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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments, see Remarks, filed 07/17/2026, with respect to rejection under 103 have been fully considered and are persuasive. The allowable subject matter has been included in the claims and hence the said rejection(s) has been withdrawn. Claims 11 – 13 are cancelled.
Applicant’s arguments, see Remarks, filed 07/17/2026, with respect to rejection under DP and not persuasive. The attorney was clearly explained about the DP rejection during an interview and the attorney understood the same. The attorney also indicated that the client at this does not want to file a e-TD and wants to have ‘control’ over the paperwork to file the e-TD. Hence, the rejection is maintained.
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 – 10, 12 – 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7-18, 20 – 27 of U.S. (allowed) application no. 17583687. Although the claims at issue are not identical, they are not patentably distinct from each other because although the claims at issue are not identical, they are not patentably distinct from each other because application claims 1 – 10, 12 – 21 are anticipated by the above said issued application. Therefore, the corresponding dependent claims are also rejected for the same rationale.
Instant App. 18754422
Pending App. 17583687
1. (Currently Amended) A private identity system, the system comprising:
at least one processor operatively connected to a memory, the at least one processor configured to: associate a unique identifier with a first and second encryption key; generate at a local device a label mappable to or encoded as the unique identifier in response to input of plaintext biometric information to at least one pre-trained embedding network stored on the local device; communicate the unique identifier from the local device to a remote device; retrieve, at the local device, a respective key of the first and second encryption keys based on, at least in part, the unique identifier; retrieve, at the remote device, an associated key of the first and second encryption key based on, at least in part, the unique identifier; employ the first and second encryption keys to authenticate a user of the local device for access to the remote device; define a label for identifying an entity during an enrollment and associate the label with the generated encrypted feature vectors produced from the input of plaintext identifying information during the enrollment; and wherein the definition of the label includes operations to generate the label to include an identification environment. wherein the generation of the label is based, at least in part, on an encryption key and the unique identifier for an entity.
2. (Original) The system of claim 1, wherein the at least one pre-trained embedding network is
configured to generate fully private encoded feature vectors that are one-way homomorphic encryptions of the input plaintext biometric.
3. (Original) The system of claim 2, wherein the label mappable to or encoded as the unique identifier is returned based on a geometric evaluation of the generated fully private encoded feature vectors against enrolled fully private encoded feature vectors.
4. (Original) The system of claim 2, wherein the fully private encoded feature vectors are
processed as an input to a classification network to predict a match to the label representing an enrolled identity for an entity.
5. (Original) The system of claim 4, wherein the system further comprises a classification network configured to accept fully encrypted homomorphic encrypted feature vectors and output the label responsive to predicting a match to an enrolled entity.
6. (Original) The system of claim 1, wherein the at least one pre-trained embedding network is configured to transform the plaintext identification information into fully encrypted homomorphic encrypted feature vectors.
7. (Original) The system of claim 1, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic
identifying information.
8. (Original) The system of claim 1, wherein the at least one processor is further configured to
assign, at the local device, a unique candidate identifier to respective encoded feature vectors to return in response to geometric evaluation.
9. (Original) The system of claim 8, further comprising at least one local classification network
trained to identify a respective match based on encoded feature vectors and return the unique candidate identifier as a respective label.
10. (Original) The system of claim 1, wherein the at least one processor is further configured to generate an identity profile and associate metadata information based on current device context and/or activity to a trained identity.
11. (Cancelled)
12. (Cancelled)
13. (Cancelled)
14. (Currently Amended) A method for managing private identity, the method comprising: associating, by at least one processor, a unique identifier with a first and second encryption key; generating at a local device a label mappable to or encoded as the unique identifier in response to input of plaintext biometric information to at least one pre-trained embedding network stored on the local device; communicating the unique identifier from the local device to a remote device; retrieving, at the local device, a respective key of the first and second encryption keys based on, at least in part, the unique identifier; retrieving, at the remote device, an associated key of the first and second encryption key based on, at least in part, the unique identifier; and employing the first and second encryption keys to authenticate a user of the local device for access to the remote device; defining the label for identifying an entity during an enrollment and associating the label with the generated encrypted feature vectors produced from the input of plaintext identifying information during the enrollment; and wherein the defining of the label includes generating the label based, at least in part, on an encryption key and the unique identifier for an entity, and specifying an identification environment.
15. (Original) The method of claim 14, wherein the method comprises generating fully private encoded feature vectors that are one-way homomorphic encryptions of the input plaintext biometric using the at least one pre-trained embedding network.
