DETAILED ACTION
Acknowledgements
The amendment filed 4/24/2026 is acknowledged.
Claims 1-3, 8-9, 11-14, 16, 18-19, 21, and 23-24 are pending.
Claims 1-3, 8-9, 11-14, 16, 18-19, 21, and 23-24 have been examined.
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/Arguments
Regarding the rejection of the claims under 35 USC 101, applicant states that MPEP 2111.01 (II) rejects examiner’s reasoning that the claims are broad enough to read on the embodiment described in the specification. Examiner notes, however, that applicant’s argument mischaracterizes the guidance provided in MPEP 2111.01 (II). The MPEP section states that claim may not be limited in scope to a particular embodiment when the claim scope is broader than that embodiment. It does not state that when the claim scope of broader than a particular embodiment described in the specification, the claim scope excludes that embodiment. Applicant is arguing that because the claims have been broadened and no longer recite an “estimated value”, the claims should be interpreted as excluding the embodiment in the specification in which the claimed invention is used to predict an estimated value. This is an incorrect application of MPEP 2111.01 (II). While broader claims are not limited to a particular embodiment in the specification, and may read on additional embodiments beyond that described in the specification, they do not exclude the embodiment described in the specification, and thus still read on that embodiment. Therefore, the claims still fall within the "certain methods of organizing human activity" grouping of abstract ideas.
Applicant cites ex parte Hannun to state that obtaining predicted character probabilities from a trained neural network resulting in predicted character probability outputs did not recite organizing human activity. Applicant further states that the specific technology of the invention is not a proper basis for distinguishing ex parte Hannun because the Federal Circuit routinely compares the claims at issue with claims found eligible in past cases. Applicant then states that in ex parte Hannun, the Board found that while some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claim, and therefore in the present claims the limitation of encrypting access using homomorphic encryption is also not a mathematical concept.
Examiner notes that comparing the claims at issue with claims found be eligible in a case involves comparing the technology recited in the claims as well, because that is an important factor in the reasoning for why a particular set of claims in a case were found to be eligible. Here, the claims of ex parte Hannun, dealt with analyzing an input audio signal to generate spectrogram frames for an audio file, and using a trained neural network to obtain probabilities for determining the words or letters spoken in the audio file to generate a transcription of the audio file. The board considered these features when determining that the claims were eligible, as it found “the claims here are directed to a specific implementation including the steps of normalizing an input file, generating a jitter set of audio files, generating a set of spectrogram frames, obtaining predicted character probabilities from a trained neural network and decoding a transcription of the input audio using the predicted character probability outputs.” The claims were not found patent eligible merely because they recited a step of “obtaining predicted character probabilities outputs from the trained neural network”. Thus, features present in the claims of the ex parte Hannun case, and which are not present in the present application, were used to find the claims patent eligible. The present claims, on the other hand, simply make a prediction regarding a characteristic of data (e.g., the value of the data), determine the accuracy of the prediction, and store and control access to it. Thus the nature and facts of the present claims differ significantly from that of the invention of ex parte Hannun, and because the patent eligibility analysis depends on these facts, the analysis in ex parte Hannun is not applicable to the present claims. The present claims recite predicting a characteristic of data (e.g., the value of the data) and determining the accuracy of the prediction in a commercial context where the prediction is for determining the value of a product to be bought or sold. Thus, the present claims remain directed to certain methods of organizing human activity.
Regarding applicant’s argument that the limitation of encrypting access using homomorphic encryption is also not a mathematical concept because the Board found that while some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claim, examiner notes that the claims do continue to recite a mathematical concept, at least because the claims recite "encrypting . . .using homomorphic encryption," which is a mathematical concept. Also, although there are different ways of performing homomorphic encryption, all of these ways are mathematical calculations. The document titled "Homomorphic Encryption Use Cases," originally cited by applicant in the interview of 9/9/2025 and also attached to the Office action of 1/28/2026, cites a 2009 dissertation by Craig Gentry which provides the first an algorithm for performing full homomorphic encryption (See section entitled “Future Implementations of Homomorphic Encryption”). This dissertation details a manner of performing homomorphic encryption, and demonstrates that homomorphic encryption is a series of mathematical calculations. (See Craig Gentry, “A Fully Homomorphic Encryption Scheme”, Stanford University, September 2009).
