DETAILED ACTION
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/20/26 has been entered, in which Applicant amended claims 1, 6, 11, and 16, and cancelled claims 2, 7, 12, and 17. Claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 are pending in this application and have been rejected 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 .
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 USC 101 rejections of claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 regarding abstract ideas are still applied in light of Applicant’s amendments and explanations.
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, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for creation and expansion of high-quality data set collections for training of machine learning algorithms via crowdsourced curation. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 6, 8, and 10) and "system" (claims 1, 3, 5, 11, 13, 15-16, 18, and 20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
scoring each data entry within the data set, wherein the score for each data entry is calculated from a plurality of scoring metrics comprising a cyber-risk score generated from cybersecurity vulnerabilities of the data sources, a data provenance score based on a chain of custody of the data, and a data quality score based on completeness, uniqueness, timeliness, validity, accuracy, and consistency metrics;
summing all of the scores for each data entry within the data set, combining to form an overall reputation score for the data set;
flagging any erroneous data entries wherein a scoring metric produces an error, outlier, or unknown result that may not be resolved through a machine learning algorithm;
comparing the overall reputation score with a numerical threshold for reputability;
sending the flagged erroneous data entries and data sets not meeting the numerical threshold for reputability to a verification queue;
…
receiving the flagged erroneous data entries and data sets not meeting the threshold for reputability;
assigning the data in the verification queue to a data steward for human curation; and recalculating the plurality of scoring metrics for the curated data to generate a new overall reputation score.
Independent claim 6, 11, and 16 recite substantially similar claim language.
Dependent claims 3, 5, 8, 10, 13, 15, 18, and 20 recite the same or similar abstract idea(s) as independent claims 1, 6, 11, and 16 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to creation and expansion of high-quality data set collections for training of machine learning algorithms via crowdsourced curation and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior.
Step 2A - Prong 2: Claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A computing system for creation and expansion of high quality data set collections for training of machine learning algorithms via crowdsourced curation employing a cyber decision platform, the computing system comprising: one or more hardware processors configured for: / A system for creation and expansion of high quality data set collections for training of machine learning algorithms via crowdsourced curation employing a cyber decision platform, comprising one or more computers with executable instructions that, when executed, cause the system to: / Non-transitory, computer-readable storage media having computer-executable instruction embodied thereon that, when executed by one or more processors of a computing system employing a cyber decision platform for creation and expansion of high quality data set collections for training of machine learning algorithms via crowdsourced curation, cause the computing system to: " (claims 1, 6, 11, and 16) “storing the data sets that meet the threshold for reputability to a data store as a reputable data set collection,” (claims 1, 6, 11, and 16); “generating synthetic data based on the reputable data set collection using a neural network; merging the synthetic data with the reputable data set collection,” (claims 1, 6, 11, and 16); “wherein the synthetic data set is generated by a generative adversarial network,” (claims 3, 8, 13, and 18), however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "computing system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
"receiving a data set via ingestion application programming interfaces (APIs) from external data sources over a network… when the data set meets the numerical threshold for reputability, automatically uploading the data set to a network-accessible marketplace data store as a reputable data set collection " (claims 1, 6, 11, and 16) however the receiving of data from these various sources is merely insignificant extra-solution activity, e.g. data gathering, and/or merely an attempt at limiting the abstract idea to a particular field of use and thus fails to integrate the recited abstract idea into a practical application (e.g. MPEP 2106.0S(h): "Examiners should keep in mind that this consideration overlaps with other considerations, particularly insignificant extra-solution activity (see MPEP § 2106.05{g)). For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017} (limiting use of abstract idea to use with XML tags).");
Step 2B: Claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of creation and expansion of high-quality data set collections for training of machine learning algorithms via crowdsourced curation via a "computer system", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to creation and expansion of high-quality data set collections for training of machine learning algorithms via crowdsourced curation.
Claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Subject Matter Overcoming Prior Art
Claims 1, 3, 5-6, 8, 10-11, 13, 15-16, 18, and 20 are found to overcome the prior art rejection. The claims would be found to be allowable over the prior art of record if they overcame the 35 USC 101 rejections.
Reasons for Overcoming the Prior Art
The following is a statement of reasons for the indication of overcoming the prior art rejection:
The following limitations of claim 1,
…
scoring each data entry within the data set, wherein the score for each data entry is calculated from a plurality of scoring metrics comprising a cyber-risk score generated from cybersecurity vulnerabilities of the data sources, a data provenance score based on a chain of custody of the data, and a data quality score based on completeness, uniqueness, timeliness, validity, accuracy, and consistency metrics;
summing all of the scores for each data entry within the data set, combining to form an overall reputation score for the data set;
flagging any erroneous data entries wherein a scoring metric produces an error, outlier, or unknown result that may not be resolved through a machine learning algorithm;
comparing the overall reputation score with a numerical threshold for reputability;
sending the flagged erroneous data entries and data sets not meeting the numerical threshold for reputability to a verification queue;
when the data set meets the numerical threshold for reputability, automatically uploading the data set to a network-accessible marketplace data store as a reputable data set collection;
generating synthetic data based on the reputable data set collection using a neural network;
merging the synthetic data with the reputable data set collection;
receiving the flagged erroneous data entries and data sets not meeting the threshold for reputability;
assigning the data in the verification queue to a data steward for human curation; and
recalculating the plurality of scoring metrics for the curated data to generate a new overall reputation score.
in combination with the remainder of the claim limitations are neither taught nor suggested, singularly or in combination, by the prior art of record. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings. Independent claims 6, 11, and 16, and dependent claims 3, 5, 8, 10, 13, 15, 18, and 20 likewise overcome the art of record.
