DETAILED ACTION
This action is in response to the filing on 08/09/2023. Claims 1-20, are pending and have been considered below.
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 .
Specification
The disclosure is objected to because of the following informalities:
Para. 9, line 1, recites "host platform 320", should recite -- host platform 120 --.
Para. 15, last line, recites "learning model 410may", should recite -- learning model 410 may --.
Appropriate correction is required.
Claim Objections
Claims 1, 3-4, 6, 9, 11-12, 14, 17, and 19-20 objected to because of the following informalities:
Claim 1, line 10, recites "a representative vector within the cluster", should recite -- a representative vector within the cluster of vectors -- for consistency.
Claim 3, line 3, recites "vectors in the cluster", should recite -- vectors in the cluster of vectors -- for consistency.
Claim 4, lines 3-4, recites "the representative vector within the cluster", should recite -- the representative vector within the cluster of vectors -- for consistency.
Claim 6, lines 2-3, recites "the representative vector within the cluster", should recite -- the representative vector within the cluster of vectors -- for consistency.
Claim 9, line 8, recites "a representative vector within the cluster", should recite -- a representative vector within the cluster of vectors -- for consistency.
Claim 11, lines 2-3, recites "vectors in the cluster", should recite -- vectors in the cluster of vectors -- for consistency.
Claim 12, lines 3-4, recites "the representative vector within the cluster", should recite -- the representative vector within the cluster of vectors -- for consistency.
Claim 14, line 2, recites "the representative vector within the cluster", should recite -- the representative vector within the cluster of vectors -- for consistency.
Claim 17, line 9, recites "a representative vector within the cluster", should recite -- a representative vector within the cluster of vectors -- for consistency.
Claim 19, line 3, recites "vectors in the cluster", should recite -- vectors in the cluster of vectors -- for consistency.
Claim 20, line 4, recites "the representative vector within the cluster", should recite -- the representative vector within the cluster of vectors -- for consistency.
Appropriate correction is required.
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 2-3, 10-11, and 18-19 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.
Claim 2 recites the limitation “wherein the processor is configured to iteratively reduce the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the processor is configured to iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors".
Claim 3 recites the limitation “wherein the processor is configured to iteratively reduce the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the processor is configured to iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors".
Claim 10 recites the limitation “wherein the identifying comprises iteratively reducing the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the identifying comprises iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors".
Claim 11 recites the limitation “wherein the iteratively reducing the plurality of vectors comprises dynamically changing a distance threshold allowed between vectors in the cluster” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the iteratively removing vectors from the plurality of vectors comprises dynamically changing a distance threshold allowed between vectors in the cluster".
Claim 18 recites the limitation “wherein the identifying comprises iteratively reducing the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the identifying comprises iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors".
Claim 19 recites the limitation “wherein the iteratively reducing the plurality of vectors comprises dynamically changing a distance threshold allowed between vectors in the cluster” on lines 1-3, it is unclear what it means to reduce the plurality of vectors, it is unclear if it is reducing the amount of vectors, reducing the dimensions/size of vectors, or some other reduction. For the purpose of examination, it will be interpreted as "wherein the iteratively removing vectors from the plurality of vectors comprises dynamically changing a distance threshold allowed between vectors in the cluster".
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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 9, and 17
Step 1:
Claims 1, 9, and 17 recite a system, method, and manufacture, respectively; therefore, they are directed to one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1:
Claims 1, 9, and 17 recite a system, method, and manufacture, respectively, comprising:
convert a plurality of transaction strings corresponding to a plurality of transactions into a plurality of vectors in multidimensional vector space, respectively — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
identify a cluster of vectors in the multidimensional space that correspond to a subset of transactions among the plurality of transactions that are related based on distances between the cluster of vectors in the multidimensional space — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
identify a representative vector within the cluster that corresponds to an exemplary transaction of the subset of transactions based on the cluster of vectors — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)).
Step 2A Prong 2:
This judicial exception is not integrated into a practical application.
Claims 1, 9, and 17 recite the additional elements of:
A computing system comprising: a data store; and a processor configured to — This element amounts to no more than generally linking the abstract idea to a particular field of use or technological environment (see MPEP § 2106.05(h)).
A computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising — This element amounts to no more than generally linking the abstract idea to a particular field of use or technological environment (see MPEP § 2106.05(h)).
convert a plurality of transaction strings corresponding to a plurality of transactions into a plurality of vectors in multidimensional vector space, respectively, via execution of a machine learning model on the plurality of transaction strings — This element amounts to no more than a recitation of the words "apply it" (or an equivalent), or mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
store the representative vector within the data store — This element amounts to no more than insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is well-understood, routine, conventional activity of storing and retrieving information in memory (see MPEP § 2106.05(d)(II)).
Step 2B:
The claims do not contain significantly more than the judicial exception.
Claims 1, 9, and 17 recite the additional elements of:
A computing system comprising: a data store; and a processor configured to — This element amounts to no more than generally linking the abstract idea to a particular field of use or technological environment (see MPEP § 2106.05(h)).
A computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising — This element amounts to no more than generally linking the abstract idea to a particular field of use or technological environment (see MPEP § 2106.05(h)).
convert a plurality of transaction strings corresponding to a plurality of transactions into a plurality of vectors in multidimensional vector space, respectively, via execution of a machine learning model on the plurality of transaction strings — This element amounts to no more than a recitation of the words "apply it" (or an equivalent), or mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
store the representative vector within the data store — This element amounts to no more than insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is well-understood, routine, conventional activity of storing and retrieving information in memory (see MPEP § 2106.05(d)(II)).
As such claims 1, 9, and 17 are not patent eligible.
Dependent Claims 2-8, 10-16, and 18-20
Step 1:
Claims 2-8, 10-16, and 18-20 recite a system; therefore, it is/they are directed to one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1:
Claims 2-8, 10-16, and 18-20 merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claims 1, 9, and 17, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim(s) disclose similar limitations described for the independent claim(s) above and do not provide anything more than the abstract idea.
Claims 2, 10, and 18 recite a system, method, and manufacture comprising:
wherein the processor is configured to iteratively reduce the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors (interpreted as wherein the processor is configured to iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors per 35 U.S.C. 112(b) rejection above) — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)).
Claims 3, 11, and 19 recite a system, method, and manufacture comprising:
wherein the processor is configured to iteratively reduce the plurality of vectors based on a dynamically changing distance threshold allowed between vectors in the cluster (interpreted as wherein the processor is configured to iteratively remove vectors from the plurality of vectors based on a dynamically changing distance threshold allowed between vectors in the cluster per 35 U.S.C. 112(b) rejection above) — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
Claims 4, 12, and 20 recite a system, method, and manufacture comprising:
wherein the processor is further configured to convert a plurality of additional transaction strings into a plurality of additional vectors, identify additional vectors within the cluster of vectors, and modify the representative vector within the cluster based on the additional vectors — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
Claims 5 and 13 recite a system and method comprising:
wherein the processor is configured to identify a centroid of the cluster of vectors within the multidimensional vector space as the representative vector within the cluster of vectors — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
Claims 6 and 14 recite a system and method comprising:
wherein the processor is configured to select a vector from among the cluster of vectors as the representative vector within the cluster based on a predetermined criteria — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)).
Claims 7 and 15 recite a system and method comprising:
wherein the processor is configured to identify duplicate vectors among the plurality of vectors in the multidimensional vector space, and remove the duplicate vectors prior to identifying the cluster of vectors — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
Claims 8 and 16 recite a system and method comprising:
wherein the processor is further configured to receive a new group of transaction strings, convert the new group of transaction strings into a group of vectors in multidimensional space, and identify a transaction among the new group of transactions that corresponds to the exemplary transaction based on a comparison of the group of vectors to the representative vector in multidimensional vector space — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations (see MPEP, § 2106.04(a)(2)(A)).
Step 2A Prong 2:
This judicial exception is not integrated into a practical application.
Claims 8 and 16 recite the additional element of:
wherein the processor is further configured to receive a new group of transaction strings — This element amounts to no more than insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is well-understood, routine, conventional activity of receiving or transmitting data over a network (see MPEP § 2106.05(d)(II)).
Step 2B:
The claims do not contain significantly more than the judicial exception.
Claims 8 and 16 recite the additional element of:
wherein the processor is further configured to receive a new group of transaction strings — This element amounts to no more than insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is well-understood, routine, conventional activity of receiving or transmitting data over a network (see MPEP § 2106.05(d)(II)).
