments according to application type; and concatenating some or all of the segments clustered according to application type, and wherein each segment comprises a sequence of one or more events performed within a same application) (paragraphs: 0219); generating, by the computing system, a reduced dataset based on the assignment of the plurality of event sequences to the plurality of clusters ( wherein the segments clustered according to element comprise one or more events performed with respect to a particular element of a user interface implemented via the computing device) (paragraphs: 0219); and building, by the computing system, a data structure for event sequence visualization based on the reduced dataset ( clustering the plurality of application traces according to the sequence of the one or more events performed within each respective application trace; and labeling each of the plurality of application traces to form a plurality of sequences of labels, wherein the labeling is performed according to the cluster to which the respective application trace is assigned, Conversely, decreasing the coverage feature may helpfully reduce or eliminate noise, e.g. unrelated events/segments, repetitive events/segments, erroneous events/segments, etc. a) (paragraphs: 0110, 0219, 0250; and fig. 3); however, Ma does not disclose another example for assigning, by the computing system, each event sequence of the plurality of event sequences to a respective cluster of the plurality of clusters.
In the same field of endeavor, Kim clearly specifically discloses another example for assigning, by the computing system, each event sequence of the plurality of event sequences to a respective cluster of the plurality of clusters (abstract, paragraphs: 0103, 0106, 0107 and fig. 3)
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to incorporate the teaching of Kim into teaching of Ma for the purpose of providing more example for assigning event sequence to a respective cluster.
Consider claims 2, and 13, Ma and Kim clearly show the system, and the method, wherein building the data structure for event sequence visualization based on the reduced dataset comprises building a Trie data structure for event sequence visualization based on the reduced dataset (Ma: paragraphs: 0149 and 0267).
Consider claims 3, and 14, Ma and Kim clearly show the system, and the method, wherein identifying the patterns of events from the event sequence dataset comprises identifying patterns of events that occur at least a threshold number of times in the event sequence dataset (Ma: paragraphs: 0141, 0143, and 0181-0182).
Consider claim 4, Ma and Kim clearly show the system, and the method, wherein identifying the patterns of events from the event sequence dataset comprises identifying patterns of events that occur at least twice in the event sequence dataset (Ma: paragraphs: 0141 - 0143, and 0181-0182).
Consider claims 5, and 15, Ma and Kim clearly show the system, and the method, wherein encoding each event sequence of the plurality of event sequences into the respective vector embedding comprises encoding each event sequence of the plurality of event sequences into a respective vector embedding using one-hot encoding (Ma: paragraphs: paragraphs: 0067, 0162).
Consider claims 6, and 16, Ma and Kim clearly show the system, and the method, wherein executing the clustering algorithm on the plurality of vectors to generate the plurality of clusters comprises executing a k-means clustering algorithm on the plurality of vectors to generate the plurality of clusters (Ma: paragraphs: 0219).
Consider claims 7 and 17, Ma and Kim clearly show the system, and the method, wherein assigning each event sequence of the plurality of event sequences to the respective cluster of the plurality of clusters comprises assigning each event sequence of the plurality of event sequences to one and only one respective cluster of the plurality of clusters (Ma: paragraphs: 0110, 0219, 0250).
Consider claims 8, and 18, Ma and Kim clearly show the system, and the method, wherein assigning each event sequence of the plurality of event sequences to the respective cluster of the plurality of clusters comprises assigning each event sequence of the plurality of event sequences to a medoid or centroid determined by the clustering algorithm (Ma: paragraphs: 0110, 0219, 0250).
Consider claims 9 and 19, Ma and Kim clearly show the system, and the method, wherein each event sequence of the plurality of event sequences comprises a contact center bot flow of an organization (Ma: paragraphs: 0041 and 0065).
Consider claims 10 and 20, Ma and Kim clearly show the system, and the method, wherein the event sequence dataset comprises data associated with events of an interactive voice response (IVR) system of a contact center system (Ma: paragraphs: 0255-0256).
Consider claim 11, Ma and Kim clearly show the method, further comprising displaying, by the computing system, a graphical representation of the data structure for event sequence visualization in response to building the data structure (Ma: paragraphs: 0022; Kim: paragraphs: 0086).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amal Zenati whose telephone number is 571- 270- 1947. The examiner can normally be reached on 8:00 -5:00 M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571- 272- 7488. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/AMAL S ZENATI/Primary Examiner, Art Unit 2693
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