DETAILED ACTION
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 .
This written action is responding to the amendment dated on 11/27/2025.
Claims 1, 5-7 and 11-12 have been amended and all other claims are previously presented
Claims 1-12 are submitted for examination.
Claims 1-12 are pending.
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.
Priority
This application filed on April 24, 2024 claims priority of foreign application JP2023-071811 filed on April 25, 2023.
Information Disclosure Statement
The following Information Disclosure Statements in the instant application submitted in compliance with the provisions of 37 CFR 1.97, and thus, have been fully considered:
IDS filed on 04 April 2024.
Response to Arguments
Applicant’s amendment, filed on November 27, 2025, has claims 1, 5-7 and 11-12 amended and all other claims previously presented. Among the amended claims, claims 1, 11 and 12 are independent ones, and thus, the amendment necessitates a new ground of rejection.
Applicant’s remark, filed on November 27, 2025 on top of page 8 regarding 35 U.S.C. 101 abstract idea has been considered, however is not found persuasive. Applicant particularly argues that the claimed invention provides a technical solution by generating pseudo examination data based on the specific diagnostic data and the deleted examination data and storing the pseudo examining data in the memory in association with the specified diagnostic data as the training data. The claim language doesn’t describe how the storing of the pseudo examining data in the memory in association with the specified diagnostic data as the training data, helps the machine learning model. Also the claim doesn’t mention anything about the machine learning model.
Examiner suggest to include utilization of the newly stored training data by the machine learning model may help in overcoming the 35 U.S.C. 101 abstract idea.
Applicant’s remark, filed on November 27, 2025 on page 10 regarding, “Accordingly, the pseudo examination data recited in the claimed invention is fundamentally different from the preliminary data disclosed in Sasaki, and it would not have been obvious for a skilled person to modify Sakuri's withdrawal information by Sasaki's preliminary data to arrive at Claim 1” has been considered and found persuasive, however applicant’s amendment necessitates a new ground of rejection. Accordingly newly cited art by Shelton, IV et al. (US 2024/0111893) discloses, “At 53762, a data system (e.g., the surgical data system 45002 including the data classification module 45004 and the data removal module 45002) may detect a change in a consent associated with a patient. At 53764, the data system may identify private data (e.g., the dataset 53628) associated with the change in the consent. At 53766, the data system may identify a machine learning model (e.g., the machine learning model 53728) to which the private data has contributed. At 53768, the data system may determine input data that has contributed to the machine learning model. The input data may include the private data. At 53770, the data system may determine, based on the change in the consent associated with the private data, whether to replace the private data in the input data with replacement data. At 53772, the data system may adjust the input data based on the determination of whether to replace the private data in the input data with replacement data. A data system (e.g., the surgical data system 45002) may adjust the input data to a machine learning model (e.g., the machine learning model 53728) by one or more of the following: removing at least a portion of private data (e.g., the dataset 53628) associated with a change of a consent from the input data, replacing the private data with private data that differs from the private data associated with change in the consent, replacing the private data with public data, or converting the private data to the public data. A data system (e.g., the surgical data system 45002) may replace private data (e.g., the dataset 53628) associated with a change in a consent with synthetic data. For example, the data system may generate synthetic data based on the private data if the data system determines, based on the change in the consent, to replace the private data. The data system may replace the private data in the input data with the generated synthetic data. For example, the synthetic data may include transformed data or transformed dataset after the private data is processed using data enrichment. In some examples, the synthetic data may be generated by replacing one or more fields of a dataset with artificial data.”. (¶329-¶331). Sakuri, teaches, “The acquisition unit 15a further acquires consent withdrawal information indicating withdrawal of consent of each patient and adds the consent withdrawal information to the patient information 14a in the storage unit 14. Specifically, as illustrated in FIG. 4, the acquisition unit 15a acquires the consent withdrawal information instead of answer data from the answer SV 3. For example, the acquisition unit 15a acquires consent withdrawal information that that the patient registers in the answer SV 3 according to a procedure similar to that for registering answers and consent information after confirming the answers of the patient in the past and the consent information in a case where the initial purpose of the use in the third-party organization changes or the intention of the patient changes, or the like”. (¶42). “The generation unit 15b stops the combining with the examination-treatment information 14b corresponding to the patient information containing consent withdrawal information. Specifically, as illustrated in FIG. 6, the generation unit 15b stores the generated provision information 14c in the storage unit 14 and deletes the provision information 14c corresponding to the patient information 14a containing the consent withdrawal information from the storage unit 14. In the example illustrated in FIG. 6, when consent withdrawal information is contained in the patient information 14a, the generation unit 15b specifies the provision information 14c corresponding to the patient information 14a and deletes the provision information 14c from the storage unit 14.”. (Fig. 6, ¶48). Thus a person having an ordinary skill in the art would have combined the teachings of Shelton of generating synthetic data to replace the deleted data when a patient withdraws a consent of Sakuri. The motivation/suggestion for doing so would be to maintain consistency of a training model after data to train the model is deleted from the model.
