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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
Claim(s) 1-3, 5-7, 10, 11, 14-16 are/is rejected under 35 U.S.C. 103 as being unpatentable over University of Munich, 6/1/1999, "OPTICS: ordering points to identify the clustering structure, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dl.acm.org/doi/pdf/10.1145/304181.304187 in view of Oberhofer et al. US2018/0113928
Regarding claim 1, University of Munich teaches: wherein each record
- is initially marked as unprocessed, (University of Munich see page 52 at the beginning each object from a set is not yet processed)
- comprises a first attribute and a second attribute, and (University of Munich see page 52 core-distance and reachability distance)
- forms part of a neighborhood within a predefined threshold distance, (University of Munich see page 50-52 placing every object in a cluster based on a radius distance or reachable or core distance in a neighborhood)
wherein a record is a core record if its neighborhood comprises at least a predefined number of records, and the record is a non-core record if its neighborhood comprises less than the predefined number of records (University of Munich see page 51 each object in a cluster is within a radius with a minimum number of objects within the neighborhood where a core object is defined using the minimum number of objects)
a) selecting, an unprocessed record of the collection of records and computing a core distance for the selected unprocessed record, wherein the core distance is the smallest distance from the selected unprocessed record at which its neighborhood still comprises at least the predefined number of records, (University of Munich see page 52 each object of a set of object not yet processed to be clustered based on a radius with a minimum number of objects within the neighborhood where a core object is defined using a smallest distance radius to a core distance and using a minimum number of objects)
b) if the selected unprocessed record is a core record, assigning, the core distance to the first attribute and labelling the selected unprocessed record as core-record, otherwise, assigning a predefined value to the first attribute and labelling the selected unprocessed record as non-core record, (University of Munich see page 51 52 each object of a set of object not yet processed to be clustered based on a radius with a minimum number of objects within the neighborhood where a core object is defined using a smallest distance radius to a core distance and using a minimum number of objects. Objects that do not satisfy these conditions are border objects. Examiner notes the optional recitation in the claim language)
c) adding, the selected unprocessed record to the index and marking the selected unprocessed record as processed, (University of Munich see page 53 object is written to ordered file)
d) if the selected processed record is a core record, processing, each record of the neighborhood of the selected processed record; by
computing, a reachability distance for each record, wherein the reachability distance is the smallest distance at which a respective record is still directly density-reachable from the selected processed record, (University of Munich see pages 51-53 core object has a neighborhood where reachability distance with respect to other objects is the smallest distance with clusters defined by global density)
populating, a priority queue with each record which is marked as unprocessed in an ascending order of its reachability distance, wherein the reachability distance is assigned to the second attribute, (University of Munich see page 53 objects not in priority queue inserted and ordered based on reachability distance then written to ordered file)
moving each record which was previously labelled as non-core record and outputs a smaller reachability distance than previously assigned to its second attribute from the index, and wherein each removed record is inserted into the priority queue according to the ascending order of its reachability distance, and
e) appending, each record of the priority queue to the index, wherein each record is marked, as processed, (University of Munich see page 52 53 storing objects in order to be processed and objects not in priority queue inserted and ordered based on reachability distance and object already in queue are moved to the top of the queue if reachability distance is smaller than previous reachability distance then written to ordered file)
wherein if the appended record is a core record, then the records of its neighborhood are processed according to step d), wherein the steps a) to e) are repeated until all records of the collection of records are processed and inserted into the index. (University of Munich see page 53 if object is a core object iteratively collect and process all objects within radius distance within core object and write into ordered file. Examiner notes optional recitation in claim language)
University of Munich does not distinctly disclose: wherein the index is computed according to the following steps
by one or more computer processors
removing, by one or more computer processors, wherein each removed record is marked as unprocessed
However, Oberhofer teaches: wherein the index is computed according to the following steps (Oberhofer see paragraph 0022 store records with cluster identifier in index)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
removing, by one or more computer processors, wherein each removed record is marked as unprocessed (Oberhofer see paragraph 0013 0015 0022 0095 0098 processor, record linkage used process of matching records or processed cluster of records to store in index, record may be removed from index of given cluster which reads on record marked as unprocessed)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 2, University of Munich teaches: determining, whether the selected unprocessed record is a core record between step a) and step b) by counting the records in the neighborhood of the selected unprocessed record. (University of Munich see page 51 52 each object of a set of object not yet processed to be clustered based on a radius with a minimum number of objects within the neighborhood where a core object is defined using a smallest distance radius to a core distance and using a minimum number of objects. Objects that do not satisfy these conditions are border