DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application, filed on November 07, 2024, is accepted.
Claims 1 – 2 are being considered on the merits.
Drawings
The drawings, filed on November 07, 2024, are accepted.
Specification
The specification, filed on November 07, 2024, is accepted.
Claim Objections
Claim 1 is objected to because of the following informalities: “an encryptor configured to encrypts” should be “an encryptor configured to encrypt”. Appropriate correction is required.
Double Patenting
No rejection warranted at application’s initial filling time of filling for a patent.
Claim Interpretation
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.
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) 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 limitations are:
Claim 1 recites “a sorter configured to sort…”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has investigated the specification of the instant application and finds the following:
Page [4] lines [25 – 32]: “The sorting unit 12 is configured to sort the personal information data Dp that should be secret when creating a desired AI model from AI learning data, in accordance with prescribed criteria. The desired AI model may be, for example, an AI model for personnel evaluation or the like. Sorting unit 12 may have criteria setting unit 12a. The criteria setting unit 12a sets prescribed criteria so as to reduce the amount of calculation processing, depending on the degree of increase or decrease in the amount of calculation processing for secure computation and the degree to which the personal information should be secret.”
Claim 1 recites “an encryptor configured to encrypt …”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has investigated the specification of the instant application and finds the following:
Page [6] lines [4 – 11]: “The encryption unit 13 is configured to output the personal information data Dp sorted by the sorting unit 12 as an encrypted personal information data Dpe by encrypting the personal information data Dp using various encryption techniques that are already or will be developed. The sorted personal information data Dp can be said to be personal information data Dp to be encrypted. As an example of encryption technology, secure computation such as homomorphic encryption and secure distributed encryption can be mentioned.”
Claim 1 recites “a holder configured to hold …”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has investigated the specification of the instant application and finds the following:
Page [6] lines [12 – 21]: “Holding unit 14 includes a memory or the like of a plurality of computers housed in a network and is configured using existing or upgraded blockchain technology. Holding unit 14 holds in the blockchain AI learn data of the status including the enciphered personal information Dpe either sequentially or at appropriate timing-intervals. That is, the holding unit 14 holds AI learning data including two kinds of data, an encrypted personal information data Dpe and an unencrypted personal information data Dn, in a block chain. A personal information data Dn that is not encrypted is data that can be treated as "non-personal information data".”
Claim 1 recites “AI learner configured to perform …”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has investigated the specification of the instant application and finds the following:
Page [6] lines [28 – 36] to page [7] lines [1 – 12]: “The AI learning unit 15 may be configured to perform AI learning using a supervised learning method, an unsupervised learning method, a semi-supervised learning method, a reinforcement learning method, a generative AI method, or the like, and to provide the learned AI models sequentially or collectively to a destination via a network. AI learning unit 15 may be configured using a neural network that performs efficient AI learning by expression learning, transfer learning, feature selection, fine tuning or hyperparameter tuning, ensemble learning, or the like. Alternatively, AI learning unit 15 may be configured as a generation AI that learns a pattern or a relation of the AI learning data and generates content data differing from the AI learning data. In either case, since the criteria setting by the criteria setting unit 12a in the sorting unit 12 is performed, the computation processing amount in the secure computing unit 15a is small compared to the case where the criteria setting is not performed. The secure computation here is, for example, a secure computation such as a perfectly homomorphic encryption method or a homomorphic encryption method and a secure distribution method. In particular, when the criteria setting unit 12a in the sorting unit 12 sets the criteria by AI learning, the further the learning proceeds, the less the computation processing of the secure computation is, which is advantageous in practice.”
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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 (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.
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.
Claims 1 – 2 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230385451 A1 to Lockhart, III et al., (hereinafter, “Lockhart”) in view of US 20230006845 A1 to Leedom, JR., (hereinafter, “Leedom”).