16. (Original) The method of claim 15, wherein the method comprises returning the label mappable to or encoded as the unique identifier based on a geometric evaluation of the generated fully private encoded feature vectors against enrolled fully private encoded feature vectors.
17. (Original) The method of claim 15, wherein the method comprises processing the fully private encoded feature vectors as an input to a classification network to predict a match to the label
representing an enrolled identity for an entity.
18. (Original) The method of claim 17, wherein the method further comprises instantiating a classification network configured to accept fully encrypted homomorphic encrypted feature vectors
and output the label responsive to predicting a match to an enrolled entity.
19. (Original) The method of claim 14, wherein the method comprises transforming the plaintext
identification information into fully encrypted homomorphic encrypted feature vectors with the at least one pre-trained embedding network.
20. (Original) The method of claim 14, wherein the method comprises assigning, by the at least one processor a unique candidate identifier to respective encoded feature vectors to return in response
to geometric evaluation.
21. (Original) The method of claim 21, further comprising at least one local classification network trained to identify a respective match based on encoded feature vectors and return the unique
candidate identifier as a respective label.
1. (Previously presented) A private identity system, the system comprising: at least one processor operatively connected to a memory, the at least one processor configured to:instantiate, at a local device or a remote device, at least one pre-trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information;instantiate, at the local device or the remote device, at least one local classification network configured to:accept the encrypted feature vectors and return a matching label to an identity or an unknown result during prediction;assign, at the local or the remote device, a unique identifier to respective encrypted feature vectors for training the at least one classification network using the unique identifier as a respective label;manage the at least one classification network to output matching labels responsive to input of associated encrypted feature vectors; andwherein the at least one processor is further configured to generate an entity identity responsive to geometric matching executed on encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network and executed on stored encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network.
2. (Original) The system of claim 1, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
3. (Previously presented) The system of claim 1, wherein the at least one processor is further configured to assign, at the local or remote device, a unique candidate identifier to respective encrypted feature vectors to return in response to geometric evaluation and for training the at least one local classification network using the unique candidate identifier as a respective label.
4. (Previously presented) The system of claim 1, wherein the at least one processor is further configured to reconcile entity identification by the at least one classification network on respective local or remote devices such that the at least one classification network and any geometric evaluation returns the same identity in response to processing of encrypted feature vectors associated with the same entity.
5. (Original) The system of claim 1, wherein the at least one processor is further configured to generate an identity profile and associate metadata information based on current device context and/or activity to a trained identity.
6. (Cancelled)
7. (Previously presented) The system of claim 1, wherein the at least one processor is further configured to store the generated encrypted feature vectors, generated at least in part from the input of plaintext identifying information, for use in subsequent geometric matching responsive to a positive match from geometric matching and by a classification network.
8. (Previously presented) The system of claim 7, wherein the at least one processor is further configured to trigger training of the at least one classification network on a local device responsive to storing of a threshold number of encrypted feature vectors.
9. (Previously presented) The system of claim 1, wherein the at least one processor is further configured to define a label for identifying an entity during an enrollment and associate the label with the generated encrypted feature vectors generated, at least in part, from the input of plaintext identifying information during the enrollment.
10. (Previously presented) The system of claim 9, wherein the at least one processor is further configured to:generate the label to define an identification environment, wherein generation of the label is based on at least in part an encryption key and unique identifier for an entity.
11. (Previously presented) The system of claim 1, wherein the at least one processor is further configured to communicate at least one encrypted feature for prediction by the at least one classification network responsive to generating an unknown result from the geometric match.
12. (Previously presented) The system of claim 11, wherein the at least one processor is further configured to request remote identification responsive to an unknown result returned by local geometric match or local prediction by the at least one classification network.
13. (Previously presented) The system of claim 12, wherein the at least one processor is further configured to return a user identifier and at least one encrypted feature vector in response to a successful remote match by either a remote geometric match or a remote prediction by the at least one classification network.
14. (Previously presented) A computer implemented method for private identity, the method comprising:instantiating, by at least one processor at a local device or a remote device, at least one pre- trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information; instantiating, by the least one processor at the local device or the remote device, at least one local classification network;accepting, by the at least one classification network, the encrypted feature vectors and returning a matching label to an identity or an unknown result during prediction;assigning, by the least one processor at the local device or the remote device, a unique identifier to respective encrypted feature vectors for training the at least one classification network using the unique identifier as a respective label; andmanaging the at least one classification network to output matching labels responsive to input of associated encrypted feature vectors.
