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 .
The action is in response to the application filed on 12/21/2022. Claims 1-7 are pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-037362, filed on 10 March 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/21/2022 is in compliance with the provisions of 37 CFR 1.97, 1.98, and MPEP § 609. It has been placed in the application file, and the information referred to therein has been considered as to the merits.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claim 3 is 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 3 recites “prediction performances of which the tendency is similar”. Similar is a relative term and the claim does not provide any details as to how one would determine if a tendencies of prediction performances is “similar”. The claim is rejected.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1:
The claim recites a non-transitory computer-readable recording medium, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea generating a plurality of division candidate datasets divided in accordance with different criteria from each other, from a combined dataset is which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea determining division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset which amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a non-transitory computer readable recording medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of using a non-transitory computer readable recording medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of generating a plurality of division candidate datasets divided in accordance with different criteria from each other, from a combined dataset is generally linked to the abstract idea, therefore does not amount to significantly more MPEP 2106.05(h).
The additional element of using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not amount to significantly more MPEP 2106.05(h).
Therefore, the claim is ineligible.
Regarding Claim 2:
Claim 2 which incorporates the rejection of Claim 1, recites further abstract ideas generating a first vector whose components are the respective prediction performances… using the divided dataset, generating each of second vectors whose components are the respective prediction performances… for each of the plurality of division candidate datasets, and identifying the division candidate datasets that correspond to the second vectors with the highest similarity, from among the plurality of division candidate datasets which amount to mental processes as they can be performed in a human mind. The claim recites a further abstract idea calculating similarity between each of the second vectors that one-to-one correspond to the plurality of division candidate datasets and the first vector which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 3:
Claim 3 which incorporates the rejection of Claim 1, recites further abstract ideas identifying a tendency of the respective prediction performances… using the divided dataset, identifying the tendency of the respective prediction performances… for each of the plurality of division candidate datasets, and identifying the division candidate datasets with the prediction performances of which the tendency is similar to the tendency of the respective prediction performances that correspond to the divided dataset which amount to mental processes as they can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 4:
Claim 4 which incorporates the rejection of Claim 1, recites a further abstract idea further dividing the training data into internal training data and internal validation data by using the determined division criteria which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 5:
Claim 5 which incorporates the rejection of Claim 1, recites further abstract ideas generating an additional dataset obtained by newly adding additional data to the divided dataset that includes the training data and the validation data and dividing the additional dataset into the training data and the validation data by using the determined division criteria which amount to mental processes as they can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 6:
Step 1:
The claim recites a method, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea generating… a plurality of division candidate datasets divided in accordance with different criteria from each other, from a combined dataset is which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea identifying… the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea determining… division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset which amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a processor circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of generating… respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using… each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a processor circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of generating… respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using… each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not amount to significantly more MPEP 2106.05(h).
Therefore, the claim is ineligible.
Regarding Claim 7:
Step 1:
The claim recites an information processing apparatus, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea generating a plurality of division candidate datasets divided in accordance with different criteria from each other, from a combined dataset is which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea determining division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset which amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance is generally linked to the abstract idea, therefore does not amount to significantly more MPEP 2106.05(h).
Therefore, the claim is ineligible.
Claim Rejections - 35 USC § 103
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 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Meliksetian et al. (U.S. Patent Application Publication No. US 20230094000 A1), hereinafter “Meliksetian” in view of Leite et al. “Exploiting Performance-based Similarity between Datasets in Metalearning”, hereinafter “Leite”.
Regarding Claim 1, Meliksetian teaches:
A non-transitory computer-readable recording medium storing a determination program for causing a computer to execute processing (Meliksetian, ¶37, “include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”) comprising:
generating a plurality of division candidate datasets divided in accordance with different criteria from each other (Division candidate dataset is an aggregated dataset group, Meliksetian, ¶4, “generating a plurality of aggregated dataset group. Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets, grouped together based on a calculation of a degree of correlation”), from a combined dataset obtained by combining training data and validation data in a divided dataset(Combined dataset is different aggregate dataset group made of plurality of datasets including datasets making up division candidate dataset, Meliksetian, ¶4, “Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets”) that has been divided into the training data and the validation data used for machine learning (Data is split into training and testing, Meliksetian, ¶54, “training, testing, and evaluating ML computer model instances”);
generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets (Meliksetian, ¶4, “generating, for each aggregated dataset group, a plurality of ML computer model instances”);
determining division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset (Meliksetian, ¶4, “calculation of a degree of correlation between characteristics associated with each of the original datasets in the plurality of original datasets to generate an aggregated dataset”).