Regarding Step 2A Prong Two, applicant states that the examiner identifies the following software steps as additional elements:
"training a machine learning prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of machine learning trained data predictors;
determining an accuracy of the machine learning trained data predictors;
determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the machine learning trained data predictors;
storing the accuracy profile and the accuracy of the machine learning trained data predictors with the data stream on the blockchain on the secure platform; and
controlling access on the secure platform to the data stream, the accuracy profile, the machine learning trained data predictors, and the accuracy of the machine learning trained data predictors on the blockchain by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens implemented on the blockchain."
Examiner notes that this is incorrect. The Office action does not identify these features as additional elements. Rather, the only additional elements are the use of a local computer, a secure platform on a central server computer, a secure communication link from the local computer to the secure platform, a machine learning prediction function model and machine learning trained data, a blockchain, a computer system comprising one or more computer processors and one or more non-transitory computer-readable storage media, and a computer program product comprising program instructions on a computer-readable storage medium to perform the steps. These additional elements do not provide a practical application or significantly more than the abstract idea because these additional elements only involve using a computer as a tool to automate and/or implement the abstract idea. Applicant’s argument is moot because it is based on the incorrect premise that the quoted steps are identified by the examiner as additional elements.
Applicant further states that the claims recite a specific technique to solve a specific problem in the technical field of transmitting, storing, and controlling access to sensitive data in data streams, and the claims recite specific limitations that achieve an improved technological result of providing a secure platform for determining the accuracy of a prediction model trained on the data stream and controlling access to the accuracy of the data stream. Applicant states that the claims are similar to the example from ex parte Desjardins of improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams, and the use of the machine learning model in ex parte Hannun was found to be an improved technological result.
Examiner notes, however, that the functionality recited in the claims involves the steps of “transmitting a data stream . . .,” “encrypting access to the data stream . . . using homomorphic encryption,” “storing the homomorphic encrypted data stream . . .,” “training a . . . prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of . . . trained data predictors,” “determining an accuracy of the . . . trained data predictors,” “determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the . . . trained data predictors,” “storing the accuracy profile and the accuracy of the . . . trained data predictors with the data stream . . .,” and “controlling access . . . to the data stream, the accuracy profile, the . . . trained data predictors, and the accuracy of the . . . trained data predictors . . . by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens . . . .” Each of these steps is part of the abstract idea of transferring, storing, and protecting a set of information, predicting an estimated value of the information, determining an accuracy of the prediction, and storing and controlling access to the estimated value, accuracy, and the information. These steps do not provide a technological solution or a solution to a technological problems, because the steps describe a process for determining a prediction regarding a set of information, such as the value of the information, and an accuracy of the prediction, as well as storing and protecting the set of information and predictions from unauthorized access, which is a manner of managing data as part of a commercial interaction rather than a technological process. The additional elements recited beyond the abstract idea, such as the use of a local computer, a secure platform on a central server computer, a secure communication link from the local computer to the secure platform, a machine learning model, a blockchain, a computer system comprising one or more computer processors and one or more non-transitory computer-readable storage media, and a computer program product comprising program instructions on a computer-readable storage medium, do not provide significantly more than the abstract idea because these additional elements only involve using a computer as a tool to automate and/or implement the abstract idea. Further, regarding the citation of ex parte Desjardins, examiner notes that the claims in that case did not merely recite the use of a machine learning model, but recited features that improved the technology of machine learning. The present claims recite no such features. Similarly, in ex parte Hannun, the claim was not found patent eligible merely because it used a machine learning model, but because the machine learning model was used to improve a technical invention related to speech recognition. On the other hand, the present claims use machine learning as part of a commercial interaction rather than a technological process.
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, 8-9, 11-14, 16, 18-19, 21, and 23-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-3, and 8-9 are directed to a method, claims 11-14 and 21 are directed to a system comprising one or more processors and one or more non-transitory computer-readable storage media, and claims 16, 18-19 and 23-24 are directed to a computer program product comprising a computer-readable storage medium, which according to the specification, excludes transitory signals (See PGPub of specification ¶ 63). Therefore, these claims fall within the four statutory categories of invention.
