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 The rejections have been withdrawn in light of allowed subject matter. Claims 7, 8 are cancelled.
Applicant's arguments filed Remarks, filed 07/17/2026 for DP rejection have been fully considered but they are 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 time does not want to file a e-TD and wants to have ‘control’ over the paperwork to file the e-TD at a later time. 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 6, 9 – 22 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, over claims 1-21 of US (allowed) application no. 17583795, over claims 1-27 of US (allowed) application no. 17583763. 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 – 6, 9 – 22 are anticipated by the above said issued application. Therefore, the corresponding dependent claims are also rejected for the same rationale.
Instant App. 18754457
Pending App. 17583687
Pending App. 17583795
Pending App. 17583763
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 encoded feature vectors produced from plaintext biometric information to at least one classification 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; and employ the first and second encryption keys to authenticate a user of the local device for access to the remote device; assign, at the local device, the unique candidate identifier to respective encoded feature vectors to return in response to a geometric evaluation; and use at least one local classification network trained to identify a respective match based on encoded feature vectors to return the label mappable to or encoded as the unique identifier.
2. (Original) The system of claim 1, further comprising at least one pre-trained embedding network 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 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.
6. (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.
7. (Cancelled)
8. (Cancelled)
9. (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.
10. (Original) 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 from the input of plaintext identifying information during the enrollment.
11. (Original) The system of claim 10, 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, an encryption key and unique identifier for an entity.
12. (Currently Amended) A computer implemented 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 encoded feature vectors produced from plaintext biometric information to at least one classification 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, by at least one processor, the first and second encryption keys to authenticate a user of the local device for access to the remote device assigning, at the local device, the unique candidate identifier to respective encoded feature vectors to return in response to a geometric evaluation; and using at least one local classification network trained to identify a respective match based on encoded feature vectors to return the label mappable to or encoded as the unique identifier.
13. (Original) The method of claim 12, 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.
14. (Original) The method of claim 13, 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.
15. (Currently Amended) The method of claim 12, wherein the method-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.
16. (Original) The method of claim 12, wherein the method comprises transforming the plaintext identification information into fully encrypted homomorphic encrypted feature vectors using the at least one pre-trained embedding network.
17. (Original) The method of claim 12, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
18. (Original) The method of claim 12, wherein the method comprises assigning, at the local device, a unique candidate identifier to respective encoded feature vectors to return in response to a geometric evaluation.
19. (Original) The method of claim 18, where the method comprises instantiating 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.
20. (Original) The method of claim 12, wherein the method comprises generating an identity profile and associating metadata information based on current device context and/or activity to a trained identity.
21. (Original) The method of claim 12, wherein method comprises defining a label for identifying an entity during an enrollment and associate the label with the generated encrypted feature vectors
from the input of plaintext identifying information during the enrollment.
22. (Original) The method of claim 21, wherein the method comprises generating the label to
define an identification environment, wherein generating the label is based on, at least, an
encryption key and unique identifier for an entity.
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.
1. (Original) 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;instantiate at least one classification network configured to:accept the encrypted feature vectors and label inputs to train the at least one classification network to recognize the encrypted features produced by the at least one pre-trained embedding network, andaccept the encrypted feature vectors and return a matching label to an identity or an unknown result during prediction;monitor device activity or content;capture plaintext identifying information embedded in the device activity or content;and communicate the plaintext identifying information to the at least one pre-trained embedding network as input; andassign a unique activity identifier to respective encrypted feature vectors generated from the communicated plaintext identifying information to return in response to geometric evaluation and for training the at least one classification network using the unique identifier as a respective label;responsive to matching the unique activity identifier display at least one function in a user interface, wherein the at least one function targets the unique activity identifier with an associated action.
2. (Original) The system of claim 1, wherein the at least one processor is configured to select from a plurality of actions and identify the at least one function based on a user device context.
3. (Original) The system of claim 2, wherein the at least one processor is configured to determine the user device context based on at least one of: a current application being executed, a current operations being executed, content being displayed, or content being accessed.
4. (Original) The system of claim 1, wherein the at least one associated action includes a search function configured to execute a search through digital activity and digital content for activity and content matching a unique identifier.
5. (Original) The system of claim 4, wherein the at least one processor is configured to display the unique identifier in association with content returned by the search through digital activity.
6. (Original) The system of claim 1, wherein the at least one processor is configured to generate a display separating content that uniquely matches the unique identifier and content that includes the identifier.
7. (Original) The system of claim 1, wherein the at least one associated action includes functions to block and/or deny subsequent activity associated with the unique identifier, and the at least one processor is configured to block content having the unique identifier in subsequent digital activity.
8. (Original) The system of claim 7, wherein the at least one processor is configured to:notify a current user of a block and/or deny status; andpresent options to allow content or activity associated with the unique identifier.
9. (Original) The system of claim 1, wherein the at least one associated action includes functions to assign as verified status to an actor or source associated with the unique identifier, and the at least one processor is configured to display a verified status for at least one subsequent content display showing the content associated with the verified unique identifier.