Meliksetian does not explicitly teach:
using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed;
identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets.
However, Leite teaches:
using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed (Leite, p. 90, Abstract, “performance-based characterization of each dataset, which is in the form of a vector of performance values of different algorithms”);
identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets (Closest prediction performances are identified when checking similarity, Leite, p. 93, col. 2, ¶3, “dataset weights capturing dataset similarity of each dataset di to the target dataset dt”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Leite recommending the best workflow for a target dataset using similarity measurements with the pipelines of Meliksetian. The motivation to do so would be to find the best pipelines to use for a dataset (Leite, p. 90, Abstract, “the recommendations of the system get adjusted to the characteristics of the target dataset. We show that this new strategy leads to improved results of the active testing approach”).
Regarding Claim 2, Meliksetian in view of Leite teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. In the combination as set forth above, Leite further teaches:
wherein the identifying includes:
generating a first vector whose components are the respective prediction performances when the respective machine learning pipelines are executed by using the divided dataset and generating each of second vectors whose components are the respective prediction performances when the respective machine learning pipelines are executed, for each of the plurality of division candidate datasets (Leite, p. 91, col. 1, ¶2, “vectors of performance values (multiple landmarkers) obtained on different datasets that can be used to characterize datasets”);
calculating similarity between each of the second vectors that one-to-one correspond to the plurality of division candidate datasets and the first vector (Leite, p. 91, col. 1, ¶2, “Dataset similarity is then assessed by comparing these two performance vectors, by employing, for instance, Spearman’s correlation”); and identifying the division candidate datasets that correspond to the second vectors with the highest similarity, from among the plurality of division candidate datasets (Leite, p. 93, col. 2, ¶3, “includes dataset weights capturing dataset similarity of each dataset di to the target dataset dt . The identification of the best competitor takes these weights into account”).
Regarding Claim 3, Meliksetian in view of Leite teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. In the combination as set forth above, Leite further teaches:
wherein the identifying includes:
identifying a tendency of the respective prediction performances when the respective machine learning pipelines are executed by using the divided dataset and identifying the tendency of the respective prediction performances when the respective machine learning pipelines are executed, for each of the plurality of division candidate datasets (Performance values identify tendency of performances when pipelines are executed, Leite, p. 93, col. 1, ¶1, “a series of landmarkers, representing performance values of different algorithms on a given dataset. Let us represent this measure by PdtA, where A represents the algorithms and dt the given target dataset. This measure can be calculated for any dataset used in the past. The measure PdiA is particularly useful”); and
identifying the division candidate datasets with the prediction performances of which the tendency is similar to the tendency of the respective prediction performances that correspond to the divided dataset, from among the plurality of division candidate datasets (Leite, p. 93, col. 2, ¶3, “includes dataset weights capturing dataset similarity of each dataset di to the target dataset dt. The identification of the best competitor takes these weights into account”, Leite, p. 93, col. 1, ¶3, “Pairs of measures PdtA and PdiA discussed in the previous subsection can be used to calculate dataset similarity”).
Regarding Claim 4, Meliksetian in view of Liete teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Meliksetian further teaches:
for causing the computer to execute the process comprising further dividing the training data into internal training data and internal validation data by using the determined division criteria (During re-training there will be different combinations of training and validation data, Meliksetian, ¶26, “improve ML computer model (or simply “model”) training/re-training, model evaluation… multiple automated model-training-testing runs”, based on division criteria, ¶4, “Each aggregated dataset group comprises one or more original datasets… grouped together based on a calculation of a degree of correlation between characteristics… to generate an aggregated dataset”).
Regarding Claim 5, Meliksetian in view of Liete teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Meliksetian further teaches:
for causing the computer to execute a process comprising:
generating an additional dataset obtained by newly adding additional data to the divided dataset that includes the training data and the validation data (New combinations of data can be made with aggregations of original datasets in new model-training-testing runs, Meliksetian, ¶26, “improve ML computer model (or simply “model”) training/re-training, model evaluation… multiple automated model-training-testing runs”, datasets comprise training and validation data, ¶54, “training, testing, and evaluating ML computer model instances”); and
dividing the additional dataset into the training data and the validation data by using the determined division criteria (Data is divided into more training and validation data with different aggregated datasets, Meliksetian, ¶4, “calculation of a degree of correlation between characteristics associated with each of the original datasets in the plurality of original datasets to generate an aggregated dataset”).