objects)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 3, University of Munich teaches: wherein during processing of each record of the priority queue in step d), only if the first attribute is unassigned, the core distance is computed and assigned, to the first attribute. (University of Munich see page 52 53 storing objects in order to be processed and objects not in priority queue inserted and ordered based on reachability distance and object already in queue are moved to the top of the queue if reachability distance is smaller than previous reachability distance then written to ordered file)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 5, University of Munich teaches: extracting, of a first exact clustering of the collection of records for the predefined threshold distance based on a combined evaluation of the reachability distance and the core distance of the records in the index according to a linear scan (University of Munich see page 52 retrieving objects within certain radius neighborhood of core distance by DBSCAN forming a cluster of objects)
by one or more computer processors, in the index, through the index, wherein each record is assigned either a cluster identifier or a noise identifier. (Oberhofer see paragraph 0022 0095 processor, store records with cluster identifier in index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 6, University of Munich teaches: wherein the first exact clustering of the collection of records is extracted, for a selected threshold distance, wherein the selected threshold distance is less than or equal to the predefined threshold distance, (University of Munich see page 52 53 retrieving objects within certain radius neighborhood of core distance by DBSCAN exacting a cluster based on distance)
wherein the method further comprises a candidate verification step, wherein for an extracted cluster of the first exact clustering, each record
-that is located in the index closely ahead of the extracted cluster, (University of Munich see page 53 current object checked for distance based on cluster ordering)
- of which a computed core distance is assigned the first attribute, and (University of Munich see page 52 core-distance)
- to which the noise identifier is assigned, (University of Munich see page 51 52 objects not contained in any cluster by definition is a noise object)
is verified, against the records of the extracted cluster labelled as core records and to which an assigned computed core distance is less than or equal to the selected threshold distance. (University of Munich see page 53 extract cluster and check whether current object is within cluster by determining reachable distance being smaller than radius)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 7, University of Munich teaches: wherein the candidate verification step is executed, for each extracted cluster of the first exact clustering after the extraction of each extracted cluster, respectively. (University of Munich see page 53 extract cluster and check whether current object is within cluster by determining reachable distance being smaller than radius)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 10, University of Munich teaches: wherein the collection of records is separated, into at least one subset, wherein each subset of the at least one subset corresponds to an extracted cluster of the first exact clustering with respect to the predefined threshold distance, wherein the second exact clustering is computed for each subset separately. (University of Munich see page 49 51-53 clustering algorithm to group clusters into subclasses extract all density-based clustering based on radius distance and minimum number of points with a plurality of clusters)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 11, University of Munich teaches: wherein in the step d) the cluster identifier is increased and each record of the neighborhood of the selected processed record is inserted into a border record collection of the respective cluster identifier if the respective record is labelled as a non-core record, wherein each border record collection is merged, with the subset corresponding to the respective cluster identifier prior to computing the second exact clustering for each subset. (University of Munich see page 51-53 clustering based on radius distance and determining core objects and border objects such that border objects are added to a cluster based on being within reachable distance from core object)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 14, University of Munich teaches: wherein the second exact clustering is computed, using a density-based spatial clustering with noise - DBSCAN - algorithm. (University of Munich see page 50 51 density cluster with areas of noise, DBSCAN algorithm)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Regarding claim 15, University of Munich teaches: wherein each record of the collection of records represents a process instance of a process, wherein the process was executed in a source computer system or with aid of the source computer system. (University of Munich see page 49 52 processing objects in information systems)
Regarding claim 16, University of Munich teaches: wherein the second exact clustering is computed, using a density-based spatial clustering with noise - DBSCAN - algorithm. (University of Munich see page 50 51 density cluster with areas of noise, DBSCAN algorithm)
by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Claim(s) 4 are/is rejected under 35 U.S.C. 103 as being unpatentable over University of Munich, 6/1/1999, "OPTICS: ordering points to identify the clustering structure, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dl.acm.org/doi/pdf/10.1145/304181.304187 in view of Oberhofer et al. US2018/0113928 in view of Gulati et al. US2020/0304236
Regarding claim 4, University of Munich does not teach: according to any of the preceding claims, wherein each record of the index comprises a third attribute, in which a permutation order of the record is stored
Gulati teaches: according to any of the preceding claims, wherein each record of the index comprises a third attribute, in which a permutation order of the record is stored (Gulati see paragraphs 0096 0108 permutation order indicated as an index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include permutations as taught by Gulati for the predictable result of more efficiently organizing and managing data.
