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 .
This office action is in response to submission of application on 1/16/2024.
Claims 1-20 are presented for examination.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 7 state “the selected subsets.” However, in claim 1, it introduces “selected subsets of the dataset” and “selects the candidate anomalous subset” such that it’s unclear if the “selected subsets” include the candidate subset or not.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-20 are rejected under 35 U.S.C. 101 because the claims invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claims 10-18 are directed to a method and claim 19-20 are directed to a computer readable storage medium; therefore, all these claims are directed to one of the four statutory categories.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claim 10 recites limitations of:
computing, by the system, a diversity score of the candidate anomalous subset relative to selected subsets of the dataset; – mathematical concept (relationships, formulas or equations, calculations) of computing a diversity score
and selecting, by the system, the candidate anomalous subset based on the diversity score. – mental process (observation, evaluation, judgement) as a human mind can select a candidate
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claim 10 recites limitations of:
obtaining, by a system operatively coupled to a processor, a candidate anomalous subset of a dataset; - obtaining a candidate merely amounts to data gathering which is insignificant extra-solution activity. See MPEP § 2106.05(g), item (3), which identifies necessary data gathering and outputting as an example of extra-solution activity.
Step 2B: Does the claim recite additional elements that amounts to significantly more than the
judicial exception?
The additional elements are:
obtaining, by a system operatively coupled to a processor, a candidate anomalous subset of a dataset; - obtaining a candidate merely amounts to data gathering which is insignificant extra-solution activity. See MPEP § 2106.05(g). Data gathering is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(iv)
The additional elements do not amount to significantly more than the abstract idea. Therefore,
the claim is not patent eligible.
Independent claim 19 recites the same relevant limitations and a similar analysis applies. Claim 19 recites the additional elements of “A computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:” – components recited at a high level are construed as generic computer components used to implement the abstract idea. See MPEP 2106.05(f)(2). They do not integrate the abstract idea into a practical application. Nor do they amount to
significantly more. Therefore, the independent claims are not patent eligible.
The above analysis similarly applies to the dependent claims.
Dependent claim 11 and 20 recites, “compares the diversity score to a penalty threshold to determine the selected subsets.” – mental process (observation, evaluation, judgement) as a human mind can compare the diversity score to a penalty threshold.
Dependent claim 12 recites, “wherein the diversity score is computed with an intersection-over-union measure or intersection-over-previous metric.” – mathematical concept (relationships, formulas or equations, calculations) of computing using an intersection-over-union measure or intersection-over-previous metric
Dependent claim 13 recites, “initializing, by the system, records of the dataset with penalty values.” – mathematical concept (relationships, formulas or equations, calculations) of initializing records
Dependent claim 14 recites, “selecting, by the system, the candidate anomalous subset if the diversity score is within the penalty threshold.” – mental process (observation, evaluation, judgement) as a human mind can select the candidate anomalous subset
Dependent claim 15 recites, “rejecting, by the system, the candidate anomalous subset if the diversity score is not within the penalty threshold.” – mental process (observation, evaluation, judgement) as a human mind can choose to reject the candidate
Dependent claim 16 recites, “incrementing, by the system, the penalty values of records that overlap between the candidate anomalous subset and the selected subsets by a regularization parameter in response to a determination that the candidate anomalous subset is not selected.” – mathematical concept (relationships, formulas or equations, calculations) of incrementing the penalty values of records
Dependent claim 17 recites, “selecting, by the system, the candidate anomalous subset based on the penalty values of records in the dataset.” – mental process (observation, evaluation, judgement) as a human mind can select the candidate anomalous subset
Dependent claim 18 recites,
“iteratively incrementing, by the system, the penalty values;” – mathematical concept (relationships, formulas or equations, calculations) of incrementing the penalty values
“and iteratively obtaining, by the system, a candidate anomalous subset of the dataset until
the candidate anomalous subset is selected.” Iteratively obtaining merely amounts to data gathering which is insignificant extra-solution activity. See MPEP § 2106.05(g). Data gathering is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(iv)
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The following are the references used:
Van Leeuwen et al. (Diverse Subgroup Set Discovery, herein Van)
Speakman et al. (Penalized Fast Subset Scanning, herein Speakman)
Konig et al. (Performance Anomaly Diagnosis, US 20160147585 A1, herein Konig)
Claim(s) 1, 2, 4-11, and 13-20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Van in view of Speakman.