As such claims 2-8, 10-16, and 18-20 are not patent eligible.
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.
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. (US 2019/0272482 A1), hereinafter Patil, in view of Perrizo et al. (US 2008/0109437 A1), hereinafter Perrizo.
Regarding claim 1, Patil teaches A computing system comprising: a data store; and a processor configured to (Patil discloses a data sampling system which includes one or more processors and gathers transaction data from a transaction server comprising one or more storage devices [see Patil, para. 17-19 and FIG. 1]):
convert a plurality of transaction strings corresponding to a plurality of transactions into a plurality of vectors in multidimensional vector space, respectively, via execution of a machine learning model on the plurality of transaction strings (Patil discloses that the transaction records include text string descriptions [see Patil, para. 20] and are converted to transaction vectors through a vectorizer using a word2vec model [see Patil, para. 35]);
identify a cluster of vectors in the multidimensional space that correspond to a subset of transactions among the plurality of transactions that are related based on distances between the cluster of vectors in the multidimensional space (Patil discloses clustering the transaction records, by their word2vec representations, by calculating a Euclidean distance between the vector representations [see Patil, para. 42]);
identify a representative vector within the cluster based on the cluster of vectors (Patil discloses computing a centroid for each of the clusters [see Patil, para. 42]).
It would have been obvious to one of ordinary skill in the art before the effective filing date that in the same manner that Patil stored transaction data [see Patil, para. 19], to store the vector representation of the transaction data, including the representation vectors, to teach store the representative vector within the data store, otherwise it would not be possible to compare sample data against the clusters without knowing where the cluster centroid is.
However, Patil fails to teach identify a representative vector within the cluster that corresponds to an exemplary transaction of the subset of transactions based on the cluster of vectors.
In the same field of endeavor, Perrizo teaches:
identify a representative vector within the cluster that corresponds to an exemplary transaction of the subset of transactions based on the cluster of vectors (Perrizo discloses determining a high-quality value k for the number of centroids as a pre-processing step for k-means or k-medoid clustering [see Perrizo, para. 63], and that k-medoid clustering selects k-centroids as representative vectors and iteratively replaces the centroids if the quality of clustering is improved [see Perrizo, para. 9]).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate identify a representative vector within the cluster that corresponds to an exemplary transaction of the subset of transactions based on the cluster of vectors as suggested in Perrizo into Patil because both methods are directing to clustering models (see Patil, Abstract; see Perrizo, Abstract). Incorporating the teaching of Perrizo into Patil would significantly speed up the computation and scale to very large data sets (see Perrizo, para. 17).
It would have been further obvious to one of ordinary skill in the art before the effective filing date to modify the method of Patil with the clustering technique of Perrizo [see Perrizo, para. 9 and 63] such that the clustering identifies an initial exemplary transaction as the centroid for each of the cluster of vectors and iteratively determines higher quality exemplary transactions for centroids as the clustering progresses.
Regarding claim 2, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the processor is configured to iteratively reduce the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors (interpreted as wherein the processor is configured to iteratively removing vectors from the plurality of vectors based on dynamically changing criteria to generate the cluster of vectors per 35 U.S.C. 112(b) rejection above) (Patil discloses a filtering method where records within a δ-ball distance threshold from cluster anchors are dropped, and that the filtering depends on the anchor sample that filtering starts with [see Patil, para. 49-50 and 59]. Thus, the criteria is dynamic because the anchor sample started with changes the outcome, and there are a plurality of anchor samples that can be chosen each time).
Regarding claim 3, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 2 and further teaches:
wherein the processor is configured to iteratively reduce the plurality of vectors based on a dynamically changing distance threshold allowed between vectors in the cluster (interpreted as wherein the processor is configured to iteratively remove vectors from the plurality of vectors based on a dynamically changing distance threshold allowed between vectors in the cluster per 35 U.S.C. 112(b) rejection above) (Patil discloses a filtering method where records within a δ-ball distance threshold from cluster anchors are dropped, and that the filtering depends on the anchor sample that filtering starts with [see Patil, para. 49-50 and 59]. Thus, the criteria is dynamic because the anchor sample started with changes the outcome, and there are a plurality of anchor samples that can be chosen each time).
