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
This is in response to the communication filed on 03/21/2025. Claims 1-4 were pending in the application. Claims 3-4 are allowed. Claims 1-2 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract of the disclosure is objected to because (i) it is not clear whether “BC network” referring to a blockchain network or a different type of network; and (ii) a numerical references included in abstract (such as terminal 2 or network 5) may not be necessary and/ or could be placed in paratheses or be omitted. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b), and MPEP § 1826.
Claim Rejections - 35 USC § 103
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 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 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0035018 A1 (hereinafter KIM et al.) in view of US 2019/0287026 A1 (hereinafter Calmon et al.)
Regarding claim 1, KIM et al. teaches a machine learning data management system (note figure 3.300: verifying apparatus; para. [0030]: an apparatus for verifying integrity if an AI learning data) comprising:
a first information terminal device on a data provider side (note figure 3.320, para. [0031], [0037]: user terminal as data provider);
a second information terminal device
a server device (note figure 3.300: verifying apparatus 300 communicatively connected to AI server 330),
wherein the first information terminal device (note figure 3.320, para. [0031], [0037]: user terminal as data provider), the second information terminal device (note figure 3.320, para. [0032], [0037]: second user terminal), and the server device are communicably connected (note figure 3.300: verifying apparatus 300 communicatively connected to AI server 330 and user terminal 320),
wherein the machine learning data management system is configured to manage
machine learning data including training data, a trained model, test data, and output data, the machine learning data being used in a machine learning model (note para. [0005], [0042]: AI learning model/ training data),
wherein the server device comprises:
a first receiving unit configured to receive unencrypted machine learning data on the data provider side from the first information terminal device (note para [0037], [0039]: verifying apparatus receiving data from a first user terminal);
a first encryption unit configured to encrypt, into encrypted machine learning data, the unencrypted machine learning data on the data provider side that has been received by the first receiving unit (note para [0037], [0045]: verifying apparatus encrypting the received data from a first user terminal);
a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side
a second encryption unit configured to encrypt, into encrypted machine learning data, the unencrypted machine learning data
a registration unit (note figure 4.400: storage unit) configured to register, in a blockchain network, the encrypted machine learning data on the data provider side that has been encrypted by the first encryption unit (note figure 3.310: blockchain; para. [0040]; storing encrypted data in blockchain); and
a verification unit (note figure 3.300; figure 4.300: verifying apparatus) configured to determine authenticity of the machine learning data provided from the data provider side
KIM et al. fails to teach expressly a second information terminal device on a data recipient side; a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side; and determine authenticity of the machine learning data provided from the data provider side to the data recipient side.
However, Calmon et al. teaches a second information terminal device on a data recipient side (note figure 1B: 2nd LSP node 122); a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side (note figure 5 A.520; para. [0055], [0057]: receiving encrypted learning model from verification node); and determine authenticity of the machine learning data provided from the data provider side to the data recipient side (note figure 5A.540; para. [0055], [0056]: verifying authenticity of the learning model)
Calmon et al. and KIM et al. are analogous art because they are from the same field of endeavor of secure storage and verification of machine learning data utilizing blockchain network. Therefore, before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in art to modify KIM et al. device to include the features of a second information terminal device on a data recipient side; a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side; and determine authenticity of the machine learning data provided from the data provider side to the data recipient side since such arrangement would further allow a verification node/ server to facilitate secure collaboration of machine learning data/ models between two participating nodes/ terminals (note Calmon et al., para. [0001], [0005])
Regarding claim 2, KIM et al. teaches a machine learning data management system (note figure 3.300: verifying apparatus; para. [0030]: an apparatus for verifying integrity if an AI learning data) comprising:
a first information terminal device on a data provider side (note figure 3.320, para. [0031], [0037]: user terminal as data provider);
a second information terminal device
a server device (note figure 3.300: verifying apparatus 300 communicatively connected to AI server 330),