Regarding claim 1, Shmukler teaches an AI learning system comprising: a sorter configured to sort, from among AI learning data, personal information that should be secret when creating a desired AI model, according to prescribed criteria; [Lockhart, para. 11 discloses the system may crawl one or more identified web sites to extract data. The system may process the extracted data, searching the extracted data to identify patterns representing portions of PII data. The scraped data may be provided to an Artificial Intelligence (AI) engine for processing against particular rules to verify PII data or may be elevated to an administrator for review. Para. 14 discloses the compromised PII exchange system and method may be configured to not only search (and crawl) an initially identified set of websites based on an list of URLs (e.g., URLs for websites on the dark web) that have been provided to the system, but also automatically search additional websites identified by URLs detected in the initially identified websites.] an encryptor configured to encrypts the sorted personal information; [Lockhart, para. 39 discloses the compromised identity exchange system may disassociate and encrypt the PII data from an at-risk entity if the at risk entity did not perform the disassociation and may communicate the encrypted data to one or more of the compromised entities in response to the query. The compromised identity exchange system may receive results from the one or more entities in response to the queries where a match was made to a full PII identity or disassociated identity elements.] and an AI learner configured to perform secret calculations on at least the data portion related to encrypted the personal information to learn the AI model, using the stored AI learning data; [Lockhart, para. 13 discloses the memory may be configure to store instructions that, when executed, cause the processor to extract data from one or more websites using a crawler, detect portions within the data that resemble personally identifying information (PII) data based on PII data patterns using a risk assessment module, and compare a detected portion to data within a database of disassociated compromised PII data to determine a match using the risk assessment module. The instructions may further cause the processor to selectively assign a risk score to a data item within the database in response to determining the match using a risk scoring module.] wherein the sorting unit sets the prescribed criteria so as to reduce amount of calculation processing, depending on the degree of increase or decrease in the amount of calculation processing of the secure computation due to encryption of the personal information and the degree to which the personal information should be secret, [Lockhart, para. 39 discloses the compromised identity exchange system may receive results from the one or more entities in response to the queries where a match was made to a full PII identity or disassociated identity elements. Each match returned can include information about the data breach, which may consists of the date of the breach, the size/volume of the breach, a code indicating how the data was lost or stolen, among other attributes. In addition to these attributes, attributes associated with the consumer may also be used to measure risk. These attributes might include the number and severity of data breaches a consumer has been involved with, the location of the consumer, the event, if any, that is triggering the risk assessment, among other things. Additionally, participating at-risk entities' reported fraud data will be used to identify fraud rates within every compromised entity's compromised file, as well as attributes will be generated that reflect location of fraud, fraud linkages to email, physical address, phone number or other identity elements.], but Lockhart does not teach a holder configured to hold the AI learning data including the encrypted personal information in a blockchain.
However, Leedom does teach a holder configured to hold the AI learning data including the encrypted personal information in a blockchain. [Leedom, para. 77 discloses The operating systems within each device are designed to aid in authenticating the user including time variable encryption keys generated by each of the paired hardware components to allow secure encryption of the personal data arranged to be stored immutably in the associated personal blockchain or as blocks in a blockchain, serving other users and/or purposes, wherein only the user can enable the personal computer device(s) to grant access to those blocks containing the immutably stored personal data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Leedom’s system with Lockhart’s system, with a motivation for user transactions, such as transactions successively enabled over time by the user's smartphone and/or paired smartwatch will generate user data that may be encrypted within blocks of the user's blockchain. The immutably stored personal data will be date stamped and stored in successive blocks of the associated personal blockchain. By creating and storing parts of each encryption key in the respective paired personal computers, the encryption of personal data for storage in the personal blockchain may be undertaken in a way that requires the respective paired personal computers to be in sufficient proximity for the DApp (smartcontract and blockchain) to operate properly. [Leedom, para. 75]
As per claim 2, modified Lockhart teaches the AI learning system according to claim 1, wherein the sorting unit sets the prescribed criteria and sorts the personal information by AI learning using the degree of increase/decrease in the amount of calculation processing and the degree to which the personal information should be secret as AI learning data. [Lockhart, para. 39 discloses the compromised identity exchange system may receive results from the one or more entities in response to the queries where a match was made to a full PII identity or disassociated identity elements. Each match returned can include information about the data breach, which may consists of the date of the breach, the size/volume of the breach, a code indicating how the data was lost or stolen, among other attributes. In addition to these attributes, attributes associated with the consumer may also be used to measure risk. These attributes might include the number and severity of data breaches a consumer has been involved with, the location of the consumer, the event, if any, that is triggering the risk assessment, among other things. Additionally, participating at-risk entities' reported fraud data will be used to identify fraud rates within every compromised entity's compromised file, as well as attributes will be generated that reflect location of fraud, fraud linkages to email, physical address, phone number or other identity elements.]
Conclusion
Pertinent prior art made of record however not relied upon:
US 20240089081 A1 to Kushnir et al.
“An example system includes a processor to compute a tensor of indicators indicating a presence of partial sums in an encrypted vector of indicators. The processor can also securely reorder an encrypted array based on the computed tensor of indicators to generate a reordered encrypted array.”
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/P.P./Patent Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408