Regarding claim 1,
Van teaches,
A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: (Van, page 20, section 7, “All proposed and used methods in this paper have been implemented in C++… All experiments were conducted on a quad-core Xeon 3.0 GHz system with 8 Gb of memory”)
a scoring component that computes a diversity score of the candidate anomalous subset relative to selected subsets of the dataset; (Van, page 14-15, section 5.2, “A score based on multiplicative weighted covering (Lavraˇc et al 2004) is used to weigh the quality of each subgroup, aiming to minimise the overlap between the selected subgroups. This score is defined as (G, Sel) = 1|G|_t∈Gαc(t,Sel)… The less often tuples in subgroup G are already covered by subgroups in the selection, the larger the score” and page 13, section 4.1, “let the cover count of a tuple be the number of times it is covered by a subgroup in a subgroup set… Define the cover count of a tuple t ∈ S as c(t, G) = _G∈G sG(t).” note: the reference works through its candidate groups one at a time and keeps a running set of the groups it has already selected. For each new candidate, the reference measures how much of that candidate is made up of records that already selected groups also contain, and converts that overlap into a single value, (G, sel), a candidate built mostly from already used records get a low values, and a candidate build from fresh records get a high value. That value is therefore a measure of how different the candidate is from the groups already chosen. The candidate anomalous subset maps to the candidate group, the selected subset maps to the the already selected groups, and the diversity score… relative to selected subsets maps to the computer overlap value.)
and a selection component that selects the candidate anomalous subset based on the diversity score. (Van, page 16, section 5.2, “In each iteration, the subgroup that maximises (G, Sel) ・ ϕ(G) is selected… the _-scores for the remaining Cands are updated each iteration.” note: each round, the reference looks at the remaining candidates and keeps the one whose combined score is highest, where that score rewards both quality and low overall with the groups already selected. Because the overlap (diversity) is build into that score, the choice of which candidate to keep turn on its diversity value. Selects the candidate anomalous subset maps to keeping the top-scoring candidates, and based on the diversity score maps to the score that contains the diversity value.)
Van does not teach,
a discovery component that obtains a candidate anomalous subset of a dataset;
Speakman teaches,
a discovery component that obtains a candidate anomalous subset of a dataset; (Speakman, page 1, section 1, “we consider the “subset scan” approach to pattern detection, which treats the
problem as a constrained search over subsets of data elements, with the goal of finding the
most anomalous subsets.” and page 4, section 1.1, “our goal is to identify a subset of the data
S ⊆ D that maximizes a score function F(S).”, note: Speakman scans through the possible groups of records in a dataset and pulls out the group that stands out the most from normal, the most anomalous one.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Van with Speakman because applying Van’s diversity scoring and selection to Speakman’s anomalous subsets allow anomalous subsets that are diverse rather than redundant. The results surface distinct anomalies and cover more of the data’s anomalous regions rather than flagging the same one repeatedly.
Regarding claim 2,
The combination of Van and Speakman teaches,
The system of claim 1, wherein the selection component compares the diversity score to a penalty threshold to determine the selected subsets. (Van, page 16, section 5.2, “Subgroups are iteratively selected until no candidate subgroup meets the minimum score... Selection stops when there is no G ∈ Cands for which _(G, Sel) ・ ϕ(G) ≥ δ.”, note: the reference sets a cutoff value and adds a candidate to the final set only while the candidate’s score stays on the good side of that cutoff, once nothing can clear the cutoff, it stops.)
Regarding claim 4,
The combination of Van and Speakman teaches,
The system of claim 1, wherein the computer executable components further comprise: an initialization component that initializes records of the dataset with penalty values. (Van, page 8, section 2.2, “All tuples start with a weight of 1, but when a tuple is covered its weight decreases.”, note: Before the search begins, the reference gives every record the same number, As records get used by selected groups, that number moves, and that is how the search later avoids reusing them. Initializing records with penalty value maps to giving every record a starting number.)
Regarding claim 5,
The combination of Van and Speakman teaches,
The system of claim 2, wherein the selection component selects the candidate anomalous subset if the diversity score is within the penalty threshold. (Van, page 16, section 5.2, “until no candidate subgroup meets the minimum scoreδ… for which _(G, Sel) ・ ϕ(G) ≥ δ.” note: when a candidate score sits on the acceptable side of the cutoff, meaning it overlaps the already selected groups little enough, the reference keeps it.)