The claims recite transferring, storing, and protecting a set of information, predicting an estimated value of the information, determining an accuracy of the prediction, and storing and controlling access to the estimated value, accuracy, and the information, which is an abstract idea. Specifically, the claims recite “transmitting a data stream . . .,” “encrypting access to the data stream . . . using homomorphic encryption,” “storing the homomorphic encrypted data stream . . .,” “training a . . . prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of . . . trained data predictors,” “determining an accuracy of the . . . trained data predictors,” “determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the . . . trained data predictors,” “storing the accuracy profile and the accuracy of the . . . trained data predictors with the data stream . . .,” and “controlling access . . . to the data stream, the accuracy profile, the . . . trained data predictors, and the accuracy of the . . . trained data predictors . . . by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens . . . ,” which is grouped within the “certain methods of organizing human activity” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)) because it describes a process for determining a prediction regarding a set of information, such as the value of the information, and an accuracy of the prediction, as well as storing and protecting the set of information and predictions from unauthorized access, which is a commercial or legal interaction. Additionally, the limitation of “encrypting access to a data stream . . . using homomorphic encryption” only involves performing a mathematical calculation or inputting values into mathematical functions, and thus falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional elements of the claims such as the use of a local computer, a secure platform on a central server computer, a secure communication link from the local computer to the secure platform, a machine learning prediction function model and machine learning trained data, a blockchain, a computer system comprising one or more computer processors and one or more non-transitory computer-readable storage media, and a computer program product comprising program instructions on a computer-readable storage medium, merely use a computer as a tool to perform an abstract idea. Specifically, these additional elements perform the steps or functions of “transmitting a data stream . . .,” “encrypting access to the data stream . . . using homomorphic encryption,” “storing the homomorphic encrypted data stream . . .,” “training a . . . prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of . . . trained data predictors,” “determining an accuracy of the . . . trained data predictors,” “determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the . . . trained data predictors,” “storing the accuracy profile and the accuracy of the . . . trained data predictors with the data stream . . .,” and “controlling access . . . to the data stream, the accuracy profile, the . . . trained data predictors, and the accuracy of the . . . trained data predictors . . . by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens . . . .” The use of a processor/computer as a tool to implement the abstract idea does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional elements of using a local computer, a secure platform on a central server computer, a secure communication link from the local computer to the secure platform, a machine learning prediction function model and machine learning trained data, a blockchain, a computer system comprising one or more computer processors and one or more non-transitory computer-readable storage media, and a computer program product comprising program instructions on a computer-readable storage medium to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea transferring, storing, and protecting a set of information, predicting an estimated value of the information, determining an accuracy of the prediction, and storing and controlling access to the estimated value, accuracy, and the information. As discussed above, taking the claim elements separately, these additional elements perform the steps or functions of “transmitting a data stream . . .,” “encrypting access to the data stream . . . using homomorphic encryption,” “storing the homomorphic encrypted data stream . . .,” “training a . . . prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of . . . trained data predictors,” “determining an accuracy of the . . . trained data predictors,” “determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the . . . trained data predictors,” “storing the accuracy profile and the accuracy of the . . . trained data predictors with the data stream . . .,” and “controlling access . . . to the data stream, the accuracy profile, the . . . trained data predictors, and the accuracy of the . . . trained data predictors . . . by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens . . . .” These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of transferring, storing, and protecting a set of information, predicting an estimated value of the information, determining an accuracy of the prediction, and storing and controlling access to the estimated value, accuracy, and the information. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05 (f) & (h)). Therefore, the claim is not patent eligible.
