Detailed Action
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 5, 2025 has been entered.
Acknowledgements
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE filed on November 5, 2025.
Claims 2-3, 12-13, 22, and 24 are cancelled.
Claims 25-26 are added.
Claims 1, 4-11, 14-21, 23, and 25-26 are pending.
Claims 1, 4-11, 14-21, 23, and 25-26 are examined.
This Office Action is given Paper No. 20260317 for references purposes only.
Information Disclosure Statement
The Information Disclosure Statements filed on November 5, 2025 and February 5, 2026 have been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Objections
Claims 1 and 11 recite “to enable a third party.” Examiner assumes that Applicant intended “to enable the third party.” Appropriate correction is required.
Claim Rejections - 35 USC § 112b
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION - The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4-11, 14-21, 23, and 25-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites “continuously training the artificial neural network as training data instances are collected from one or more data sources.” This phrase is vague and indefinite because “continuously training” implies that the training goes on forever, and thus the metes and bounds of the claim cannot be determined. For purposes of applying the prior art only, Examiner will interpret as “training the artificial neural network as training data instances are collected from one or more data sources. Claim 11 is similarly rejected.
Claim 1 recites “the continuous training process.” There is lack of antecedent basis for this term. For purposes of applying the prior art only, Examiner will interpret as “the training process.” Claim 11 is similarly rejected.
Claims 6-7 recite “the first portion” and “the second portion.” There is lack of antecedent basis for these terms. For purposes of applying the prior art only, Examiner will interpret as “a first portion” and “a second portion.” Claims 16-17 are similarly rejected.
Claim Rejections - 35 USC § 103
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-11, 14-21, 23, and 25-26 are rejected under 35 U.S.C. 103(a) as being unpatentable over Mendelsohn (US 2011/0137821) in view of Auerbach et al. (US 11,522,700).
Claims 1, 11
Mendelsohn discloses:
training, by a set of computing devices having one or more processors (processor, see [0019]), an artificial neural network (trained neural networks, see [0021]), wherein the training comprises:
continuously training the artificial neural network (produce a trained neural network, see [0026]) as training data instances (market data, see [0021]) are collected from one or more data sources (exchanges, see [0021-0023]);
determining, based on a performance of the artificial neural network on a classification task (e.g. variety of financial data for every market in the system, see [0027]), that additional training is required for a specific type of data (e.g. financial data, see [0027]);
responsive to the determining, retrieving a stored training data set comprising historical training data (historical data, see [0026]) that is related to the classification task and includes the specific type of data;
configuring a retraining process (retrain the server’s neural networks, see [0026-0027]) to improve the performance of the artificial neural network with respect to the classification task based on the stored training data set, wherein the configured retraining process uses different training operations (trains the suite of predictions for each market and tests them against new data, see [0027]) than the continuous training process;
retraining the artificial neural network (neural network trainer does retraining as necessary, see [0027-0028]) using the configured retraining process and the stored training data set; and
resuming the continuous training for additional training data instances (e.g. new prices become available, see [0037, 0042]) that are collected from the one or more data sources.
Mendelsohn does not disclose:
Receiving… enterprise;
For each… party;
Assigning… asset;
Configuring… assets.
Auerbach teaches:
receiving, at the set of computing devices, a set of assets (digital math-based assets, see C54 L24-41) privately generated by an enterprise;
for each asset of the set of assets: classifying, by the set of computing devices and utilizing the trained artificial neural network, the respective asset into an access control category (hot vs. cold wallet, see C78 L1-38) of a plurality of access control categories, wherein each asset control category of the plurality of access control categories is associated with a set of asset controls (e.g. hot wallets cover transactions anticipated during a defined period, see C78 L1-38) that dictate one or more transaction parameters (e.g. minimum price limit, collar minimum, see C279 L9-60) for an exchange of the respective asset with a third party;
assigning the set of asset controls (e.g. hot wallets cover transactions anticipated during a defined period, see C78 L1-38) for the access control category classified by the artificial neural network for the respective asset; and
configuring an interface (GUI for an interface with the digital asset exchange, see C50 L63-67, Figure 72A) associated with the enterprise to enable a third party to access the set of assets based on the respective sets of access controls assigned to the set of assets.
