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 .
1. This action is responsive to the application filed on 08/25/2025.
2. Claims 1-20 are pending.
3. Claims 1-20 are rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-8, 10-15, and 17-20 of Patent No. 12,417,315. Although the claims at issue are not identical, they are not patentably distinct from each other because they are both claiming common subject matter, obfuscating personal data from internet using machine learning.
The Examiner notes that parent application (above-mentioned patent) and the instant application are directed towards patentability indistinct variations of the same inventive concept, which is obfuscating personal data from internet using machine learning. The Examiner made the double patenting rejection determination based on the fact that the parent application has claims that have overall appearances with basically the same design characteristics and the differences between both parent application and current application are considered patentably insufficient when they are obvious to a person skilled in the art. While the parent applications have different scopes, the Examiner found that the difference in scope is minor and patentably indistinct between the claims being compared.
Thus, the applied nonstatutory double patenting rejection applies in the current application because the claims are directed to the same inventive concept but with different appearances or differing scope that are patentability indistinct from each other.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei Chen et al (US 20210182429 A1), hereinafter “Chen” in view of Samuel E. Bradshaw et al (US 20210056405 A1), hereinafter “Bradshaw”.
Regarding Claim 1, Chen discloses a method comprising:
receiving, by a server from a user device associated with a user, a request to obfuscate publicly available personal data associated with the user (Chen, Paragraph 0018, content server receives content from a submitting user. Paragraph 0019, content server detects sensitive material in the submitted content. Paragraphs 0020-0022, obfuscation data can be retrieved from a data store and/or received from the submitting user);
retrieving, by the server from a database, verified personal data associated with the user (Chen, Paragraph 0018, content server extracts from the received content one or more name entities and/or one or more image face portions. Paragraph 0022, content server accesses the obfuscation data for the content and determines whether the content includes any items of sensitive material are to be obfuscated. Paragraph 0025, users submit content (e.g., textual data and/or image data) to different sites (e.g., social media, public blog, private blog, etc.). Providing content to requesting users);
and disseminating, by the server and based on receiving approval to disseminate the synthetic personal data from the user device, the synthetic personal data on the Internet (Chen, Paragraph 0025, content is loaded on a social media feed).
However, Chen fails to explicitly disclose generating, by the server using a machine learning model trained to generate synthetic personal data based on at least verified personal data associated with the user, synthetic personal data associated with the user.
Bradshaw, from the same or similar field of endeavor, discloses generating, by the server using a machine learning model trained to generate synthetic personal data based on at least verified personal data associated with the user, synthetic personal data associated with the user (Bradshaw, Paragraph 0011, obfuscation occurs at any one of the multiple computing devices that host different versions of the ANN. Paragraph 0023, training master version of the artificial neural network (ANN). Receiving data to train the master version of the ANN. Paragraphs 0034-0035, training the master version of the ANN based on received obfuscated data using machine learning. Generating obfuscated data based on extracted user data. Paragraph 0085, server performs functions).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Bradshaw in order to further modify the method of obfuscating content based on requesting user roles from the teachings of Chen with the method of machine learning with feature obfuscation from the teachings of Bradshaw.
One of ordinary skill in the art would have been motivated because training the machine learning model the system will be able to contain an updated version of the obfuscation process (Bradshaw – Paragraph 0022).
Regarding Claim 2, the combination of Chen and Bradshaw disclose the method of claim 1 above, where Chen further discloses further comprising:
before disseminating the synthetic personal data, calculating, by the server, a difference value between the synthetic personal data and the verified personal data, wherein disseminating the synthetic personal data on the Internet is based on a determination that the difference value satisfies a threshold (Chen, Paragraphs 0029-0031, determining similarity threshold in order to identify items of sensitive material and obfuscate these identified items of sensitive material).
Regarding Claim 3, the combination of Chen and Bradshaw disclose the method of claim 2 above, where Chen further discloses further comprising:
calculating, by the server, a similarity value between the synthetic personal data and the verified personal data, wherein disseminating the synthetic personal data on the Internet is further based on a determination that the similarity value satisfies a second threshold (Chen, Paragraphs 0029-0031, determining similarity threshold in order to identify items of sensitive material and obfuscate these identified items of sensitive material).