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, 11 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites storing training data obtained from an examination performed on an individual and generated diagnostic data based on the examination, deleting specific examination data based on received delete instruction, generating pseudo examination data for the deleted examination data and storing the pseudo examination data along with training data in a memory.
The limitations of store, in a memory, training data in which examination data obtained from an examination got by an individual and diagnostic data generated based on the examination data are included in association with each other; delete specific examination data from the memory; specify the diagnostic data associated with the deleted examination data, and store the pseudo examination data in the memory in association with the specified diagnostic data as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor configured to,” nothing in the claim element precludes the step from practically being performed in the mind or performing by a human using a pen and paper. For example, but for the “processor” language, “configured to” in the context of this claim encompasses that a person manually storing papers having user’s personal information and information related to examination in a box. Similarly, the limitation of delete specific examination data from the data storage, and specifying diagnostic data associated with the deleted examination, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation manually by a human but for the recitation of generic computer components. For example, but for the “by a processor” language, “delete” in the context of this claim encompasses the user shredding the paper that has user’s information. “specify” in the context of claim is that the user referring to a table to define the similar diagnostic information related to deleted information as explained in the disclosure of the instant application. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – generate pseudo examination data, which is pseudo for the deleted examination data, based on the specified diagnostic data. Basically the user creates dummy data for the deleted examination data. This is all done by a processor as the claim recites “processor configured to”. The processor in is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, The claim is not patent eligible.
The dependent claims 2-10 do not represent significantly more and are too directed to non-statutory subject matter.
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.
Claims 1-4, 8 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sakuri et al. (US PGPUB. # US 2024/0410963, hereinafter “Sakuri”, provided by the applicant in an IDS), and further in view of Shelton, IV et al. (US PGPUB. # US 2024/0111893, hereinafter “Shelton”).
Referring to Claims 1, 11 and 12:
Regarding Claim 1, Sakuri teaches,
A training data management device comprising:
one or more processors configured to: (Fig. 9(1020), ¶68).
store, in a memory, training data in which examination data obtained from an examination performed on an individual and diagnostic data generated based on the examination data are included in association with each other; (Abstract, “store patient information on each patient and examination-treatment information on examination and treatment on each patient dispersedly”, ¶23, ¶27, “acquires examination-treatment information on each patient from the examination-treatment information management SV 2 and stores the medical information in the storage unit”, Fig. 2, ¶34, “patient information 14a and examination-treatment information 14b that are used for a provision process to be described below, provision information 14c “, ¶36-¶37, ¶44, “the provision information 14c obtained by combining the patient information 14a containing consent information and the examination-treatment information 14b with respect to each patient” i.e. Examiner submits that combination of examination-treatment (diagnostic) data is stored as training data)
in response to an instruction of data deletion being received, delete specific examination data from the memory; (¶42, “acquires consent withdrawal information indicating withdrawal of consent of each patient”, Fig. 6, ¶48, “deletes the provision information 14c corresponding to the patient information 14a containing the consent withdrawal information from the storage unit 14”, i.e. specific examination data is deleted (in response to withdrawn consent – instruction to delete data) )
Sakuri does not teach explicitly,
specify the diagnostic data associated with the deleted examination data;
generate pseudo examination data, which is pseudo for the deleted examination data, based on the specified diagnostic data and the deleted examination data; and
store the pseudo examination data in the memory in association with the specified diagnostic data as the training data.
However, Shelton teaches,
specify the diagnostic data associated with the deleted examination data; (Fig. 17, ¶323-¶326, Fig. 18, ¶329, “the data system may identify private data (e.g., the dataset 53628) associated with the change in the consent”, Fig. 19, ¶332, i.e. diagnostic data associated with the deleted examination data is identified)
generate pseudo examination data, which is pseudo for the deleted examination data, based on the specified diagnostic data and the deleted examination data; (¶23, Fig. 18, ¶331, “the data system may generate synthetic data based on the private data if the data system determines, based on the change in the consent, to replace the private data”, i.e. synthetic data (pseudo examination data) is generated) and
store the pseudo examination data in the memory in association with the specified diagnostic data as the training data. (¶23, Fig. 18, ¶331, “The data system may replace the private data in the input data with the generated synthetic data”, Fig. 19, ¶332, Claim 13, i.e. synthetic data (pseudo examination data) is stored along with diagnostic data as the training data for machine learning).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Shelton with the invention of Sakuri.
