DETAILED ACTION
This is in response to applicant's communication filed on 09/15/2025, wherein:
Claim 1-4, 6-14, and 16-20 are pending.
Claim 5, and 15 are cancelled.
Response to Arguments
Terminal disclaimer filed on 09/15/2025 have not overcome the double patenting rejection for US 9594791 B2 and US 10866937 B2.
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.
Claim 1-4, 6-14, and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4, 6-11, and 14 of U.S. Patent No. US 9594791 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because their scope are overlapped.
Claim 1: an apparatus comprising: a processor configured to acquire computer readable instructions stored in one or more memory devices and execute the instructions to:
process a time-series of location data points for a target entity, wherein the time- series of location data points are received from a computing device associated with the target entity (claim 1 - “process a time-series of location data points for a target entity, wherein the time-series of location data points are received from the computing device”);
determine one or more sessions from the time-series of location data points by grouping one or more of the time-series of location data points (claim 6 – “determine one or more sessions from the time-series of the location data points by grouping one or more of the time-series of the location data points that are bounded in space and/or time”);
determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions (claim 6 – ”determine one or more clusters based, at least in part, on the one or more sessions based, at least in part, on a physical proximity between sessions”);
determine one or more attributes associated with the target entity based on one or more of, the time-series of location data points, the one or more sessions, and the one or more clusters (claim 1 – “determine one or more attributes associated with the target entity based, at least in part, on the time-series of location data points and on the ranking of the patterns of movement of the target entity”, claim 6 – “determine the one or more attributes associated with the target entity based, at least in part, on the one or more sessions and the one or more clusters”); and
generate a profile of the target entity based on the one or more attributes associated with the target entity (claim 1 - “generate a profile of the target entity based, at least in part, on the one or more attributes associated with the target entity”).
Claim 2: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine an accuracy of the time-series of the location data points, and discard, based on the determined accuracy, one or more of the location data points in the time-series of the location data points (claim 2 – “wherein the processor is to further determine an accuracy of the time-series of the location data points, and discard, based, at least in part, on the determined accuracy, one or more of the location data points in the time-series of the location data points”).
Claim 3: The apparatus of claim 2, wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points based on a time-series of the location data points associated with other target entities (claim 3 – “wherein the processor is to further determine the accuracy of the time-series of the location data points based, at least in part, on a time-series of the location data points associated with other target entities”).
Claim 4: The apparatus of claim 3, wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points at a particular time instance based on location information associated with the other target entities at the particular time instance (claim 4 - “wherein the processor is to further determine the accuracy of the time-series of the location data points at a particular time instance based, at least in part, on location information associated with the other target entities at the particular time instance”).
Claim 6: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to associate at least one of the location data points, the sessions, or the clusters of sessions with annotation information associated with a geographical location of the location data points, sessions, or clusters of sessions, and use the annotation information to determine the one or more attributes associated with the target entity (claim 7 – “wherein the processor is to further associate at least one of the location data points, the sessions, or the clusters with annotation information associated with a geographical location of the location data points, sessions, or clusters, and use the annotation information to determine the one or more attributes associated with the target entity”).
Claim 7: The apparatus of claim 6, wherein the processor is further configured to execute the instructions to determine the one or more attributes associated with the target entity based on movements of the target entity between two or more clusters of sessions (claim 8 – “wherein the processor is to further determine the one or more attributes associated with the target entity based, at least in part, on movements of the target entity between two or more clusters.”).
Claim 8: The apparatus of claim 7, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, statistical measures on the movements of the target entity and the annotation information associated with the target entity (claim 9 - “wherein the processor is to further determine a home location attribute based on, at least in part, statistical measures on the movements of the target entity and the annotation information associated with the target entity”).
Claim 9: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, a likelihood that a particular location is associated with a residence (claim 10 – ”wherein the processor is to further determine a home location attribute based on, at least in part, a likelihood that a particular location is associated with a residence”).
