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 .
DETAILED ACTION
Claims 1-20 are presented for examination.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the limitations of “performing a data curation process…”, “obtaining…an assignment” and “performing a data curation process…” which is a process that can be perform in the mind. The limitations encompass a human mind carrying out the function through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper, therefore, it falls within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. In particular, the claim recite the following additional elements of “a data manager” in performing the method of curating data. The recitation of a “data manager” which is described as any data processing system including hardware and/or software [specification, paragraph 35] in performing the processes are recited so generically that they represent not more than mere instructions to apply the exception using or on a generic computing system and performing generic computer function, MPEP 2106.05(f). Accordingly, the additional elements do not integrate the recited judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea, the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
The claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation a “data manager” so generically that they represent not more than mere instructions to apply the exception using or on a generic computing system and performing generic computer function. Accordingly, the claim is not patent eligible under 35 USC 101.
As to claim 2, obtaining of historical data curation resource performance data amounts to data gathering which is insignificant extra solution activity and the performing of an analysis of the historical data curation resource performance data is directed to a mental process.
As to claims 3-6, the limitations recited in the claim further defines the used of the data curation resource profile, information within the profile and types of historical data curation data thus are considered to merely apply the abstract idea which is neither a practical application under prong 2, or amount to significantly more under step 2B.
As to claim 7, the claim further recites “rank ordering the data curation resources…” which is directed to a mental process.
As to claim 8, obtaining of a data curation resource profile and various data amounts to data gathering which is insignificant extra solution activity and defining the rank ordering is directed to a mental process.
As to claims 9-10, the claims further defines the information or data within the obtained data curation resource profile thus are considered to merely apply the abstract idea which is neither a practical application under prong 2, or amount to significantly more under step 2B.
As to claim 11, the limitation “providing a …service using the curated portion of the data” recites further mental process, and the additional element of “computer-implemented” is merely the use of a computer/instructions running on the computer to carry out the judicial exception, which is neither a practical application under prong 2, nor an inventive concept under step 2B.
As to claims 12-16, these claims are rejected for the same rationale as claims 1-5 above. Additionally, the claims recite the additional element of “non-transitory storage medium having instructions stored therein, which when executed by one or more hardware processor to perform operations” which are merely generic computer components, thus is neither a practical application under prong 2, or amount to significantly more under step 2B.
As to claims 17-20, these claims are rejected for the same rationale as claims 1-4 above. Additionally, the claims recite the additional element of “processor” and “memory” which are merely generic computer components, thus is neither a practical application under prong 2, or amount to significantly more under step 2B.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Data Curation at Scale: The Data Tamer System” to Stonebraker et al. (herafter Stonebraker).
As to claim 1, Stonebraker teaches the invention as claimed including a method for curating data by a data manager, comprising:
performing a data curation matching process to identify a match between a portion of data and a data curation resource [Data Tamer Exchange (DTX) tool helping match a task to set of Domain Experts (DEs), the task being performed on various phases in the curation of particular portions of data/records, sections 4.1-4.2 directing to phases of data curation such as the performance of attribute identification and entity consolidation; 4.3 Human Interfaces directing to the involvement of DEs in the phases of data curation], the match being identified based on a likelihood that the data curation resource will curate the portion of the data more efficiently than other data curation resources [matching a task or task’s domain to set of DEs based on a vector of confidence-based expertise ratings in each of a set of domains, confidence metrics used to measure the quality of a responses, subsection 4.3.2];
obtaining, based at least in part on the match, an assignment for data curation; and
performing a data curation process based on the assignment to obtain a curated portion of data [Data Tamer administrator (DTA) decide which and how many DEs to respond to a task for each expert class, subsection 4.3.2 Crowd Sourcing Mode, right column, “Expert Classes”].
As to claim 2, Stonebraker teaches the invention as claimed including further comprising: prior to performing the data curation matching process: obtaining historical data curation resource performance data, and performing an analysis of the historical data curation resource performance data to obtain a data curation resource profile [statistics about each expert class in a task domain including number of DEs, cost per DE response and expertise ratings of the DEs, subsection 4.3.2 Crowd Sourcing Mode, right column, “Expert Classes” (Note: statistics inherently implied analysis of data from the past)].