Sakuri teaches, deleting examination data when a user withdraws a consent. Shelton teaches, replacing deleted examination data with synthetic data. Therefore, it would have been obvious to have replacing deleted examination data with synthetic data of Shelton with deleting examination data when a user withdraws a consent of Sakuri to maintain consistency of a training model after data to train the model is deleted from the model.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 11, it is a method Claim of above device Claim 1 and therefore Claim 11 is rejected with the same rationale as applied against Claim 1 above.
Regarding Claim 12, it is a non-transitory computer readable medium Claim of above device Claim 1 and therefore Claim 12 is rejected with the same rationale as applied against Claim 1 above.
Regarding Claim 2, rejection of Claim 1 is included and for the same motivation Sakuri teaches,
The training data management device according to claim 1, wherein the specific examination data is the examination data including a content indicating that consent to provide information is withdrawn from the individual. (Abstract, ¶40, ¶53, “acquires consent information indicating consent for provision of the patient information 14a and the examination-treatment information 14b from each patient”).
Regarding Claim 3, rejection of Claim 1 is included and for the same motivation Sakuri does not teach explicitly,
The training data management device according to claim 1, wherein the pseudo examination data is the examination data having the same feature as a feature of the deleted examination data.
However, Shelton teaches,
The training data management device according to claim 1, wherein the pseudo examination data is the examination data having the same feature as a feature of the deleted examination data. (¶331, Fig. 19, ¶332).
Regarding Claim 4, rejection of Claim 1 is included and for the same motivation Sakuri does not teach explicitly,
The training data management device according to claim 1, wherein the pseudo examination data is the examination data that does not completely match the deleted examination data.
However, Shelton teaches,
The training data management device according to claim 1, wherein the pseudo examination data is the examination data that does not completely match the deleted examination data. (Fig. 18, ¶31, “the synthetic data may include transformed data or transformed dataset after the private data is processed using data enrichment”).
Regarding Claim 8, rejection of Claim 1 is included and for the same motivation Sakuri does not teach explicitly,
The training data management device according to claim 1, wherein the one or more processors are configured to generate the pseudo examination data by using a pseudo examination data generation model, and the pseudo examination data generation model is a trained model that has been trained to generate the pseudo examination data by receiving input of the deleted examination data and the diagnostic data.
However, Shelton teaches,
The training data management device according to claim 1, wherein the one or more processors are configured to generate the pseudo examination data by using a pseudo examination data generation model, and the pseudo examination data generation model is a trained model that has been trained to generate the pseudo examination data by receiving input of the deleted examination data and the diagnostic data. (Fig. 18, ¶331, Fig. 19, ¶332-¶337).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Sakuri et al. (US PGPUB. # US 2024/0410963, hereinafter “Sakuri”, provided by the applicant in an IDS), and further in view of Shelton, IV et al. (US PGPUB. # US 2024/0111893, hereinafter “Shelton”), and further in view of Sasaki et al. (JP PGPUB. # JP 2021-111288A, hereinafter “Sasaki”).
Regarding Claim 9, rejection of Claim 8 is included and combination of Sakuri and Shelton does not teach explicitly,
The training data management device according to claim 8, wherein the one or more processors are configured to calculate a reconstruction error indicating a rate of match between the deleted examination data and the pseudo examination data, and
the pseudo examination data generation model generates the pseudo examination data such that an absolute value of the reconstruction error is a positive number.
However, Sasaki teaches,
The training data management device according to claim 8, wherein the one or more processors are configured to calculate a reconstruction error indicating a rate of match between the deleted examination data (Page 8, Lines 32-44) and
the pseudo examination data, and the pseudo examination data generation model generates the pseudo examination data such that an absolute value of the reconstruction error is a positive number. (Page 8, Lines 24-44).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Sasaki with the invention of Sakuri in view of Shelton.
Sakuri in view of Shelton teaches, deleting examination data when a user withdraws a consent and replacing deleted examination data with synthetic data. Sasaki teaches, calculating reconstruction error and generating pseudo examination data having reconstruction error is positive number. Therefore, it would have been obvious to have calculating reconstruction error and generating pseudo examination data having reconstruction error is positive number of Sasaki into the teachings of Sakuri in view of Shelton to accurately generate pseudo examination data.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 10, rejection of Claim 8 is included and combination of Sakuri and Shelton does not teach explicitly,
The training data management device according to claim 8, wherein the pseudo examination data generation model is a model in which a generation model that generates the pseudo examination data by receiving the input of the deleted examination data and the diagnostic data, and an evaluation model that evaluates the generated pseudo examination data are connected to each other.