15. (Original) The method of claim 14, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
16. (Previously presented) The method of claim 14, wherein the method further comprises assigning, at the local device or the remote device, a unique candidate identifier to respective encrypted feature vectors to return in response to geometric evaluation and for training the at least one classification network using the unique candidate identifier as a respective label.
17. (Previously presented) The method of claim 14, wherein the method further comprises reconciling entity identification by the at least one classification network instantiated at the remote or local device such that the at least one classification network and any geometric evaluation returns the same identity in response to processing of encrypted feature vectors associated with the same entity on the local device or the remote device.
18. (Previously presented) The method of claim 14, wherein the method further comprises generating an identity profile and associating metadata information based on current device context and/or activity to a trained identity.
19. (Cancelled)
20. (Previously presented) The method of claim 19, wherein the method further comprises storing the generated encrypted feature vectors, generated at least in part from the input of plaintext identifying information, for use in subsequent geometric matching responsive to a positive match from geometric matching and by a classification network.
21. (Previously presented) The method of claim 20, wherein the method further comprises triggering training of the at least one classification network responsive to storing of a threshold number of encrypted feature vectors.
22. (Previously presented) The method of claim 14, wherein the method further comprises defining a label for identifying an entity during an enrollment and associating the label with the generated encrypted feature vectors, generated at least in part, from the input of plaintext identifying information during the enrollment.
23. (Previously presented) The method of claim 22, wherein the method further comprises generating the label to define an identification environment, wherein generation of the label is based on at least in part an encryption key and unique identifier for an entity.
24. (Previously presented) The method of claim 1, wherein the method further comprises communicating at least one encrypted feature vector for prediction by the at least one classification network responsive to generating an unknown result from the geometric match.
25. (Previously presented) The method of claim 24, wherein the method further comprises requesting remote identification responsive to an unknown result returned by local geometric match or local prediction by the at least one classification network.
26. (Previously presented) The method of claim 25, wherein the method further comprises returning a user identifier and at least one encrypted feature vector in response to a successful remote match by either a remote geometric match or a remote prediction by the at least one classification network.
27. (Previously Presented) A private identity system, the system comprising:at least one processor operatively connected to a memory, the at least one processor configured to:instantiate at least one pre-trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information;generate a label mappable to or encoded as a unique identifier for a respective entity in response to input of plaintext biometric information to the at least one pre-trained embedding network stored on the local device;associate the label mappable to or encoded as the unique identifier with a first and second encryption key;retrieve the label mappable to or encoded as a unique identifier responsive to geometric matching executed on encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network and the matching also executed on stored encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network;retrieve a respective key of the first and second encryption keys based on, at least in part, the label mappable to or encoded as a unique identifier;retrieve, at another device, an associated key of the first and second encryption key based on, at least in part, the unique identifier; andemploy the first and second encryption keys to authenticate a user to execute secure functionality.
28. (Previously Presented) A method for private identity, the method comprising: instantiating, by at least one processor operatively connected to a memory, at least one pre- trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information;generating a label mappable to or encoded as a unique identifier for a respective entity in response to input of plaintext biometric information to the at least one pre-trained embedding network stored on the local device;associating the label mappable to or encoded as the unique identifier with a first and second encryption key;retrieving the label mappable to or encoded as a unique identifier;retrieving a respective key of the first and second encryption keys based on, at least in part, the label mappable to or encoded as a unique identifier;retrieving, at another device, an associated key of the first and second encryption key based on, at least in part, the unique identifier; andemploying the first and second encryption keys to authenticate a user to execute secure functionality.
29. (Previously Presented) The method of claim 28, wherein the act of retrieving the label mappable to or encoded as the unique identifier is executed responsive to geometric matching executed on encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network and the matching also executed on stored encrypted feature vectors generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network.
30. (New) The system of claim 29, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
Allowable Subject Matter
The claims 1 – 10, 14 – 21 are allowed pending DP rejection, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
The following is a statement of reasons for the indication of allowable subject matter: None of the prior arts teach: generate at a local device a label mappable to or encoded as the unique identifier in response to input of plaintext biometric information to at least one pre-trained embedding network stored on the local device; define a label for identifying an entity during an enrollment and associate the label with the generated encrypted feature vectors produced from the input of plaintext identifying information during the enrollment; and wherein the definition of the label includes operations to generate the label to include an identification environment. wherein the generation of the label is based, at least in part, on an encryption key and the unique identifier for an entity.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Badri Champakesan whose telephone number is (571)270-3867. The examiner can normally be reached M-F: 8.30am-4.30pm (EST). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/BADRINARAYANAN /Primary Examiner, Art Unit 2494.
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