Claim 10: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, timestamps of the location data points associated with the target entity (claim 11 – “wherein the processor is to further determine a home location attribute based on, at least in part, timestamps of the location data points associated with the target entity”)
Claim 11: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a predictive model based on the one or more attributes, wherein the predictive model is configured to predict a behavior of the target entity in a future (claim 14 – “wherein the processor is to further determine a predictive model based, at least in part, on the one or more attributes, wherein the predictive model is configured to predict a behavior of the target entity in a future”).
Claim 12: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to rank patterns of movement of the target entity, and determine the one or more attributes associated with the target entity based on the ranking of the patterns of movement of the target entity (claim 1 – “identifying and ranking patterns of movement of the target entity between individual ones of the plurality of areas of the activity; determine one or more attributes associated with the target entity based, at least in part, on the time-series of location data points and on the ranking of the patterns of movement of the target entity”).
Claim 13: the scope and content of the claim recites a method performed by the apparatus of claim 1, therefore, being addressed as in claim 1.
Claim 14: the scope and content of the claim recites a method performed by the apparatus of claim 2, therefore, being addressed as in claim 2.
Claim 16: the scope and content of the claim recites a method performed by the apparatus of claim 6, therefore, being addressed as in claim 6.
Claim 17: The method of claim 13, further comprising ranking patterns of movement of the target entity, and generating, by the first computing device, the at least one prediction of a future estimated location of the target entity based on a ranking of the patterns of movement of the target entity (claim 1 – “identifying and ranking patterns of movement of the target entity between individual ones of the plurality of areas of the activity; determine one or more attributes associated with the target entity based, at least in part, on the time-series of location data points and on the ranking of the patterns of movement of the target entity; generate a profile of the target entity based, at least in part, on the one or more attributes associated with the target entity, wherein the profile is to indicate at least one prediction of a future estimated location of the target entity within a specified time range”).
Claim 18: The scope and content of the claim recites a non-transitory computer readable medium having executable instructions executable for the apparatus of claim 1, therefore, being addressed as in claim 1.
Claim 19: The scope and content of the claim recites a non-transitory computer readable medium having executable instructions executable for the apparatus of claim 2, therefore, being addressed as in claim 2.
Claim 20: The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions (claim 6 –”determine one or more clusters based, at least in part, on the one or more sessions based, at least in part, on a physical proximity between sessions”); and
determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions (claim 1 – “determine one or more attributes associated with the target entity based, at least in part, on the time-series of location data points and on the ranking of the patterns of movement of the target entity”, claim 6 – “determine the one or more attributes associated with the target entity based, at least in part, on the one or more sessions and the one or more clusters”).
Claim 1-4, 6-14, and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 of U.S. Patent No. US 10866937 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because their scope are overlapped.
Claim 1: An apparatus comprising: a processor configured to acquire computer readable instructions stored in one or more memory devices and execute the instructions to:
process a time-series of location data points for a target entity, wherein the time- series of location data points are received from a computing device associated with the target entity (claim 1 – “process a time-series of location data points for a target entity, wherein the time-series of location data points are received from a computing device associated with the target entity”);
determine one or more sessions from the time-series of location data points by grouping one or more of the time-series of location data points (claim 1 – “determine one or more sessions from the time-series of location data points by grouping one or more of the time-series of location data points”);
determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions (claim 5 – “determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions”);
determine one or more attributes associated with the target entity based on one or more of, the time-series of location data points, the one or more sessions, and the one or more clusters (claim 1 – “determine one or more attributes associated with the target entity based on one or more of: the time-series of location data points, and the one or more sessions”, claim 5 – “determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions; and determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions”); and
generate a profile of the target entity based on the one or more attributes associated with the target entity (claim 1 – “generate a profile of the target entity based on the one or more attributes associated with the target entity”).
Claim 2: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine an accuracy of the time-series of the location data points, and discard, based on the determined accuracy, one or more of the location data points in the time-series of the location data points (claim 2 – “wherein the processor is further configured to execute the instructions to determine an accuracy of the time-series of the location data points, and discard, based on the determined accuracy, one or more of the location data points in the time-series of the location data points”).