10. (Original) The system of claim 1, wherein the at least one associated action includes operations to add authorization for device usage, wherein the at least one processor is configured to link assigned privileges to a profile associated with the unique identifier.
11. (Original) A computer implemented method for private identity, the method comprising:instantiating, by at least one processor, at least one pre-trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information;instantiating, by the at least one processor, at least one classification network configured;accepting, by the at least one classification network, the encrypted feature vectors and label inputs to train the at least one classification network to recognize the encrypted features produced by the at least one pre-trained embedding network;accepting, by the at least one classification network, the encrypted feature vectors and return a matching label to an identity or an unknown result during prediction;monitoring, by the at least one processor, device activity or content;capturing, by the at least one processor, plaintext identifying information embedded in the device activity or content;communicating, by the at least one processor, the plaintext identifying information to the at least one pre-trained embedding network as input;assigning, by the at least one processor, a unique activity identifier to respective encrypted feature vectors generated from the communicated plaintext identifying information to return in response to geometric evaluation and for training the at least one classification network using the unique identifier as a respective label;displaying, by the at one processor, at least one function in a user interface responsive to matching the unique activity identifier, wherein the at least one function targets the unique activity identifier with an associated action.
12. (Original) The method of claim 11, wherein the method further comprises selecting from a plurality of actions and identify the at least one function based on a user device context.
13. (Original) The method of claim 12, wherein the method further comprises determining the user device context based on at least one of: a current application being executed, a current operations being executed, content being displayed, or content being accessed.
14. (Original) The method of claim 11, wherein the at least one associated action includes a search function configured to execute a search through digital activity and digital content for activity and content matching a unique identifier.
15. (Original) The method of claim 14, wherein the method further comprises displaying the unique identifier in association with content returned by the search through digital activity.
16. (Original) The method of claim 11, wherein the method further comprises generating a display separating content that uniquely matches the unique identifier and content that includes the identifier.
17. (Original) The method of claim 11, wherein the at least one associated action includes functions to block and/or deny subsequent activity associated with the unique identifier, and wherein the method further comprises blocking content having the unique identifier in subsequent digital activity.
18. (Original) The method of claim 17, wherein the method further comprises:notifying a current user of a block and/or deny status; andpresenting options to allow content or activity associated with the unique identifier.
19. (Original) The method of claim 11, wherein the at least one associated action includes functions to assign as verified status to an actor or source associated with the unique identifier, and wherein the method further comprises displaying a verified status for at least one subsequent content display showing the content associated with the verified unique identifier.
20. (Original) The method of claim 11, wherein the at least one associated action includes operations to add authorization for device usage, wherein the method further comprises linking assigned privileges to a profile associated with the unique identifier.
21. (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 encoded feature vectors produced from plaintext biometric information to at least one classification component 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; and use the first and second encryption keys to authenticate a user of the local device for access to the remote device, and permit a function or operation requiring authentication.
22. (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 processing of encoded feature vectors produced from plaintext biometric information by at least one classification component 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.
23. (New) The method of claim 21, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
24. (New) The system of claim 22, wherein the plaintext identifying information includes at least one of: biometric identifying information, behavioral identifying information, or physiologic identifying information.
25. (New) The method of claim 22, further comprising using the first and second encryption keys to authenticate and/or identify a user of the local device for functionality performed at the remote device, and permitting a function or operation requiring authentication.
1. (Original) 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;instantiate at least one classification network configured to:accept the encrypted feature vectors and label inputs to train the at least one classification network to recognize the encrypted feature vectors produced by the at least one pre-trained embedding network for a plurality of identification classes, and accept the encrypted feature vectors and return a matching label to an identity or an unknown result during prediction;assign a unique 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 identifier as a respective label; andmonitor device activity or content on a user device;capture plaintext identifying information embedded in the device activity or the content; andcommunicate the plaintext identifying information to the at least one pre-trained embedding network as input to produce encrypted feature vectors for identification.
2. (Original) The system of claim 1, wherein the at least one processor is further configured to generate an activity profile associated with the unique identifier based on information associated with the device activity or the content.
3. (Original) The system of claim 2, wherein the device activity or content includes an active voice call and the unique identifier is associated with a speaker in the active voice call.
4. (Original) The system of claim 2, wherein the device activity or content includes an active video conference and the unique identifier is associated with a video conference participant.
5. (Original) The system of claim 1, wherein the at least one processor is further configured to instantiate at least one helper network configured to isolate plaintext identifying information associated with an entity from the plaintext identifying information embedded in the device activity or content.
6. (Original) The system of claim 5, wherein the at least one processor is further configured to instantiate at least a second helper network configured to validate the plaintext identifying information as a good sample of identifying information.
7. (Original) The system of claim 1, wherein the at least one processor is further configured to return an identity responsive to geometric matching executed on encrypted feature vectors generated from the plaintext identifying information against at least one stored encrypted feature vector.
