DETAILED OFFICE ACTION
Status of the Application
This Office Action is in response to Application Serial 19/227,733. Claims 1-20 are examined 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 . 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.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration by the examiner.
Claim Interpretation- 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a data acquisition module”, “a data clustering module”, “a provenance module”, “a metadata augmentation module”, “a data scoring module”, “a marketplace creator exchange module” in claims 1-20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea ) without significantly more.
Step 1 :
Claims 1-14 are machine. Claims 15-20 are process. The claims 1-20 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more that the abstract idea because the additional computer elements, which are recited at a high level of generality, provide a computer that is performing computer functions that do not add meaningful limits to practicing the abstract idea. (Step 1: YES)
Step 2A – Prong One: The claims recite an abstract idea
The claims (claim 1 and similarly claim 15) recite, “…capture and process user data from transactions associated with a user to generate a user data footprint… adapted to create reference clusters from the captured user data; … adapted to identify, confirm, and rate provenance characteristics of the user data in the created reference clusters; … adapted to generate an augmented user data footprint through supplemental user data, including watermark and authorization data on a territory basis; … adapted to process the augmented user data footprint, score the same based on industry-specific parameters and weightings, and generate one or more user data registries on an industry-by-industry basis; and … adapted to enable transacting of datasets from the one or more user data registries between users supplying data for said datasets and entities desiring to acquire rights in the same.”
The claims 1- 20 recite the abstract concept of collecting data from a person’s transactions and activities and supporting privacy controls, compliance checks, and compensation for the people supplying the data. The claim recitation of: capturing and processing user data from transactions associated with a user to generate a user data footprint; … identifying, confirming, and rating provenance characteristics of the user data are observations and evaluations that can be completed in the human mind and using pen and paper. The observation and evaluation of data and calibration of data are mental concepts. The claim (1 and similarly claim 15) recite abstract concepts that are grouped as mental concepts; thus, the claims are an abstract idea under the first prong of Step 2A.
Depending claims 2-14, 16-20 include all of the limitations of claims 1 and 15, and therefore likewise incorporate the above-described abstract idea. The limitations of depending claim 2-14, 16-20 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. This, depending claims 2-14, 16-20 are nonetheless directed towards fundamentally the same abstract idea as independent claim 1 and claim 15. (Step 2A, Prong One: Yes).
Step 2A – Prong Two
The judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “An automated integrated dataset marketplace system comprising: a data acquisition module”, “a data clustering module”, “a provenance module”, “a metadata augmentation module”; “a data scoring module”, “a marketplace creator exchange module”, in claim 1 (and similarly at claim 15).
The additional elements, listed above, are recited at a high level of generality (i.e., a computer that is performing computer functions) such that it amount to no more than mere instructions to apply the exception using generic computer components. (See Applicant’s specification [037]-[039] where there are general purpose computing components. See MPEP 2106.05(f)).
Dependent claims recite ““data acquisition module applies data encryption”; “at least one machine learning algorithm”, “unsupervised clustering”, “Natural Language (NLP)”, “blockchain-based verification”, “smart contracts, tokenized payments”. The additional elements are recited at a high level of generality (i.e., a computer that is performing computer functions) such that it amount to no more than mere instructions to apply the exception using generic computer components.
The data encryption and anonymization techniques to ensure user privacy and compliance with regulatory requirements. (See Applicant’s instant specification [011], [018]). This is MPEP 2106.05(f).
The machine learning, unsupervised clustering and/or Natural Language Processing (NLP) are used by the data clustering module to generate clusters. (See Applicant’s instant specification [012], [058]). This is MPEP 2106.05(f).
The block-chain-based verification is used by the provenance module to endure data integrity. (See Applicant’s instant specification [013], [019]). This is MPEP 2106.05(f). The “blockchain-based verification” is broadly recited. Applicant is encouraged to review instant specification [057].
The smart contracts and tokenized payments are described as mechanisms. (See claim 12, instant specification [010]). This is MPEP 2106.05(f).
Regarding the recitation of “watermark”, examiner submits a marking on a page is something that can be completed with a rubber stamp or pen and paper. As recited the watermark if an output and is not considered an additional element.
Regarding the recitation of Privacy-Inclusive Data Access (PIDA) scoring, it is a method.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any ither technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. (Step 2A, Prong Two: No)
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements, listed in Step 2A prong two analysis, amounts to no more than mere instructions to apply the exception using generic computer components that does not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the speciation recites mere generic computer components, as discussed above that are used to make an evaluation and observation of data, which is a mental concept. Specifically, MPEP 2106.05(f) recites that the following limitation are not significantly more.
Adding the words “apply it” (or an equivalent ) with the judicial exception, or mere instructions to implement, an abstract idea on a computer, (e.g., a limitation indicting that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct at 235. (see MPEP 2106.05 (f)).
Furthermore, the additional elements in the claims other than the abstract idea per se, including the elements listed and discussed above, are receiving and transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), do not amount to significantly more than the abstract idea. See MPEP 2106.05 (d) – Receiving or transmitting data over a network.
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract ideas such that the claim(s) amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claim(s) are rejected under 35 U.S.C. 1010 as being directed to non-statutory subject matter.