Claim 3: The apparatus of claim 2, wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points based on a time-series of the location data points associated with other target entities (claim 3 – “wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points based on a time-series of the location data points associated with other target entities”).
Claim 4: The apparatus of claim 3, wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points at a particular time instance based on location information associated with the other target entities at the particular time instance (claim 4 – “wherein the processor is further configured to execute the instructions to determine the accuracy of the time-series of the location data points at a particular time instance based on location information associated with the other target entities at the particular time instance”).
Claim 6: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to associate at least one of the location data points, the sessions, or the clusters of sessions with annotation information associated with a geographical location of the location data points, sessions, or clusters of sessions, and use the annotation information to determine the one or more attributes associated with the target entity (claim 6 – “wherein the processor is further configured to execute the instructions to associate at least one of the location data points, the sessions, or the clusters of sessions with annotation information associated with a geographical location of the location data points, sessions, or clusters of sessions, and use the annotation information to determine the one or more attributes associated with the target entity”).
Claim 7: The apparatus of claim 6, wherein the processor is further configured to execute the instructions to determine the one or more attributes associated with the target entity based on movements of the target entity between two or more clusters of sessions (claim 7 – “wherein the processor is further configured to execute the instructions to determine the one or more attributes associated with the target entity based on movements of the target entity between two or more clusters of sessions”).
Claim 8: The apparatus of claim 7, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, statistical measures on the movements of the target entity and the annotation information associated with the target entity (claim 8 – “wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, statistical measures on the movements of the target entity and the annotation information associated with the target entity”).
Claim 9: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, a likelihood that a particular location is associated with a residence (claim 9 – “wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, a likelihood that a particular location is associated with a residence”).
Claim 10: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, timestamps of the location data points associated with the target entity (claim 10 – “wherein the processor is further configured to execute the instructions to determine a home location attribute based on, at least in part, timestamps of the location data points associated with the target entity”).
Claim 11: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to determine a predictive model based on the one or more attributes, wherein the predictive model is configured to predict a behavior of the target entity in a future (claim 11 – “wherein the processor is further configured to execute the instructions to determine a predictive model based on the one or more attributes, wherein the predictive model is configured to predict a behavior of the target entity in a future”).
Claim 12: The apparatus of claim 1, wherein the processor is further configured to execute the instructions to rank patterns of movement of the target entity, and determine the one or more attributes associated with the target entity based on the ranking of the patterns of movement of the target entity (claim 12 – “wherein the processor is further configured to execute the instructions to rank patterns of movement of the target entity, and determine the one or more attributes associated with the target entity based on the ranking of the patterns of movement of the target entity”).
Claim 13: the scope and content of the claim recites a method performed by the apparatus of claim 1, therefore, being addressed as in claim 1.
Claim 14: the scope and content of the claim recites a method performed by the apparatus of claim 2, therefore, being addressed as in claim 2.
Claim 16: the scope and content of the claim recites a method performed by the apparatus of claim 6, therefore, being addressed as in claim 6.
Claim 17: The method of claim 13, further comprising ranking patterns of movement of the target entity, and generating, by the first computing device, the at least one prediction of a future estimated location of the target entity based on a ranking of the patterns of movement of the target entity (claim 17 – “ranking, by the first computing device, patterns of movement of the target entity, and generating, by the first computing device, the at least one prediction of a future estimated location of the target entity based on a ranking of the patterns of movement of the target entity”).
Claim 18: The scope and content of the claim recites a non-transitory computer readable medium having executable instructions executable for the apparatus of claim 1, therefore, being addressed as in claim 1.
Claim 19: The scope and content of the claim recites a non-transitory computer readable medium having executable instructions executable for the apparatus of claim 2, therefore, being addressed as in claim 2.
Claim 20: The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to: determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions; and determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions (claim 20 – “determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions; and determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions”).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNG HONG whose telephone number is (571)270-7928. The examiner can normally be reached on Monday-Friday from 8:00 am to 5:00 pm.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JINSONG HU, can be reached on (571) 272-3965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DUNG HONG/
Primary Examiner, Art Unit 2643
Prosecution Insights