DETAILED ACTION
Claims 1-24 are pending in the Instant Application.
Claims 1-3, 8, 10, 11, 13-15, 19, 21, 22 and 24 are rejected (Non-Final Rejection).
Claims 4-7, 9, 12, 16-18, 20 and 23 are objected to.
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 .
Priority
The instant Application, filed 27 December 2022, claims priority from provisional application 63/294,230, filed 28 December 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 27 December 2022 was considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 recites the limitation "the random number" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 8, 10, 11, 13-15, 19, 21, 22 and 24 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by ERTL (“Ertl”), United States Patent Application Publication No. 2018/0300363.
As per claim 1, Ertl discloses a computer-implemented method for determining a performance metric in a distributed computing environment, comprising:
setting values of configuration parameters for a first sketching data structure, where the first sketching data structure is partitioned into a plurality of registers ([Claim 4] of the reference) and the configuration parameters includes a recording parameter, b, that controls recording of data into the first sketching data structure ([0063] wherein the Poisson distribution controls the recording of data into the first sketching data structure which calculates the maximum number of distinct elements);
receiving, by a monitoring server, a plurality of observation events resulting from transactions executed in the distributed computing environment, where each observation event includes data that associates the observation event with a given set and data that describes the observation event, wherein each observation event is generated by a sensor instrumented in an application executing at least a portion of the respective transaction ([Claim 1] of the reference);
determining, for each observation event in the plurality of observation events, a target set to which the observation event belongs using the data that associates the observation event with the target set ([0038] and [0042] wherein for each event a category is used to determine a target set of matching data from the sketching data structure); extracting, for each observation event in the plurality of observation events, a value for a data element from the given observation event ([0047] wherein the value is extracted from the hash);
updating, for each observation event associated with the target set, the first sketching data structure based on the value of the data element from the given observation event and in accordance with the recording parameter ([0047] wherein the target set is updated as long as the value is greater than 0); and
estimating cardinality of data in the target set using data stored in the first sketching data structure ([0054] wherein cardinality estimates are created).
As per claim 2, Ertl discloses the method of claim 1 wherein the first sketching data structure is updated in accordance with the recording parameter b, such that changing value of the recording parameter changes maximum number of distinct data elements that can be represented by the first sketching data structure ([0062] wherein the maximum number of distinct records is determined by the Poisson distribution).
As per claim 3, Ertl discloses the method of claim 1 wherein the configuration parameters further include m as a number of registers in the first sketching data structure, and q as capacity of each resister in the first sketching data structure ([0045] wherein the number of registers and the capacity (recognized as the range in the prior art) are determined for the sketch record).
As per claim 8, Ertl discloses the method of claim 3 further comprises calculating the update candidate by evaluating logarithm of base b for the random number , truncating the logarithm result to an integer and bounding the integer within the range of possible register values ([0045] and [0047] wherein the result is an integer by counting the bits and that integer must be in the range, wherein the log of base 2 is calculated for the random number hash).
As per claim 10,Ertl discloses the method of claim 1 further comprises receiving, by the monitoring server, a second sketching data structure, where the second sketching data structure is compatible with the first sketching data structure ([0070] wherein multiple sketch data structures (two sets A and B) are received, where they are compatible as in their data is aggregated as such); and estimating a set similarity of the data elements represented by the first sketching data structure and data elements represented by the second sketching data structure using data stored in the first and second sketching data structures ([0070] wherein the similarity is determined by determining the overlap, which is subtracted from the total to determine the estimated cardinality.)
As per claim 11, Ertl discloses the method of claim 10 wherein the first sketching data structure and second sketching data structure have configuration parameters with same values ([0014] wherein the parameters are the same for the data for which the estimated cardinality is calculated, which is for the first and second sketching data structure in this case).
