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 the Information Disclosure Statement (IDS) filed on April 22, 2026, in which claims 1-10 are presented for further examination.
Information Disclosure Statement
The information disclosure statement filed on April 22, 2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file, but the information referred to therein has been considered as to the merits.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Madhavan et al., (hereinafter “Madhavan”) US 20120011115 in view of Ricketts (hereinafter “Ricketts”) US 20070112697.
As to claim 1, Madhavan discloses
receive a plurality of datasets from the database, at least one of the plurality of datasets including incomplete data (pg. 1, [0006], "receiving a collection of tables, each table including a plurality of rows, each row including a plurality of cells" and [0026], "A particular table can have incomplete information");
process each of the plurality of datasets to classify each dataset based on a classification component (see [0028], using the identified subject columns to classify the table according to classes from a class hierarchy");
determine a normalized score for each dataset based on at least one value in each dataset (D2, pg. 2, [0029], "The computation creates standard weights applicable to all cases, then normalized weights for every case where data is missing. Normalization means, on a case-by- case basis, the weights for missing values are set to zero while the weights for non-missing values are increased");
determine a total score for the plurality of datasets by aggregating the classification component scores, the total score indicating accuracy and reliability of a model corresponding to the plurality of datasets (see par [0067-0070], "overall scores were computed");;
transmit the total score to a recipient (see par [0023], "The search engine can transmit the search results 128 through the network to the client device 104").
Madhavan further discloses the claimed “processing each dataset using a machine learning process executed by the processor and having a plurality of filter sizes successively smaller filters each sized for a respective business size, the machine learning process generating a respective error distribution curve for each filter size, the error distribution curve tempering the classification component score due to incomplete or missing data” (see [0035]-[0037], a machine learning technique is used to identify the subject column. In particular, support vector machines (SVM) can be used to learn or train a classifier for subject columns in tables. SVMs are a set of related supervised learning methods used for classification and regression. For example, for particular training data composed of a set of training examples where each example is labeled as belonging to one of two categories, an SVM training algorithm builds a model that predicts which category a new example falls into)
Madhavan does not explicitly disclose determine a classification component score for each dataset.
Meanwhile, Ricketts determine a classification component score for each dataset (D2, pg. 2, [0030], "Score and classify each case (block 112) by [a] multiplying each independent variable by its standard or compensating weight, [b] summing the products into a score for each dimension, and [c] determining the zones (combination of scores) that distinguish categories. To the degree that the weights successfully classify cases, cases known to be members of a particular category will have similar scores, and those scores will be different from scores for cases in other categories.");
determine whether an incomplete data set exists among the plurality of datasets (see [0006], "executing an iterative process that finds a specific combination of compensation weights that best classify the entity cases in terms of distinct scores; and applying a resulting model, which is determined by the specific combination of compensation weights, to classify other entity cases for which the classifications are unknown").
Therefore, it would have been obvious to one having skill in the art before the claimed invention to modify the system of Madhavan to determine a classification component score for each dataset, in order to simply and accurately classify the small collection of high-value entities provided with the missing data.
As to claim 2, the combination of Madhavan and Ricketts discloses the invention as claimed. In addition, Ricketts discloses the claimed wherein the code causes the processor to determine whether a classification component comprises more than one dataset (see par. [0030], score and classify each case (block 112) by [a] multiplying each independent variable by its standard or compensating weight, [b] summing the products into a score for each dimension, and [c] determining the zones (combination of scores) that distinguish categories. To the degree that the weights successfully classify cases, cases known to be members of a particular category will have similar scores, and those scores will be different from scores for cases in other categories. Thus, if the scores are plotted on a chart, each category is comprised of points that fall within a relatively distinct zone from other categories. The better the classification, the more distinct the separation of cases into zones. Weights with the largest absolute values then identify the independent variables that contribute most to classification. This process for establishing weights is known as calibration).
As to claim 3, the combination of Madhavan and Ricketts discloses the invention as claimed. In addition, Ricketts discloses the claimed wherein the code causes the processor to determine the classification component score for each of the datasets as the normalized score for each dataset if the classification component does not comprise more than one dataset (see [0006], [0029]-[0031]).
As to claim 4, the combination of Madhavan and Ricketts discloses the invention as claimed. In addition, Ricketts discloses the claimed wherein the code causes the processor to assign a weighted data value to each dataset of the classification component if the classification component comprises more than one dataset (see [0029]-[0031], compute weights for each independent variable such that the independent variables correctly classify as many cases as possible).
As to claim 5, the combination of Madhavan and Ricketts discloses the invention as claimed. In addition, Ricketts discloses the claimed wherein the code causes the processor to determine the classification component score for each weighted dataset by applying the weighted data value to the normalized score for each weighted dataset (see [0053]-[0063]).
Claims 6-10, claims 6-10 are method for performing the system of claims 1-5. They are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170364703 (involved in identifying first version of a dataset associated with a first subset of atomized data points. A subset of data varying from the first version of the dataset is identified. The subset of data is converted to a second subset of atomized data points providing a specific format similar to the first subset. Second version of the dataset is generated to include the first subset of atomized data points and the second subset of atomized data points. The first and second subsets of atomized data points are stored as an atomized dataset in repositories.)
US 20170364570 (involved in receiving data representing a dataset with a data format into a dataset ingestion controller to form a collaborative dataset, where the dataset is associated with an identifier. A subset of data of the dataset is interpreted against data classifications at an inference engine to derive an inferred attribute for the subset of data. The subset of the data is associated with annotative data identifying the inferred attribute. The dataset is converted from the data format at a format converter to form an atomized dataset with a specific format.)
US 20170364569 (involved in receiving data representing a query (131a,131b) into a collaborative dataset consolidation system (110), where the dataset is associated with an identifier. The datasets are identified relevant to the query. The datasets are disposed in disparate data repositories. A level of authorization associated with the identifier is determined to access each of the datasets).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 May 28, 2026
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