As to claim 3, Stonebraker teaches the invention as claimed including wherein the data curation resource profile is used in the data curation matching process to identify the likelihood [DE expertise ratings and cost being a factor in matching a task to DE(s), subsection 4.3.2 Crowd Sourcing Mode].
As to claim 4, Stonebraker teaches the invention as claimed including wherein the data curation resource profile specifies levels of proficiency of one of the data curation resources to curate different types of data [matching a task or task’s domain to set of DEs based on a vector of confidence-based expertise ratings in each of a set of domains (i.e. domain expertise), confidence metrics used to measure the quality of a responses, subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses” and “Expert Classes”].
As to claim 5, Stonebraker teaches the invention as claimed including wherein the historical data curation resource performance data comprises durations of time required by one of the data curation resources to curate instances of the different types of the data [Timely DE response are factor in to expertise rating and are incentivize, subsection 4.3.2, “Motivation of Helpful and Timely DE Responses” and “Expert Classes”].
As to claim 6, Stonebraker teaches the invention as claimed including wherein the historical data curation resource performance data comprises error rates for the different types of the data in curated instances of the different types of the data as curated by the one of the data curation resources [quality DE responses are reviewed and contributes to the confidence-based metric/expertise ratings as well as presented statistics, “…the probability that the response is correct” or not wrong, subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses”].
As to claim 7, Stonebraker teaches the invention as claimed including wherein performing the data curation matching process comprises: rank ordering the data curation resources to curate the portion of the data to identify the data curation resource [presenting statistics about each expert class in the task domain, including range of expertise rating of DEs within the class along with cost based on expertise rating to assist in selection of DE(s), subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses”, “Expert Classes”, “Economic Incentives for Good Citizenship” and “Dynamic Pricing to Manage Workload”].
As to claim 8, Stonebraker teaches the invention as claimed including wherein rank ordering the data curation resources comprises: obtaining a data curation resource profile for each of the data curation resources; obtaining a data type for the portion of the data; obtaining a fitness value for each of the data curation resources using a corresponding data curation profile and the data type; and defining the rank ordering using the fitness value for each of the data curation resources [matching a task or task’s domain to set of DEs based on a vector of confidence-based expertise ratings in each of a set of domains (i.e. domain expertise) associated with the respective DE, confidence metrics used to measure the quality of a responses, subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses” and “Expert Classes”].
As to claim 9, Stonebraker teaches the invention as claimed including wherein the data curation resource profile for each of the data curation resources comprises: a level of proficiency for curating a type of the portion of the data [DE having associated expertise rating value between 0 and 1 in respective domain, subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses” and “Expert Classes”].
As to claim 10, Stonebraker teaches the invention as claimed including wherein the level of proficiency is based on at least one selected from a group consisting of: a data curation rate for the type of the portion of the data; an error rate for the type of the portion of the data; and a quality score for the type of the portion of the data [quality of DE responses are reviewed and contributes to the confidence-based metric/expertise ratings as well as presented statistics, “…the probability that the response is correct” or not wrong, subsection 4.3.2, “Confidence-Based Metrics for DEs and Responses”] (Note: the examiner is treating the Markush-type language as an open-ended interpretation such as that of “comprising” (see MPEP 2117) giving the many characteristics that contributes to the level of proficiency as being disclosed by applicant [specification, paragraph [0062]]).
As to claim 11, Stonebraker teaches the invention as claimed including further comprising: providing a computer-implemented service using the curated portion of the data [deduplicated data, attributes identification produced by Data Tamer that are used in/for various applications, section 2 Example Applications; Section 5 Experimental Validation].
As to claims 12-16, Stonebraker teaches the method for curating data as recited in claims 1-5, therefore Stonebraker teaches the non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform the method.
As to claims 17-20, Stonebraker teaches the method for curating data as recited in claims 1-4, therefore Stonebraker teaches the data processing system for performing the method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QING YUAN WU whose telephone number is (571)272-3776. The examiner can normally be reached M-F 9AM-6PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached on 571-272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QING YUAN WU/Primary Examiner, Art Unit 2199