wherein the first information terminal device (note figure 3.320, para. [0031], [0037]: user terminal as data provider), the second information terminal device (note figure 3.320, para. [0032], [0037]: second user terminal), and the server device are communicably connected (note figure 3.300: verifying apparatus 300 communicatively connected to AI server 330 and user terminal 320),
wherein the machine learning data management system is configured to manage
machine learning data including training data, a trained model, test data, and output data, the machine learning data being used in a machine learning model (note para. [0005], [0042]: AI learning model/ training data), and
wherein the server device comprises:
a first receiving unit configured to receive, from the first information terminal device, at least one piece of data selected from the group consisting of training data, a trained model, test data, and output data of unencrypted machine learning data on the data provider side (note para [0037], [0039]: verifying apparatus receiving data from a first user terminal);
a first encryption unit configured to individually encrypt, into at least one first-
encrypted value, the at least one piece of data selected from the group consisting of the
training data, the trained model, the test data, and the output data of the machine learning data on the data provider side that has been received by the first receiving unit (note para [0037], [0045]: verifying apparatus encrypting the received data from a first user terminal);
a second receiving unit configured to receive, from the second information terminal device, at least one piece of data selected from the group consisting of training data, a trained model, test data, and output data of unencrypted machine learning data that has been provided from the data provider side to the data recipient side, wherein the at least one piece of data is within a range of the machine learning data received by the first receiving unit (note para [0037], [0039]: verifying apparatus encrypting unencrypted AI data; verifying apparatus receiving data from a second user terminal);
a second encryption unit configured to individually encrypt, into at least one second- encrypted value, the at least one piece of data selected from the group consisting of the training data, the trained model, the test data, and the output data of the machine learning data
a registration unit (note figure 4.400: storage unit) configured to register, in a blockchain network, the at least one first-encrypted value of encrypted machine learning data on the data provider side that has been individually encrypted by the first encryption unit (note figure 3.310: blockchain; para. [0040]; storing encrypted data in blockchain); and
a verification unit (note figure 3.300; figure 4.300: verifying apparatus) configured to determine authenticity of the machine learning data provided from the data provider side
KIM et al. fails to teach expressly a second information terminal device on a data recipient side; a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side; and determine authenticity of the machine learning data provided from the data provider side to the data recipient side.
However, Calmon et al. teaches a second information terminal device on a data recipient side (note figure 1B: 2nd LSP node 122); a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side (note figure 5 A.520; para. [0055], [0057]: receiving encrypted learning model from verification node); and determine authenticity of the machine learning data provided from the data provider side to the data recipient side (note figure 5A.540; para. [0055], [0056]: verifying authenticity of the learning model)
Calmon et al. and KIM et al. are analogous art because they are from the same field of endeavor of secure storage and verification of machine learning data utilizing blockchain network. Therefore, before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in art to modify KIM et al. device to include the features of a second information terminal device on a data recipient side; a second receiving unit configured to receive, from the second information terminal device, unencrypted machine learning data that has been provided from the data provider side to the data recipient side; and determine authenticity of the machine learning data provided from the data provider side to the data recipient side since such arrangement would further allow a verification node/ server to facilitate secure collaboration of machine learning data/ models between two participating nodes/ terminals (note Calmon et al., para. [0001], [0005])
Allowable Subject Matter
Claims 3-4 are allowed.
Conclusion
A shortened statutory period for response to this action is set to expire in 3 (Three) months and 0 (Zero) days from the mailing date of this letter. Failure to respond within the period for response will result in ABANDOMENT of the application (see 35 U.S.C 133, M.P.E.P 710.02(b)). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANTO ABEDIN whose telephone number is 571-272-3551. The examiner can normally be reached on M-F from 8:30 AM to 6:30 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Jung (Jay) Kim, can be reached on 571-272-3804. The RightFax number for faxing directly to the examiner is 571-273-3551.
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/SHANTO ABEDIN/Primary Examiner, Art Unit 2494