Regarding Claim 4, the combination of Chen and Bradshaw disclose the method of claim 1 above, where Bradshaw further discloses wherein disseminating the synthetic personal data comprises:
uploading, by the server, the synthetic personal data to one or more public websites (Bradshaw, Fig 3, Paragraph 0061, user data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file).
Regarding Claim 5, the combination of Chen and Bradshaw disclose the method of claim 1 above, where Bradshaw further discloses wherein the disseminating the synthetic personal data on the Internet comprises at least one of:
posting the synthetic personal data to a social media website; publishing an article containing the synthetic personal data to a news website; or publishing a blog post that comprises the synthetic personal data (Bradshaw, Fig 3, Paragraphs 0060-0061, substitution step applying data masking and preserving an authentic look and feel for the obfuscated user data, wherein the substitution step includes substituting existing values in the extracted features of the user data with authentic looking values. User data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file. Paragraphs 0063-0064, shuffling data along with substituting data).
Regarding Claim 6, the combination of Chen and Bradshaw disclose the method of claim 1 above, where Bradshaw further discloses wherein the disseminating the synthetic personal data on the Internet comprises replacing first verified personal data with first synthetic personal data (Bradshaw, Fig 3, Paragraph 0061, user data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file).
Regarding Claim 7, the combination of Chen and Bradshaw disclose the method of claim 1 above, where Bradshaw further discloses further comprising:
removing, by the server from one or more public websites, one or more entries comprising the verified personal data (Bradshaw, Fig 3, Paragraphs 0060-0061, substitution step applying data masking and preserving an authentic look and feel for the obfuscated user data, wherein the substitution step includes substituting existing values in the extracted features of the user data with authentic looking values).
Regarding Claim 8, Chen discloses a computing device, comprising:
one or more processors (Chen, Paragraph 0105, processor);
and memory storing instructions that, when executed by the one or more processors (Chen, Paragraph 0105, memory), cause the computing device to:
monitor one or more public sites for publicly available personal data associated with a user (Chen, Paragraph 0018, content server extracts from the received content one or more name entities and/or one or more image face portions).
However, Chen fails to explicitly disclose generate synthetic personal data associated with the user, wherein the synthetic personal data comprises data configured to appear similar to verified personal data associated with the user while being different from verified personal data associated with the user; and disseminate, based on receiving approval to disseminate the synthetic personal data from a device associated with the user, the synthetic personal data to one or more public sites.
Bradshaw, from the same or similar field of endeavor, discloses generate synthetic personal data associated with the user, wherein the synthetic personal data comprises data configured to appear similar to verified personal data associated with the user while being different from verified personal data associated with the user (Bradshaw, Paragraph 0011, obfuscation occurs at any one of the multiple computing devices that host different versions of the ANN. Paragraph 0023, training master version of the artificial neural network (ANN). Receiving data to train the master version of the ANN. Paragraphs 0034-0035, training the master version of the ANN based on received obfuscated data using machine learning. Generating obfuscated data based on extracted user data. Paragraph 0085, server performs functions);
and disseminate, based on receiving approval to disseminate the synthetic personal data from a device associated with the user, the synthetic personal data to one or more public sites (Bradshaw, Fig 3, Paragraphs 0060-0061, substitution step applying data masking and preserving an authentic look and feel for the obfuscated user data, wherein the substitution step includes substituting existing values in the extracted features of the user data with authentic looking values. User data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file. Paragraphs 0063-0064, shuffling data along with substituting data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Bradshaw in order to further modify the method of obfuscating content based on requesting user roles from the teachings of Chen with the method of machine learning with feature obfuscation from the teachings of Bradshaw.
One of ordinary skill in the art would have been motivated because training the machine learning model the system will be able to contain an updated version of the obfuscation process (Bradshaw – Paragraph 0022).
Regarding Claim 9, this claimed limitation is the same as the limitation addressed to Claim 2 above. Therefore, it is rejected under the same rationale.
Regarding Claim 10, this claimed limitation is the same as the limitation addressed to Claim 3 above. Therefore, it is rejected under the same rationale.
Regarding Claim 11, this claimed limitation is the same as the limitation addressed to Claim 4 above. Therefore, it is rejected under the same rationale.
Regarding Claim 12, this claimed limitation is the same as the limitation addressed to Claim 5 above. Therefore, it is rejected under the same rationale.
Regarding Claim 13, this claimed limitation is the same as the limitation addressed to Claim 6 above. Therefore, it is rejected under the same rationale.