However, Sasaki teaches,
The training data management device according to claim 8, wherein the pseudo examination data generation model is a model in which a generation model that generates the pseudo examination data by receiving the input of the deleted examination data and the diagnostic data, and an evaluation model that evaluates the generated pseudo examination data are connected to each other. (Page 3, Lines Page 7, Lines 21-26, Page 8, Lines 17-20, Page 8, Lines 24-45).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Sasaki with the invention of Sakuri in view of Shelton.
Sakuri in view of Shelton teaches, deleting examination data when a user withdraws a consent and replacing deleted examination data with synthetic data. Sasaki teaches, calculating reconstruction error and generating pseudo examination data having reconstruction error is positive number. Therefore, it would have been obvious to have calculating reconstruction error and generating pseudo examination data having reconstruction error is positive number of Sasaki into the teachings of Sakuri in view of Shelton to accurately generate pseudo examination data.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sakuri et al. (US PGPUB. # US 2024/0410963, hereinafter “Sakuri”, provided by the applicant in an IDS), and further in view of Shelton, IV et al. (US PGPUB. # US 2024/0111893, hereinafter “Shelton”), and further in view of Yasushi Yagi (US PGPUB. # US 2022/0374550, hereinafter “Yagi”).
Regarding Claim 5 rejection of Claim 1 is included and Sakuri does not teach explicitly,
The training data management device according to claim 1, wherein the data storage stores the training data in which a personal ID of the individual who gets the examination is associated with the examination data using a first pseudonym ID, and the first pseudonym ID is associated with the diagnostic data using a second pseudonym ID, so that the personal ID, the examination data, and the diagnostic data are associated with each other.
However, Shelton teaches,
The training data management device according to claim 1, wherein the data storage stores the training data in which a personal ID of the individual who gets the examination is associated with the examination data using a first [pseudonym] ID, (¶141, ¶286, ¶317) [and the first pseudonym ID is associated with the diagnostic data using a second pseudonym ID, so that the personal ID, the examination data, and the diagnostic data are associated with each other].
Combination of Sakuri and Shelton does not teach explicitly
The training data management device according to claim 1, [wherein the data storage stores the training data in which a personal ID of the individual who gets the examination is associated with the examination data using] a first pseudonym ID, and the first pseudonym ID is associated with the diagnostic data using a second pseudonym ID, so that the personal ID, the examination data, and the diagnostic data are associated with each other.
However, Yagi teaches,
The training data management device according to claim 1, [wherein the data storage stores the training data in which a personal ID of the individual who gets the examination is associated with the examination data using] a first pseudonym ID, and the first pseudonym ID is associated with the diagnostic data using a second pseudonym ID, so that the personal ID, the examination data, and the diagnostic data are associated with each other. (Fig. 6A, Fig. 6B, ¶50).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Yagi with the invention of Sakuri in view of Shelton.
Sakuri in view of Shelton teaches, deleting examination data when a user withdraws a consent and replacing deleted examination data with synthetic data. Yagi teaches, creating primary pseudonym and secondary pseudonym to link the records. Therefore, it would have been obvious to have creating primary pseudonym and secondary pseudonym to link the records of Yagi in to the teachings of Sakuri in view of Shelton to protect privacy of a user and comply with government regulations.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 6 rejection of Claim 5 is included and for the same motivation Sakuri and Shelton does not teach explicitly,
The training data management device according to claim 5, wherein the one or more processors are configured to generate:
a pseudonym reverse lookup table consisting of the personal ID and the first pseudonym ID;
a pseudonym examination table consisting of the first pseudonym ID and the examination data;
an examination diagnosis reverse lookup table consisting of the first pseudonym ID and the second pseudonym ID; and
a pseudonym diagnosis table consisting of the second pseudonym ID and the diagnostic data, and
the data storage stores the pseudonym reverse lookup table, the pseudonym examination table, the examination diagnosis reverse lookup table, and the pseudonym diagnosis table.