Claim(s) 8, 9 are/is rejected under 35 U.S.C. 103 as being unpatentable over University of Munich, 6/1/1999, "OPTICS: ordering points to identify the clustering structure, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dl.acm.org/doi/pdf/10.1145/304181.304187 in view of Oberhofer et al. US2018/0113928 in view of Maurya et al. US11816612
Regarding claim 8, University of Munich teaches: wherein a second exact clustering of the collection of records is computed, based on the predefined threshold distance and a selected number of records, according to the first exact clustering (University of Munich see page 51-53 extract all density-based clustering based on radius distance and minimum number of points)
by one or more computer processors to which the cluster identifier is assigned (Oberhofer see paragraph 0022 0095, processor, store records with cluster identifier in index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
University of Munich does not teach: wherein the selected number of records is larger than the predefined number of records, wherein the collection of records is reduced to the records
Maurya teaches: wherein the selected number of records is larger than the predefined number of records, wherein the collection of records is reduced to the records (Maurya see col. 12 lines 40-67 reduce number of records when number of records exceeds predetermined threshold)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include reducing records as taught by Maurya for the predictable result of more efficiently organizing and managing data.
Regarding claim 9, University of Munich teaches: wherein the second exact clustering is computed, based on the selected threshold distance, according to the first exact clustering based on the predefined threshold distance. (University of Munich see page 51-53 extract all density-based clustering based on radius distance and minimum number of points)
by one or more computer processors to which the cluster identifier is assigned (Oberhofer see paragraph 0022 0095, processor, store records with cluster identifier in index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
Maurya teaches: wherein the collection of records is reduced to the records (Maurya see col. 12 lines 40-67 reduce number of records when number of records exceeds predetermined threshold)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include reducing records as taught by Maurya for the predictable result of more efficiently organizing and managing data.
Claim(s) 12 and 13 are/is rejected under 35 U.S.C. 103 as being unpatentable over University of Munich, 6/1/1999, "OPTICS: ordering points to identify the clustering structure, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dl.acm.org/doi/pdf/10.1145/304181.304187 in view of Oberhofer et al. US2018/0113928 in view of Appalaraju et al. US11893012
Regarding claim 12, University of Munich does not teach: wherein each record comprises a fourth attribute, wherein for each record, the number of records of its neighborhood is assigned to the fourth attribute during the step b) and the step d)
However, Appalraju teaches: wherein each record comprises a fourth attribute, wherein for each record, the number of records of its neighborhood is assigned to the fourth attribute during the step b) and the step d). (Appalraju col. 20 lines 27-54 parameters such as number of nearest neighbors)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include parameters of number of neighbors as taught by Appalraju for the predictable result of more efficiently organizing and managing data.
Regarding claim 13, University of Munich as modified teaches: by one or more computer processors (Oberhofer see paragraph 0095 processor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include indexing as taught by Oberhofer for the predictable result of more efficiently organizing and managing data.
wherein only for each core record, the number of records comprised in its neighborhood is assigned, to the fourth attribute during the step b) and the step d). (Appalraju col. 4 lines 11-45 col. 20 lines 27-54 CPU, parameters such as number of nearest neighbors)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of clustering as taught by University of Munich to include parameters of number of neighbors as taught by Appalraju for the predictable result of more efficiently organizing and managing data.
Response to arguments
Applicant’s argument: University of Munich’s teachings of core-distance and reachability-distance are not stored in first and second attribute of each record
Examiner’s response: Applicant’s argument is considered but is not persuasive. University of Munich teaches core-distance and reachability distance as it relates to objects. The claim does not specify where the attributes are stored just that records have these attributes which lines up with what is taught by the reference. If applicant intends for a different interpretation of what an attribute is examiner suggests amending the claim the clarify what is meant by the term “attribute”.
Applicant’s argument: University of Munich’s deal with all non-processed objects whereas claims are selecting processed records as the prior art does not distinguish between processed and non-processed objects only core and non core objects and therefore does not teach the removing step
Examiner’s response: Applicant’s argument is considered but is not persuasive. Oberhofer reference teaches record linkage used process of matching records or processed cluster of records to store in index, record may be removed from index of given cluster which reads on record marked as unprocessed. While University of Munich does deal with unprocessed records, the marking of removed records as unprocessed is taught by Oberhofer. Additionally and more importantly, this step is in bullet point d which is an optional recitation and therefore if earlier in the claim examiner interprets that selected record is not a core record this limitation does not need to be addressed.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN S LIN whose telephone number is (571)270-0612. The examiner can normally be reached on M-F 9-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN S LIN/Primary Examiner, Art Unit 2153