8. (Original) The system of claim 7, 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.
9. (Original) The system of claim 1, wherein the at least one processor is further configured to access stored content associated with the user device and capture any plaintext identifying information for evaluating identity.
10. (Original) The system of claim 1, wherein the at least one processor is further configured to communicate at least one of: encrypted feature vectors, unique identifiers, or trained classification networks to a remote identification service.
11. (Original) The system of claim 11, wherein the remote identification service is configured to execute geometric evaluation and execute prediction by at least one remote classification network, on the encrypted feature vectors to identify an entity associated with any plaintext identifying information.
12. (Original) The system of claim 12, wherein the remote identification service is configured to merge unique identifiers generated from a plurality of devices based on matching respective encrypted feature vectors.
13. (Original) The system of claim 13, wherein the remote identification service is configured to update the unique identifier at the user device.
14. (Original) A computer implement method for private identity, the method comprising:instantiating, by at least one processor, at least one pre-trained embedding network configured to generate encrypted feature vectors from an input of plaintext identifying information;instantiating, by the at least one processor, at least one classification network;accepting, by the at least one classification network, the encrypted feature vectors and label inputs to train the at least one classification network to recognize the encrypted feature vectors produced by the at least one pre-trained embedding network for a plurality of identification classes;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 at least one processor, a unique 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 identifier as a respective label;monitoring, by the at least one processor, device activity or content on a user device;capturing, by the at least one processor, plaintext identifying information embedded in the device activity or the content; andcommunicating, by the at least one processor, the plaintext identifying information to the at least one pre-trained embedding network as input to produce encrypted feature vectors for identification.
15. (Original) The method of claim 14, wherein the method further comprises generating an activity profile associated with the unique identifier based on information associated with the device activity or the content.
16. (Original) The method of claim 15, wherein the device activity or content includes an active voice call and the unique identifier is associated with a speaker in the active voice call.
17. (Original) The method of claim 15, wherein the device activity or content includes an active video conference and the unique identifier is associated with a video conference participant.
18. (Original) The method of claim 14, wherein the method further comprises instantiating at least one helper network configured to isolate plaintext identifying information associated with an entity from the plaintext identifying information embedded in the device activity or content.
19. (Original) The method of claim 18, wherein the method further comprises instantiating at least a second helper network configured to validate the plaintext identifying information as a good sample of identifying information.
20. (Original) The method of claim 14, wherein the method further comprises returning an identity responsive to geometric matching executed on encrypted feature vectors generated from the plaintext identifying information against at least one stored encrypted feature vector.
21. (Original) The method of claim 20, wherein the method further comprises communicating at least one encrypted feature for prediction by the at least one classification network responsive to generating an unknown result from the geometric match.
22. (Original) The method of claim 15, wherein the method further comprises accessing stored content associated with the user device and capture any plaintext identifying information for evaluating identity.
23. (Original) The method of claim 22, wherein the method further comprises communicating at least one of: encrypted feature vectors, unique identifiers, or trained classification networks to a remote identification service.
24. (Original) The method of claim 23, wherein the method further comprises executing, by the remote identification service, geometric evaluation and executing prediction by at least one remote classification network, on the encrypted feature vectors to identify an entity associated with any plaintext identifying information.
25. (Original) The method of claim 24, wherein the method further comprises merging, by the remote identification service, unique identifiers generated from a plurality of devices based on matching respective encrypted feature vectors.
26. (Original) The method of claim 25, wherein the method further comprises updating, by the remote identification service, the unique identifier at the user device.
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 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 the unique identifier with a first and second encryption key;retrieve the label mappable to the 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 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. (New) A method for private identity authentication, the method comprising:instantiating, by at least one processor operatively connected to a memory, at least one pre- trained embedding network configured, at least in part, to generate identification information for identification or authentication responsive to an input of plaintext identifying information;generating, by the at least one processor, a label mappable to a unique identifier for a respective entity in response to the input of plaintext biometric information to the at least one pre- trained embedding network stored on a local device;associating, by the at least one processor, the label mappable to the unique identifier with a first and second encryption key;retrieving, by the at least one processor, the label mappable to the unique identifier responsive to matching executed on the identification information generated, at least in part, froman input of plaintext identifying information for the entity to the at least one pre-trained embedding network and the matching executed on stored identification information generated at least in part from an input of plaintext identifying information for the entity to the at least one pre-trained embedding network;retrieving, by the at least one processor, 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.
Allowable Subject Matter
Claims 1-6 and 9-22 are allowed pending the DP rejection. The reason for allowance is that 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 encoded feature vectors produced from plaintext biometric information to at least one classification network stored on the local device; employ the first and second encryption keys to authenticate a user of the local device for access to the remote device; assign, at the local device, the unique candidate identifier to respective encoded feature vectors to return in response to a geometric evaluation; and use at least one local classification network trained to identify a respective match based on encoded feature vectors to return the label mappable to or encoded as the unique identifier.
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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