Regarding claim 4, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the processor is further configured to convert a plurality of additional transaction strings into a plurality of additional vectors, identify additional vectors within the cluster of vectors, and modify the representative vector within the cluster based on the additional vectors (Patil discloses that new samples received periodically can be added to the data set for the clustering and filtering process, as well as periodically repeating the entire clustering process leading to new cluster centroids, boundary samples, and anchor samples [see Patil, para. 51]).
Regarding claim 5, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the processor is configured to identify a centroid of the cluster of vectors within the multidimensional vector space as the representative vector within the cluster of vectors (Patil discloses computing a centroid for each of the clusters [see Patil, para. 42]).
Regarding claim 6, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the processor is configured to select a vector from among the cluster of vectors as the representative vector within the cluster based on a predetermined criteria (It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Patil with the clustering technique of Perrizo [see Perrizo, para. 9 and 63] such that the clustering identifies an initial exemplary transaction as the centroid for each of the cluster of vectors and iteratively determines higher quality exemplary transactions for centroids as the clustering progresses).
Regarding claim 8, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the processor is further configured to receive a new group of transaction strings, convert the new group of transaction strings into a group of vectors in multidimensional space, and identify a transaction among the new group of transactions that corresponds to the exemplary transaction based on a comparison of the group of vectors to the representative vector in multidimensional vector space (Patil discloses that new samples received periodically can be added to the data set for the clustering and filtering process, as well as periodically repeating the entire clustering process leading to new cluster centroids, boundary samples, and anchor samples [see Patil, para. 51]).
Regarding claim 9, claim 9 contains substantially similar limitations to those found in claim 1 above. Consequently, claim 9 is rejected for the same reasons.
Regarding claim 17, claim 17 contains substantially similar limitations to those found in claim 1. Therefore, it is rejected for the same reason as claim 1 above. Additionally, the combination of Patil and Perrizo further teaches:
A computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising (Patil discloses that the system includes a computer-readable medium which provides instructions to the processor to carry out the method [see Patil, para. 60-61]).
Regarding claims 10 and 18, claims 10 and 18 contains substantially similar limitations to those found in claim 2 above. Consequently, claims 10 and 18 are rejected for the same reasons.
Regarding claims 11 and 19, claims 11 and 19 contains substantially similar limitations to those found in claim 3 above. Consequently, claims 11 and 19 are rejected for the same reasons.
Regarding claims 12 and 20, claims 12 and 20 contains substantially similar limitations to those found in claim 4 above. Consequently, claims 12 and 20 are rejected for the same reasons.
Regarding claim 13, claim 13 contains substantially similar limitations to those found in claim 5 above. Consequently, claim 13 is rejected for the same reasons.
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 6 above. Consequently, claim 14 is rejected for the same reasons.
Regarding claim 16, claim 16 contains substantially similar limitations to those found in claim 8 above. Consequently, claim 16 is rejected for the same reasons.
Claims 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. (US 2019/0272482 A1), hereinafter Patil, in view of Perrizo et al. (US 2008/0109437 A1), hereinafter Perrizo, as applied in claim 1 above, and further in view of XIONG et al. (US 2021/0004416 A1), hereinafter Xiong.
Regarding claim 7, the combination of Patil and Perrizo as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
However, the combination of Patil and Perrizo fails to teach wherein the processor is configured to identify duplicate vectors among the plurality of vectors in the multidimensional vector space, and remove the duplicate vectors prior to identifying the cluster of vectors.
In the same field of endeavor, Xiong teaches:
wherein the processor is configured to identify duplicate vectors among the plurality of vectors in the multidimensional vector space, and remove the duplicate vectors prior to identifying the cluster of vectors (Xiong discloses deduplication and binary classification to remove duplicate vectors and simplify analysis [see Xiong, para. 32]).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate wherein the processor is configured to identify duplicate vectors among the plurality of vectors in the multidimensional vector space, and remove the duplicate vectors prior to identifying the cluster of vectors as suggested in Xiong into the combination of Patil and Perrizo because both methods are directed to clustering (see Patil, Abstract; see Xiong, Abstract). Incorporating the teaching of Xiong into the combination of Patil and Perrizo would simplify the analysis (see Xiong, para. 32).
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 7 above. Consequently, claim 14 is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BRUSS et al. (US 2020/0226460 A1) discloses clustering transactions in multidimensional space based on their distances.
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/J.T.B./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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