Dependent claims 2-3, 8-9, 12-14, 18-19 and 21, 23-24 further describe the abstract idea of transferring, storing, and protecting a set of information, predicting an estimated value of the information, determining an accuracy of the prediction, and storing and controlling access to the estimated value, accuracy, and the information. Specifically, claims 2, 9, 12, 14, 19, and 24 further describe the machine learning prediction function model and the estimated value, which are part of the abstract idea. Claims 3, 21, and 23 describe determining and intrinsic value of the data stream, which describes determining a value and is thus abstract. The broad use of artificial intelligence to determine the value does not provide a practical application or significantly more than the abstract idea because it only involves using a computer as a tool to automate and/or implement the abstract idea. Claims 8, 13, and 18 further describe the artificial intelligence, but do not require any steps or functions to be performed. Additionally, the use of a local computer, a secure platform on a central server computer, a secure communication link from the local computer to the secure platform, a machine learning prediction function model and machine learning trained data, a blockchain, a computer system comprising one or more computer processors and one or more non-transitory computer-readable storage media, and a computer program product comprising program instructions on a computer-readable storage medium to perform the steps does not provide a practical application or significantly more than the abstract idea because it amounts to no more than using a computer or processor to automate and/or implement the abstract idea. The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Statement Regarding Prior Art
The closest prior art of Blaikie, III, et al. (US 2021/0365574) (“Blaikie”) discloses encrypting access to a data stream (Blaikie ¶¶ 19, 22, 38, 93), where encrypting access to the data stream is on a blockchain (Blaikie ¶¶ 7, 22-23, 26, 37-38, 46-48), storing the encrypted data stream on a secure platform (Blaikie ¶¶ 24, 66, 71-72, 75, 90, 96), determining an estimated value of the data stream based on a prediction function (Blaikie ¶¶ 29, 52-55, 57, 66, 92), storing the estimated value with the data stream on the secure platform (Blaikie ¶¶ 65-66), and controlling access on the secure platform to the data stream and the estimated value (Blaikie ¶¶ 65-66, 74, 77, 93, 97).
Blaikie additionally discloses that encrypting access to the data stream is performed on a central server computer and the secure platform storing the encrypted data stream is on the central server computer (Blaikie ¶¶ 90-93), and that controlling access on the secure platform is controlled by a token-based system (Blaikie ¶¶ 30, 48, 65-66, 73-74, 78, 97). Blaikie further discloses bundling a plurality of data streams based on the value and number of tokens for accessing the plurality of data streams (Blaikie ¶¶ 28). Hummel, et al. (US 2013/0166354) (“Hummel”) discloses determining an accuracy of the estimated value, storing the accuracy of the estimated value, and controlling access to the accuracy of the estimated value (Hummel ¶¶ 26-28, 30-31, 34-37, 46, 49-50, 52, 55-56, 59-60). Hummel also discloses that determining the accuracy of the estimated value comprises assessing the accuracy of the data predictors and valuing the data streams based on the prediction accuracy (Hummel ¶¶ 30-31, 34-37, 40-43, 48-50, 60). Finally, Cella, et al. (US 2018/0284758) (“Cella”) discloses that encrypting access to the data stream is performed on a local computer (Cella ¶¶ 1527, 1582, 1616).
However, the prior art does not disclose, neither singly nor in combination, the specific claimed steps of transmitting a data stream on a secure communication link from a local computer to a secure platform on a central server computer, encrypting access to the data stream on a block chain using homomorphic encryption, storing the homomorphic encrypted data stream on the blockchain on the secure platform, training a machine learning prediction function model on the homomorphic encrypted data stream resulting in a dataset of a plurality of machine learning trained data predictors, determining an accuracy of the machine learning trained data predictors, determining an accuracy profile of the homomorphic encrypted data stream based on the accuracy of the machine learning trained data predictors, storing the accuracy profile and the accuracy of the machine learning trained data predictors with the data stream on the blockchain on the secure platform, and controlling access on the secure platform to the data stream, the accuracy profile, the machine learning trained data predictors, and the accuracy of the machine learning trained data predictors on the blockchain by permitting the accuracy profile to be viewed and the data stream to be obtained based on receipt of tokens implemented on the blockchain.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohammad A. Nilforoush whose telephone number is (571)270-5298. The examiner can normally be reached Monday-Friday 12pm-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John W. Hayes can be reached at 571-272-6708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mohammad A. Nilforoush/Primary Examiner, Art Unit 3697
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