However, Yagi teaches,
The training data management device according to claim 5, wherein the one or more processors are configured to generate:
a pseudonym reverse lookup table consisting of the personal ID and the first pseudonym ID; (Fig. 3, Fig. 6A, ¶50).
a pseudonym examination table consisting of the first pseudonym ID and the examination data; (Fig. 7A, ¶51)
an examination diagnosis reverse lookup table consisting of the first pseudonym ID and the second pseudonym ID; (Fig. 6B, ¶50) and
a pseudonym diagnosis table consisting of the second pseudonym ID and the diagnostic data, (Fig. 9A) and
the data storage stores the pseudonym reverse lookup table, the pseudonym examination table, the examination diagnosis reverse lookup table, and the pseudonym diagnosis table. (Fig. 3, ¶48, Fig. 6A, Fig. 7A, ¶50).
Regarding Claim 7 rejection of Claim 6 is included and for the same motivation Sakuri teaches,
The training data management device according to claim 6,
wherein, the one or more processors are configured to delete, in a case of deleting the examination data of a specific individual from the data storage: (¶42, “acquires consent withdrawal information indicating withdrawal of consent of each patient”, Fig. 6, ¶48, “deletes the provision information 14c corresponding to the patient information 14a containing the consent withdrawal information from the storage unit 14”, i.e. specific individual data (withdrawn consent) is deleted )
Combination of Sakuri and Shelton does not teach explicitly,
the personal ID of the specific individual and a specific first pseudonym ID associated with the personal ID in the pseudonym reverse lookup table;
the specific first pseudonym ID and specific examination data associated with the specific first pseudonym ID in the pseudonym examination table; and
the specific first pseudonym ID and a specific second pseudonym ID associated with the specific first pseudonym ID in the examination diagnosis reverse lookup table.
However, Yagi teaches,
the personal ID of the specific individual and a specific first pseudonym ID associated with the personal ID in the pseudonym reverse lookup table; (Fig. 6A, Fig. 11, ¶63)
the specific first pseudonym ID and specific examination data associated with the specific first pseudonym ID in the pseudonym examination table; (Fig. 7A, ¶51) and
the specific first pseudonym ID and a specific second pseudonym ID associated with the specific first pseudonym ID in the examination diagnosis reverse lookup table. (Fig. 6B, ¶50).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Takeshima (US PGPUB. # US 2022/0343634) discloses, a pseudo data generation apparatus comprising processing circuitry. The processing circuitry collects a data set including data values of one or more dimensions. The processing circuitry performs conversion of the data values of the one or more dimensions included in the data set. The processing circuitry generates a pseudo physical parameter relating to each of one or more physical amounts.
Whalety et al. (US PGPUB. # US 2018/0255023) discloses, aa system that anonymizes sensor data to facilitate machine-learning training operations without disclosing an associated user's identity. During operation, the system receives encrypted sensor data at a gateway server, wherein the encrypted sensor data includes a client identifier corresponding to an associated user or client device. Next, the system moves the encrypted sensor data into a secure enclave. The secure enclave then: decrypts the encrypted sensor data; replaces the client identifier with an anonymized identifier to produce anonymized sensor data; and communicates the anonymized sensor data to a machine-learning system. Finally, the machine-learning system: uses the anonymized sensor data to train a model to perform a recognition operation, and uses the trained model to perform the recognition operation on subsequently received sensor data.
Kim (US PAT. # US 11,200,494) discloses, a method of training an obfuscation network for obfuscating original data to protect personal information is provided. The method includes steps of a learning device, (a) inputting acquired training data into an obfuscation network to obfuscate the training data and inputting the obfuscated training data into an augmentation network to augment the obfuscated training data; (b) (i) inputting the augmented obfuscated training data into a learning network to generate first characteristic information and (ii) inputting the training data into the learning network to generate second characteristic information; and (c) training the obfuscation network such that (i) a first error, calculated by using the first and the second characteristic information, is minimized and (ii) a second error, calculated by using (ii-1) modified training data or modified obfuscated training data, and (ii-2) the obfuscated training data or the augmented obfuscated training data, is maximized.
Suzuki (US PGPUB. # US 2023/0237774) discloses, a data collection system according to the present disclosure includes: a sensor device that collects data; and a server device including a learning model that performs output according to a learning result, corresponding to input, and a data analysis unit that specifies data that is beneficial for or lacking in training of the learning model. The server device transmits, to the sensor device, a request signal for collecting data that is beneficial for or lacking in the training specified by the data analysis unit, or data similar to the data, the sensor device collects data that is beneficial for or lacking in the training, or similar data based on the received request signal, and transmits the collected data to the server device, and the server device retrains the learning model based on the data transmitted from the sensor device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARSHAN I DHRUV whose telephone number is (571)272-4316. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yin-Chen Shaw can be reached at 571-272-8878. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DARSHAN I DHRUV/Primary Examiner, Art Unit 2498