As per claim 13, Ertl discloses the method of claim 10 wherein the first sketching data structure is updated in accordance with the recording parameter b, such that changing the recording parameter to increase maximum number of distinct data elements that can be represented by the first sketching data structure decreases the amount of similarity information encoded therein and vice versa ([0070] wherein since the number of different elements is counted twice, if more data is added to the first skeleton structure that is distinct from the second sketching data, the similarity will go down).
As per claim 14, Ertl discloses a computer-implemented method for determining a performance metric in a distributed computing environment, comprising:
setting values of a configuration parameters for a first sketching data structure, where the first sketching data structure is partitioned into a plurality of registers and the configuration parameters includes m as a number of registers in the first sketching data structure ([0045] wherein of registers is determined in step 402), q as capacity of each resister in the first sketching data structure ([0045] wherein the capacity is recognized as the range in the prior art); a as rate of an exponential distribution and a recording parameter, b, that controls recording of data into the first sketching data structure ([0063] wherein the Poisson distribution controls the recording of data into the first sketching data structure which calculates the maximum number of distinct elements);
receiving, by a data recorder, a plurality of observation events resulting from activities executed in the distributed computing environment, where each observation event includes data that associates the observation event with a given set and data that that describes properties of an element of the given set, wherein each observation event is generated by a sensor instrumented in an application executing at least a portion of the activities ([Claim 17]);
determining, for each observation event in the plurality of observation events, a target set to which the observation event belongs using the data that associates the observation event with the given set ([0038] and [0042] wherein for each event a category is used to determine a target set of matching data from the sketching data structure);
determining, for each observation event in the plurality of observation events, an update value for the first sketching data structure ([0047] wherein the value is extracted from the hash), where the update value is derived from a logarithm of a specific base for a pseudo-random number ([0047] wherein the hash is the pseudo random number), where the specific base is value of the recording parameter b and the pseudo-random number is selected from the exponential distribution with a rate set to a ([0045] wherein the update value is derived by determining the number of leading zero bits of the remaining q bits and incrementing this determined number by one, wherein the recording parameter b is set to “2”); selectively updating, for each observation event in the plurality of observation events, the first sketching data structure based on the update value ([0047] wherein the target set is updated as long as the value is greater than 0); and estimating cardinality of data in the target set using data stored in the first sketching data structure ([0054] wherein cardinality estimates are created).
As per claim 15, Ertl discloses the method of claim 14 wherein the sketching data structure is updated in accordance with the recording parameter, such that changing value of the recording parameter changes maximum number of distinct set elements that can be represented by the first sketching data structure ([0062] wherein the maximum number of distinct records is determined by the Poisson distribution).
As per claim 19, Ertl discloses the method of claim 14 wherein the update value is determined by evaluating logarithm of base b for the pseudo-random number, truncating the logarithm result to an integer and bounding the integer within the range of possible register values ([0045] and [0047] wherein the result is an integer by counting the bits and that integer must be in the range, wherein the log of base 2 is calculated for the random number hash).
As per claim 21, Ertl discloses the method of claim 14 further comprises receiving a second sketching data structure, where the second sketching data structure is compatible with the first sketching data structure([0070] wherein multiple sketch data structures (two sets A and B) are received, where they are compatible as in their data is aggregated as such); and estimating similarity of the data elements represented by the first sketching data structure and the second sketching data structure using data stored in the first and second sketching data structures ([0070] wherein the similarity is determined by determining the overlap, which is subtracted from the total to determine the estimated cardinality.)
As per claim 22, Ertl discloses the method of claim 21 wherein the first sketching data structure and second sketching data structure are compatible if they have configuration parameters with same values ([0014] wherein the parameters are the same for the data for which the estimated cardinality is calculated, which is for the first and second sketching data structure in this case).
As per claim 24, Ertl discloses the method of claim 21 wherein the first sketching data structure is updated in accordance with the recording parameter b, such that changing the recording parameter to increase maximum number of distinct data elements that can be represented by the first sketching data structure decreases the amount of similarity information encoded therein and vice versa ([0070] wherein since the number of different elements is counted twice, if more data is added to the first skeleton structure that is distinct from the second sketching data, the similarity will go down).