Dependent claims 2-14 further narrow the abstract idea of independent claim 1.The claims do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract ides such that the claims amount to significantly more than the abstract idea itself.
Dependent claims 16-20 further narrow the abstract idea of independent claim 15. The claims do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract ides such that the claims amount to significantly more than the abstract idea itself.
Claims 1- 20 are rejected under 35 U.S.C. 101.
Distinguishable Over the Prior Art
Examiner analyzed claim 1 (and similarly claim 15) in view of the prior art on record and finds not all of the claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine references with a reasonable expectation of success.
Grossman (US 2021/0,333,995 A1) discloses a cybersecurity, fraud, and risk systems. (Grossman Figure 2 and the associated text). Specifically, Grossman discloses data blocks that are comprised of at least one of data storage blocks and data provenance blocks. (Grossman [005]). Grossman describes a transaction process, using an example of a workflow for a risk model that is used to produce a score about the user's overall risk; a separate transaction risk model is used to produce a score about the risk of a particular transaction; both of the scores, plus additional inputs, are used as input to a third risk model that produces a third score that integrates the scores from the two models; and, the third score is used as input to a fourth model, that rescales the score and applies certain business rules, such as ignoring small dollar transactions that may unnecessarily inconvenience the user compared to the potential reduction in risk to the organization. (Grossman [018]). Grossman discloses layer, layer 2, includes a Data and Model Provenance Blocks (hereinafter “DMPB”) module 103a. In an embodiment, Layer 2 includes a centralized ledger 103b. The management module 103 is adapted and configured to store and/or manage one or more of the following: immutable cryptographically signed logs, claims, and other assertions about user access, data access, data provenance, data processing and related events. (Grossman [020]). In Grossman, once a user registers with the system 100, the user is assigned a random string of letters and numbers (i.e., the block chain user ID) that is associated with all user related data in data storage blocks 101 and all provenance related data in data provenance blocks 102., (Grossman [021]). Grossman discloses the data storage blocks 101 and data provenance blocks 102 may be immutable and cannot be changed once they are written. Grossman [021]. Grossman discloses monitoring consumer purchases. Grossman discloses chases be used to score fraud and risk models.
Hamalainen (2020, Minimal learning machine: Theoretical results and clustering-based reference point selection) discloses selecting reference points ( reference clusters) for the Machine Learning Model (MLM) generalization capability. Several clustering-based methods for reference point selection in regression scenarios are then proposed and analyzed. Hamalainen [abstract].
Venkadasubbiah (2022, Big Data Analytics and Computational Intelligence for Cybersecurity. Studies in Big Data) discloses data clustering and data footprinting. (Venkadasubbiah [p.205). Venkadasubbiah discloses techniques in data mining are classification, clustering, and regression. Data mining helps to unearth facts about customers from databases, including purchasing behavior. (Venkadasubbiah [p.205]). Venkadasubbiah discloses anonymous and pseudonymous footprinting as a means of gathering information. (Venkadasubbiah [p.205, 206]).
Hurrah (2019, Dual watermarking framework for privacy protection and content authentication of multimedia, Future Generation Computer Systems) teaches water marking framework for privacy. Hurrah discloses develop new algorithms for strengthening the existing cybersecurity frameworks, ensure security, privacy, copyright protection and authentication of data. In this paper a new technique for copyright protection, data security and content authentication of multimedia images is presented. The copyright protection of the media is taken care of by embedding a robust watermark using an efficient inter-block coefficient differencing algorithm, Hurrah [abstract]
Dasupta (2020, "A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity) teaches multiple social media feeds that talk about cyber-attacks and their characteristics. The social media feeds provide a large repertoire of information that is available in natural language about various cyber-attacks, making it possible for researchers to mine Cyber-threat intelligence (CTI) from these sources. One of the ways [25] to extract CTI is to use automated, often machine learning-based, algorithms to mine knowledge from these open-source data feeds about cyber-attacks.
However, Grossman, Hamalainen, Venkadasubbiah, Hurrah, Dasupta, individually or in combination with the prior art of record does not explicitly teach the combination of claim limitations as recited in independent claim 1 (and similarly claim 15). Thus, claim 1 (and similarly claim 15) is found to be distinguishable over the prior art. Dependent claims 2-14 and 16-20 are distinguishable because they depend on claim 1. Dependent claims 16-20 are distinguishable because they dependent on claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pavani (Balancing Privacy and Precision: A Novel Approach to Customer Segmentation) discloses safeguarding sensitive customer purchase details, employing privacy-preserving techniques on top of clustering algorithms to understand customer and market trends and segment customers effectively.
Padmanabhan (US 20190236598 A1) discloses method for implementing machine learning models for smart contracts using distributed ledger technologies in cloud based computing environment. A consensus agreement is received from multiple participating nodes on the blockchain to apply a new machine learning model to smart contract transactions executed through the blockchain.
George (2024, Cyber Threats to Critical Infrastructure: Assessing Vulnerabilities Across Key Sectors) discuss cyberthreats by industry.
Chinatacunta (2025, Privacy-Preserving Customer Segmentation for Scalable Media Optimization in E-Commerce) uses machine learning to create a framework for customer segmentation and media optimization that protects privacy and considered consumer privacy act.
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/THEA LABOGIN/Examiner, Art Unit 3624
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