Mendelsohn discloses training an artificial neural network, continuously training the artificial neural network, determining additional training is required, retrieving historical training data, configuring a retraining process, retraining the artificial neural network, and resuming the continuous training. Mendelsohn does not disclose receiving a set of assets, classifying the assets, assigning asset controls, and configuring an interface, but Auerbach does. It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to combine the calculating predictive technical indicators of Mendelsohn with the receiving a set of assets, classifying the assets, assigning asset controls, and configuring an interface of Auerbach because 1) a need exists for developing technical indicators to help traders respond to market changes without losing profit opportunity or risk increased losses (see Mendelsohn [0003-0004]); and 2) a need exists for generating user defined smart contracts and depositing, holding, and distributing collateral in the form of a stable value token for a security token based on the user defined smart contract (see Auerbach C4 L36-41). Receiving a set of assets, classifying the assets, assigning asset controls, and configuring an interface can assist in responding to market changes without losing profit opportunity or risk increased losses.
Claims 4, 14
Furthermore, Auerbach teaches:
publishing at least a portion of the set of assets to a blockchain using a custodial digital wallet (custodial digital wallet, see C79 L13-24) associated with the enterprise.
Claims 5, 15
Furthermore, Auerbach teaches:
publishing at least a portion of the set of assets to a blockchain using a digital wallet (custodial digital wallet, see C79 L13-24) associated with the enterprise.
Claims 6, 16
Furthermore, Auerbach teaches:
the first portion has a first access control category that indicates that a first set of asset controls of the first access control category is less restrictive (hot wallets cover transactions anticipated during a defined period, see C78 L1-38) than a second set of asset controls for a second access control category classified for the second portion (cold wallets have keys that have never been exposed to the Internet, see C78 L1-38).
Claims 7, 17
Furthermore, Auerbach teaches:
the first portion has a first access control category that indicates a greater frequency of access (hot wallets to satisfy withdrawals from the exchange, see C78 L1-38) than a second access control category classified for the second portion (cold wallets have keys that have never been exposed to the Internet, see C78 L1-38).
Claims 8, 18
Furthermore, Auerbach teaches:
the set of asset controls includes an asset control that matches an access control (e.g. matches an interest rate, see C111 L22-28) for an enterprise entity that communicated at least one of the assets from the set of assets to the set of computing devices.
Claims 9, 19
Furthermore, Auerbach teaches:
the set of asset controls includes an asset control that indicates a security clearance level (certain level of security, see C65 L3-24, C78 L1-38).
Claims 10, 20
Furthermore, Auerbach teaches:
the one or more transaction parameters include a minimum pricing requirement (minimum price limit, collar minimum, see C279 L9-60).
Claims 21, 23
Furthermore, Mendelsohn discloses:
the stored training data set is based on at least one of historical data (historical data, see [0026]), generated data, or simulated data.
Claims 25, 26
Furthermore, Mendelsohn discloses:
determining that the performance of the artificial neural network on the classification task has stabilized (neural network trainer does retraining as necessary, see [0027-0028]) as a result of the retraining; and
responsive to determining that the performance has stabilized, resuming the continuous training (classifying relationships between financial markets, see [0036]).
Response to Arguments
Applicant argues that the prior art does not teach the amendments.
Examiner has replaced the Watson reference with Mendelsohn. Please see revised rejection with new mappings.
Claim Interpretation
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure (see attached form PTO-892).
Skeirik (US 5,167,009) discloses on-line process control neural network using data pointers.
Applicant is reminded that functional recitation(s) using the word and/or phrases “for”, “adapted to”, or other functional language (e.g. see claims 1 and 11 which recite “to improve the performance” and “to enable”) have been considered but are given little patentable weight because they fail to add any structural limitations and are thereby regarded as intended use language. To be especially clear, all limitations have been considered. However, a recitation of the intended use of the claimed product must result in a structural difference between the claimed product and the prior art in order to patentably distinguish the claimed product from the prior art. If the prior art structure is capable of performing the intended use, then it reads on the claimed limitation. In re Casey, 370 F.2d 576, 152 USPQ 235 (CCPA 1967) ("The manner or method in which such a machine is to be utilized is not germane to the issue of patentability of the machine itself.”); In re Otto, 136 USPQ 458, 459 (CCPA 1963). See also MPEP §§ 2106 II (C.), 2114 and 2115. Unless expressly noted otherwise by Examiner, the claim interpretation principles in the paragraph apply to all examined claims currently pending.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from Examiner should be directed to Chrystina Zelaskiewicz whose telephone number is 571.270.3940. Examiner can normally be reached on Monday-Friday, 9:30am-5:00pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Neha Patel can be reached at 571-270-1492.
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/CHRYSTINA E ZELASKIEWICZ/Primary Examiner, Art Unit 3699