Allowable Subject Matter
Claims 4-7. 9, 12, 16-20 and 23 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The following limitations in claim 4 including, “wherein updating the first sketching data structure further comprises a) setting an iteration counter to one; b) generating, with a random number generator, a random number using the value of the data element from the given transaction event, where the random number is selected from values having an exponential distribution and a is rate of the exponential distribution; c) calculating an update candidate for the first sketching data, where calculating the updated candidate depends on recording parameter b; d) randomly selecting a register from amongst the plurality of registers; e) updating the selected register with the update candidate when the update candidate is larger than value of the selected register” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to. Claim 5-7 are dependent on claim 4 and are objected to at least based on their dependence.
The following limitations in claim 9 including, “estimating cardinality of the represented data elements by calculating b-K for each register in the plurality of registers and sum results thereof, where b is the recording parameter and K is value of a corresponding register; multiplying the sum with a and natural logarithm of b to yield a first product; multiplying m by difference of one minus reciprocal value of the recording parameter to yield a second product; and dividing the second product by the first product” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 12 including, “determining d0 as a number of corresponding registers in the first and second sketching data structures having the same value; determining d+ as a number of registers in the first sketching data structure having a higher value than corresponding register in the second sketching data structure; determining d- as a number of registers in the first sketching data structure having a lower value than corresponding register in the second sketching data structure; estimating cardinality of data in a second target set using data stored in the second sketching data structure; defining a log likelihood function using the d0, d+, d-, the cardinality of the target set and the cardinality of the second target set and the recording parameter b, where the log likelihood function yields an estimate for Jaccard index for the data in the target set and the second target set” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 16 including, “creating the sequence of pseudo-random values in ascending order; determining the smallest register value stored in the registers of the first sketching data structure; monitoring the number of register value updates that occurred since determination of the smallest register value; determining the smallest register value again, in response to the number of register value updates since the last determination of the smallest register value exceeds a certain threshold, where a new determined smallest register value is greater than any previously determined smallest register value and a current update value is smaller than any previously determined update value; comparing the current update value with the monitored smallest register count; stop processing of the observation event to create further update values in response to the smallest register value being larger than the current update value” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 17 including, “wherein the pseudo-random number is generated by drawing a new random number from the exponential distribution and adding the new random number to a previously generated random number, where the new random number is divided m + 1 - j before being summed with the previously generated random number and j is value of the current iteration” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 18 including, “the pseudo-random random number is generated by segmenting the exponential distribution into a plurality of adjacent, non-overlapping segments and drawing the random number from a given segment in the plurality of adjacent, non-overlapping segments, where the given segment corresponds to the current iteration and the number of adjacent, non-overlapping segments equals the number of registers in the sketching data structure” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 20 including, “estimating cardinality of the data elements by calculating b-K for each register in the plurality of registers and sum results thereof, where b is the recording parameter and K is value of a corresponding register; multiplying the sum with a and natural logarithm of b to yield a first product; multiplying m by difference of one minus reciprocal value of the recording parameter to yield a second product; and dividing the second product by the first product” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
The following limitations in claim 23 including, “wherein estimating similarity further comprises determining d0 as a number of corresponding registers in the first and second sketching data structures having the same value; determining d+ as a number of registers in the first sketching data structure having a higher value than corresponding register in the second sketching data structure; determining d- as a number of registers in the first sketching data structure having a lower value than corresponding register in the second sketching data structure; estimating cardinality of data in a second target set using data stored in the second sketching data structure; defining a log likelihood function using the d0, d+, d-, the cardinality of the target set and the cardinality of the second target set and the recording parameter b, where the log likelihood function yields an estimate for Jaccard index for the data in the target set and the second target set” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KANNAN SHANMUGASUNDARAM whose telephone number is (571)270-7763. The examiner can normally be reached M-F 9:00 AM -6:00 PM.
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/KANNAN SHANMUGASUNDARAM/Primary Examiner, Art Unit 2168