Regarding Claim 14, this claimed limitation is the same as the limitation addressed to Claim 7 above. Therefore, it is rejected under the same rationale.
Regarding Claim 15, Chen discloses a non-transitory computer-readable medium storing instruction that, when executed by one or more processors, cause a computing device to:
monitor one or more public sites for publicly available personal data associated with a user (Chen, Paragraph 0018, content server extracts from the received content one or more name entities and/or one or more image face portions).
However, Chen fails to explicitly disclose generate, using a trained machine learning model, synthetic personal data associated with the user of a user device, wherein the synthetic personal data is configured to appear similar to verified personal data associated with the user, while being different; and disseminate, based on receiving approval to disseminate the synthetic personal data from the user device, the synthetic personal data to the one or more public sites.
Bradshaw, from the same or similar field of endeavor, discloses generate, using a trained machine learning model, synthetic personal data associated with the user of a user device, wherein the synthetic personal data is configured to appear similar to verified personal data associated with the user, while being different (Bradshaw, Paragraph 0011, obfuscation occurs at any one of the multiple computing devices that host different versions of the ANN. Paragraph 0023, training master version of the artificial neural network (ANN). Receiving data to train the master version of the ANN. Paragraphs 0034-0035, training the master version of the ANN based on received obfuscated data using machine learning. Generating obfuscated data based on extracted user data. Paragraph 0085, server performs functions);
and disseminate, based on receiving approval to disseminate the synthetic personal data from the user device, the synthetic personal data to the one or more public sites (Bradshaw, Fig 3, Paragraphs 0060-0061, substitution step applying data masking and preserving an authentic look and feel for the obfuscated user data, wherein the substitution step includes substituting existing values in the extracted features of the user data with authentic looking values. User data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file. Paragraphs 0063-0064, shuffling data along with substituting data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Bradshaw in order to further modify the method of obfuscating content based on requesting user roles from the teachings of Chen with the method of machine learning with feature obfuscation from the teachings of Bradshaw.
One of ordinary skill in the art would have been motivated because training the machine learning model the system will be able to contain an updated version of the obfuscation process (Bradshaw – Paragraph 0022).
Regarding Claim 16, this claimed limitation is the same as the limitation addressed to Claim 2 and Claim 9 above. Therefore, it is rejected under the same rationale.
Regarding Claim 17, this claimed limitation is the same as the limitation addressed to Claim 3 and Claim 10 above. Therefore, it is rejected under the same rationale.
Regarding Claim 18, the combination of Chen and Bradshaw disclose the non-transitory computer-readable medium of claim 15 above, where Bradshaw further discloses wherein the instructions, when executed by the one or more processors, cause the computing device to disseminate the synthetic personal data on the Internet by:
uploading the synthetic personal data to one or more public websites; posting the synthetic personal data to a social media website; publishing an article containing the synthetic personal data to a news website; or publishing a blog post that comprises the synthetic personal data (Bradshaw, Fig 3, Paragraphs 0060-0061, substitution step applying data masking and preserving an authentic look and feel for the obfuscated user data, wherein the substitution step includes substituting existing values in the extracted features of the user data with authentic looking values. User data includes customer records, personal identifiable information, and other types of sensitive information that is substituted with randomly or deliberately supplied false customer records, personal identifiable information, and the like, respectively, such as from a look up file. Paragraphs 0063-0064, shuffling data along with substituting data).
Regarding Claim 19, this claimed limitation is the same as the limitation addressed to Claim 6 and Claim 13 above. Therefore, it is rejected under the same rationale.
Regarding Claim 20, this claimed limitation is the same as the limitation addressed to Claim 7 and Claim 14 above. Therefore, it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. All the references listed on 892 are related to the subject matter of obfuscating personal data from internet using machine learning.
Some of the prior arts include:
US 20210133350 A1, which discloses a method of automated securing of sensitive personal data in data pipelines
US 11755848 B1, which discloses a method of processing structured and unstructured text to identify sensitive information.
US 20190034657 A1, which discloses a method of privacy detection during insider threat monitoring.
US 20210334401 A1, which discloses a method of privacy protection verification.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAVIER O GUZMAN whose telephone number is (571)270-0588. The examiner can normally be reached Monday - Friday 8 am to 4 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jorge L. Ortiz-Criado can be reached at (571)272-7624. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAVIER O GUZMAN/ Primary Examiner, Art Unit 2496