Regarding Claim 6, Meliksetian teaches:
A determination method implemented by a computer (Meliksetian, ¶37, “instructions for use by an instruction execution device”), the determination method comprising:
generating, in a processor circuit of the computer (Meliksetian, ¶6, “executed by the one or more processors”), a plurality of division candidate datasets divided in accordance with different criteria from each other (Division candidate dataset is an aggregated dataset group, Meliksetian, ¶4, “generating a plurality of aggregated dataset group. Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets, grouped together based on a calculation of a degree of correlation”), from a combined dataset obtained by combining training data and validation data in a divided dataset(Combined dataset is different aggregate dataset group made of plurality of datasets including datasets making up division candidate dataset, Meliksetian, ¶4, “Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets”) that has been divided into the training data and the validation data used for machine learning (Data is split into training and testing, Meliksetian, ¶54, “training, testing, and evaluating ML computer model instances”);
generating, in the processor circuit of the computer(Meliksetian, ¶6, “executed by the one or more processors”), respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets (Meliksetian, ¶4, “generating, for each aggregated dataset group, a plurality of ML computer model instances”);
determining, in the processor circuit of the computer, division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset (Meliksetian, ¶4, “calculation of a degree of correlation between characteristics associated with each of the original datasets in the plurality of original datasets to generate an aggregated dataset”).
Meliksetian does not explicitly teach:
using… each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed;
identifying… the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets
However, Leite teaches:
using… each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed (Leite, p. 90, Abstract, “performance-based characterization of each dataset, which is in the form of a vector of performance values of different algorithms”);
identifying… the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets (Closest prediction performances are identified when checking similarity, Leite, p. 93, col. 2, ¶3, “dataset weights capturing dataset similarity of each dataset di to the target dataset dt”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Leite recommending the best workflow for a target dataset using similarity measurements with the pipelines of Meliksetian. The motivation to do so would be to find the best pipelines to use for a dataset (Leite, p. 90, Abstract, “the recommendations of the system get adjusted to the characteristics of the target dataset. We show that this new strategy leads to improved results of the active testing approach”)
Regarding Claim 7, Meliksetian teaches:
An information processing apparatus (Meliksetian, ¶41, “data processing apparatus”) comprising:
a memory (Meliksetian, ¶6, “a memory”); and
a processor coupled to the memory, the processor being configured to perform processing, the processing including (Meliksetian, ¶6, “a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined”):
generating a plurality of division candidate datasets divided in accordance with different criteria from each other (Division candidate dataset is an aggregated dataset group, Meliksetian, ¶4, “generating a plurality of aggregated dataset group. Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets, grouped together based on a calculation of a degree of correlation”), from a combined dataset obtained by combining training data and validation data in a divided dataset(Combined dataset is different aggregate dataset group made of plurality of datasets including datasets making up division candidate dataset, Meliksetian, ¶4, “Each aggregated dataset group comprises one or more original datasets, of a plurality of original datasets”) that has been divided into the training data and the validation data used for machine learning (Data is split into training and testing, Meliksetian, ¶54, “training, testing, and evaluating ML computer model instances”);
generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the plurality of division candidate datasets (Meliksetian, ¶4, “generating, for each aggregated dataset group, a plurality of ML computer model instances”);
determining division criteria used for the identified division candidate dataset to be the division criteria used for the divided dataset (Meliksetian, ¶4, “calculation of a degree of correlation between characteristics associated with each of the original datasets in the plurality of original datasets to generate an aggregated dataset”).
Meliksetian does not explicitly teach:
using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed;
identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets
However, Leite teaches:
using each of the divided dataset and the plurality of division candidate datasets to calculate respective prediction performances, each of the respective prediction performances indicating a prediction performance when a corresponding machine learning pipeline of the respective machine learning pipelines is executed (Leite, p. 90, Abstract, “performance-based characterization of each dataset, which is in the form of a vector of performance values of different algorithms”);
identifying the division candidate datasets that have the prediction performances closest to the respective prediction performances calculated by using the divided dataset, from among the plurality of division candidate datasets (Closest prediction performances are identified when checking similarity, Leite, p. 93, col. 2, ¶3, “dataset weights capturing dataset similarity of each dataset di to the target dataset dt”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Leite recommending the best workflow for a target dataset using similarity measurements with the pipelines of Meliksetian. The motivation to do so would be to find the best pipelines to use for a dataset (Leite, p. 90, Abstract, “the recommendations of the system get adjusted to the characteristics of the target dataset. We show that this new strategy leads to improved results of the active testing approach”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSE CHEN COULSON whose telephone number is (571)272-4716. The examiner can normally be reached Monday-Friday 8:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSE C COULSON/
Examiner, Art Unit 2122
/MICHAEL H HOANG/Examiner, Art Unit 2122