The closest prior art of record is described as follows:
Midboe (U.S. Patent Application Publication Number 2016/0350674) - The abstract provides for the following: Approaches presented herein enable intelligent service request classification and assignment learning. More specifically, a request comprising a free form text or spoken description is received from a user. The request description is parsed and classified by a regression-based classifier. The regression-based classifier classifies based on, for example: the description itself; the requestor's history of requests, and/or supplemental demographics about a requestor. Optionally, a user may verify the classification or select from a plurality of returned classifications. A service provider or administrator confirms that a classification is correct. If not, the incorrectly classified request is queued. If so, the correctly classified request is added to a set of training data to be used in classifying future requests.
Williams, JR. et al. (U.S. Patent Application Publication Number 2015/0254555) - The abstract provides for the following: Embodiments are directed towards classifying data using machine learning that may be incrementally refined based on expert input. Data provided to a deep learning model that may be trained based on a plurality of classifiers and sets of training data and/or testing data. If the number of classification errors exceeds a defined threshold classifiers may be modified based on data corresponding to observed classification errors. A fast learning model may be trained based on the modified classifiers, the data, and the data corresponding to the observed classification errors. And, another confidence value may be generated and associated with the classification of the data by the fast learning model. Report information may be generated based on a comparison result of the confidence value associated with the fast learning model and the confidence value associated with the deep learning model.
Baumard (U.S. Patent Application Publication Number 2016/0078365) - The abstract provides for the following: Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions.
Colin Puri et al. “Analyzing and Predicting Security Event Anomalies” The abstract provides for the following: This paper presents a novel and unique live operational and situational awareness implementation bringing big data architectures, graph analytics, streaming analytics, and interactive visualizations to a security use case with data from a large Global 500 company. We present the data acceleration patterns utilized, the employed analytics framework and its complexities, and finally demonstrate the creation of rich interactive visualizations that bring the story of the data acceleration pipeline and analytics to life. We deploy a novel solution to learn typical network agent behaviors and extract the degree to which a network event is anomalous for automatic anomaly rule learning to provide additional context to security alerts. We implement and evaluate the analytics over a data acceleration framework that performs the analysis and model creation at scale in a distributed parallel manner. Additionally, we talk about the acceleration architecture considerations and demonstrate how we complete the analytics story with rich interactive visualizations designed for the security and business analyst alike. This paper concludes with evaluations and lessons learned.
Leo Dirac (CA Patent Application Publication Number CA 2953817 A1) - The abstract provides for the following: At a machine learning service, a set of candidate variables that can be used to train a model is identified, including at least one processed variable produced by a feature processing transformation. A cost estimate indicative of an effect of implementing the feature processing transformation on a performance metric associated with a prediction goal of the model is determined. Based at least in part on the cost estimate, a feature processing proposal that excludes the feature processing transformation is implemented.
Response to Arguments
Applicant’s arguments filed 5/20/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are eligible under 35 USC 101. (See Applicant’s Remarks, 5/20/2026, pgs. 10-14). Examiner respectfully disagrees. As noted in the 35 USC 101 analysis presented above, the claims recite an abstract concept that is encapsulated by decision making analogous to a method of organizing human activity or mathematical concepts. Examiner notes that each of the limitations that encapsulate the abstract concepts are recited in the above 35 USC 101. Additionally, the claims do not recite a practical application of the abstract concepts in that there is no specific use or application of the method steps other than to make conclusory determinations and provide for direction for either a person or machine to follow at some future time or to make calculations that are mathematical operations. The claims do not recite any particular use for these determinations and directions that improve upon the underlying computer technology (in this instance the computer software, processor, and memory). Instead, Examiner asserts that the additional elements in the claim language are only used as implementation of the abstract concepts utilizing technology. The concepts described in the limitations when taken both as a whole and individually are not meaningfully different than those found by the courts to be abstract ideas and are similarly considered to be certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions or to make calculations that are mathematical operations. The steps are then encapsulated into a particular technological environment by executing these steps upon a computer processor and utilizing features such as a computer interface or sending and receiving data over a network or displaying information via a computerized graphical user interface. However, sending and receiving of information over a network and execution of algorithms on a computer are utilized only to facilitate the abstract concepts (i.e. selecting data on an interface, publishing/displaying information, etc.). In this way Applicant’s claims are distinguished from the Flook and Dier. Applicant claims that the pending claims are meaningfully integrated into technology, however, as noted above, the claims merely utilize the technology to facilitate the overarching abstract concepts with no specific improvement to the technology. Instead, the improvements are directed towards the abstract idea, which is insufficient to rise to the level of significantly more. Additionally, the claims do not provide for a purpose of the abstract idea beyond that of a data output that is to be then used to accomplished some unclaimed task. Without a defined purpose or process claimed Examiner cannot find a practical application of the claim language. As such, Examiner asserts that the implementation of the abstract concepts recited by the claims utilize computer technology in a way that is considered to be generally linking the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Accordingly, Examiner does not find that the claims recite a practical application of the abstract concepts recited by the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. 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, Jerry O'Connor can be reached on (571) 272-6787.
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/M. H. D./
Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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