Regarding claim 6,
The combination of Van and Speakman teaches,
The system of claim 2, wherein the selection component rejects the candidate anomalous subset if the diversity score is not within the penalty threshold. (Van, page 16, section 5.2, “Selection stops when there is no G ∈ Cands for which _(G, Sel) ・ ϕ(G) ≥ δ.”, note: When a candidate’s score sits on the wrong side of the cutoff, meaning it overlaps the already selected group too much, the reference does not keep it.)
Regarding claim 7,
The combination of Van and Speakman teaches,
The system of claim 4, wherein the computer executable components further comprise: a regularization component that increments the penalty values of records that overlap between the candidate anomalous subset and the selected subsets by a regularization parameter in response to a determination that the candidate anomalous subset is not selected. (Van, page 8, section 2.2, “multiplicative weights: w(t, i ) = γ i (for a given parameter 0 < γ < 1)”, note: the increment, every time a record shows up in a group that has been kept, the reference adjusts that record’s number by a set, fixed amount, pushing the next round of searching toward records that have not been used yet. The regularization parameter maps to that fixed amount.)
Regarding claim 8,
The combination of Van and Speakman teaches,
The system of claim 1, wherein the discovery component selects the candidate anomalous subset based on the penalty values of records in the dataset. (Speakman, page 7, section 2, “For a fixed risk q, functions satisfying ALTSS can be efficiently optimized over all subsets S ⊆ D by including all and only those data elements making a positive contribution to the scoring function, that is, si ∈ argmaxS⊆D F(S | q) if and only if γi (q) =λi (q) + _i > 0.”, note: When Speakman’s scan forms the anomalous subset, it includes a record only if that record makes a positive contribution once its penalty is added in, a record whose base score plus its penalty is above zero is kept, and on that is not is left out. Which records end up in the discovered subset is decided directly by the records’ penalty values.)
Regarding claim 9,
The combination of Van and Speakman teaches,
The system of claim 7, wherein the regularization component iteratively increments the penalty values, and wherein the discovery component iteratively obtains a candidate anomalous subset of the dataset until the selection component selects the candidate anomalous subset. (Van, page 16, section 5.2, “In k iterations, k subgroups are selected… the _-scores for the remaining Cands are updated each iteration.” and page 13, section 4.1, “the cover count of a tuple t ∈ S as c(t, G) = _G∈G sG(t).” note: This is a loop. The reference finds a group, updates the pre-record cover counts and the resulting scores, find the next group, updates again, over and over, until it lands on a group worth keeping, then repeats for the group after that.)
Claims 10, 11, and 13-18 is a method claim, A computer-implemented method, comprising, that corresponds to system claim 1, 2, and 4-9, respectively. Otherwise, they are not patentably distinguishable. Therefore, claims 10, 11, and 14-18 are rejected for the same reasons as, claims 1, 2, and 4-9, respectively.
Claims 19 and 20 are a product claim, A computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to, (Van, page 20, section 7, “All proposed and used methods in this paper have been implemented in C++… All experiments were conducted on a quad-core Xeon 3.0 GHz system with 8 Gb of memory”) that corresponds to system claim 1 and 2, respectively. Otherwise, they are not patentably distinguishable. Therefore, claims 19 and 20 are rejected for the same reasons as, claims 1, 2, and 4-9, respectively.
Claim(s) 3 and 12, is/are rejected under 35 U.S.C. 103 as being unpatentable over Van in view of Speakman and in further view of Konig.
Regarding claim 3, Konig teaches,
The system of claim 1, wherein the diversity score is computed with an intersection-over- union measure or intersection-over-previous metric. (Konig, paragraph [0060], “the Jaccard-distance between σ.sub.θ.sub.1(R) and σ.sub.θ.sub.2(R)”, note: the Jaccard measure is intersection -over-union. It takes the records two groups share (their intersection) and divide by all the records they cover between them (their union).)
Claim 12 is a method claim, A computer-implemented method, comprising, that corresponds to system claim 3, respectively. Otherwise, they are not patentably distinguishable. Therefore, claim 12 is rejected for the same reasons as, claim 3, respectively.
Conclusion
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/U.P.T./ Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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