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 .
Status of Application
This final office action is in response to Applicant’s amendment received by the Office on 03-DEC-2025. Claims 1-20 have been presented in the application. Claims 1-20 are amended. The amendments have been entered. Accordingly, pending claims 1-20 are addressed herein.
Response to Arguments
Applicant’s arguments, filed 03-DEC-2025, with respect to the rejections of independent claims 1 and 11 under 102 have been fully considered. The amendments change the scope of the claims and a new rejection has been made in view of Wang (US 20230045727 A1) and Chai et al. (US 2021/0001897 A1). Applicant’s amendments overcome the 101 rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations is/are: "logistics engine..." of claim 1.
Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The applicant’s specification states, (Paragraph [17-18] “machine learning models… In this example, a model such as a logistics engine 116 is illustrated”).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230045727 A1) and Chai et al. (US 2021/0001897 A1).
Regarding claim 1, Wang teaches: A method comprising: determining, automatically by a logistics engine (element 104) executing on one or more hardware processors (Figure 13, 14; “Processor”) and using real-time sensor data and operational logs generated by mobile entities operating in a domain (Paragraph [54], "The underlying SDK or the underlying application may obtain the vehicle trajectory data acquired by the sensor of the vehicle in real time, to report the vehicle trajectory data to the server in real time or at a specified time"), a context of a mobile entity operating in a domain (Figure 1, 2; element S202), the context including a current task performed by the mobile entity (Paragraph [56], "a travel scenario corresponding to going to work, going off work, shopping, and touring"), the current task being identified from timestamped operational logs, manifests (Paragraph [58], "For example, based on the travel time, the travel route…the general information of the target travel scenario is determined to be going to work"), or monitoring data indicating start and end times of tasks (Paragraph [58], "For example, based on ... the travel starting point, and the travel end point…the general information of the target travel scenario is determined to be going to work"); generating, by the logistics engine, a database of task-bound rich trajectories by segmenting historical trajectories of multiple mobile entities into sub-trajectories (Paragraph [84], "In this embodiment, trajectory matching is performed on the current vehicle trajectory data and the each piece of historical vehicle trajectory data in the historical vehicle trajectory data set to obtain a plurality of matching degrees") defined as sequences of timestamped position collections between successive recorded positions (Paragraph [58], "and historical vehicle trajectory data B corresponds to a travel time from 3:10 to 3:35"), each sub- trajectory including aggregated sensor attributes (Paragraph [54], "The underlying SDK or the underlying application may obtain the vehicle trajectory data acquired by the sensor of the vehicle in real time, to report the vehicle trajectory data to the server in real time or at a specified time"), associating each sub-trajectory with a corresponding task in the operational logs (Paragraph [56], "for example, a travel scenario corresponding to going to work, going off work, shopping, and touring"), and storing the sub-trajectories as context-annotated nodes (Paragraph [50], "The vehicle trajectory data includes sampling data of at least one sampling point. The sampling data includes at least one of a sampling position"); filtering, based on the determined context, the database of task-bound rich trajectories (Figure 3; element S302) to identify candidate trajectories that are each associated with the same context as the mobile entity (Figure 3; element S304); and determining a similar trajectory from the candidate trajectories (Figure 3; "maximum matching degree"), wherein a similarity score for each of the candidate trajectories accounts for attribute importances (Paragraph [58], "Perform feature extraction on historical vehicle trajectory data in the same cluster to obtain travel feature information corresponding to each cluster"), wherein the similar trajectory has a highest similarity score, wherein the similar trajectory is considered as an expected trajectory for the mobile entity (Figure 3; element S304); and issuing, by the logistics engine, a control instruction to the mobile entity (Figure 2, 3; element S210, S306) or to a supervisory control system to adjust movement of the mobile entity in accordance with the expected trajectory (Figure 9)
While Wang teaches the limitations as stated above, it does not expressly teach:
determined by a machine-learning model
However, Chai et al. teaches: determined by a machine-learning model (Paragraph [22], "a vehicle, e.g., an autonomous or semi-autonomous vehicle, can use a trained machine learning model, referred to in this specification as a “trajectory prediction system,”")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data of Wang, to include the use of a machine-learning model, as taught by Chai et al. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model.
Regarding claim 2, Wang teaches: The method of claim 1, wherein the context of the mobile entity further comprises automatically parsing timestamped operational logs to identify a task identifier (Paragraph [58], "...For example, based on the travel time, the travel route, the travel starting point, and the travel end point of the target travel scenario, the general information of the target travel scenario is determined to be going to work.") associated with the mobile entity within a defined time window (Paragraph [58], "...In this case, the travel time from 3:00 to 3:35 may be used as description information of the target travel scenario for describing the travel time. Further, each piece of description information of the target travel scenario may also be summarized and analyzed by an analyst, so that general information of the target travel scenario is obtained for overall description of a travel scenario...")
Regarding claim 3, Wang teaches: The method of claim 1 wherein generating the database of task-bound rich trajectories further comprises clustering sub- trajectories (Paragraph [58], "In an embodiment, the server may obtain historical vehicle trajectory data of the target vehicle, perform cluster analysis on massive historical vehicle trajectory data to obtain a plurality of clusters") according to spatial proximity (Paragraph [58], " historical vehicle trajectory data with coincidence of the travel route greater than a preset threshold are classified into the same cluster") or similarity of path geometry prior to associating each cluster with a task (Paragraph [58], "For example, historical vehicle trajectory data with the same travel starting point and travel end point are classified into the same cluster")
Regarding claim 4, Wang teaches: The method of claim 1 wherein associating each sub-trajectory with a corresponding task further comprises weighting data from multiple sensors (Paragraph [139]) according to sensor reliability (Paragraph [51], "It may be understood that the vehicle trajectory data is continuously generated once the vehicle starts") when forming the context-annotated nodes (Paragraph [50], "...sampling point...")
Regarding claim 5, Wang teaches: The method of claim 1 wherein the context annotated nodes each store aggregated sensor attributes including at least one of load weight, mast height, velocity, or orientation data corresponding to the associated tasks (Paragraph [50], "The sampling data includes at least one of a... travel speed, an azimuth angle… or the like")
Regarding claim 6, Wang teaches: The method of claim 1 wherein generating the database of task-bound rich trajectories further comprises discarding task-bound trajectories having fewer than a threshold number of nodes (Figure 3; element 304; Paragraph [58], "The server may extract at least one type of information such as a travel time") to eliminate statistically insignificant sequences (Written as an intended use)
Regarding claim 7, Wang teaches the limitations as stated above as stated in claim 1, including analyzing vehicle trajectories, it does not expressly teach:
training a machine-learning model using a sample of the task-bound trajectories
wherein an input to the model comprises attributes associated with the samples
However, Chai et al. teaches: The method of claim 1, further comprising training a machine-learning model using a sample of the task-bound trajectories (Paragraph [44]), wherein an input to the model comprises attributes associated with the samples (Paragraph [32])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model of Wang and Chai et al, to include training the machine-learning model, as taught by Chai et al. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a trained machine learning model.
Regarding claim 8, Wang teaches: The method of claim 1 further comprising determining feature importances for the task bound trajectories (Paragraph [58], "Perform feature extraction on historical vehicle trajectory data in the same cluster to obtain travel feature information corresponding to each cluster")
Regarding claim 9, Wang teaches: The method of claim 8, wherein the feature importances correspond to attribute importances (Paragraph [58], "The server may extract at least one type of information such as a travel time, a travel route, a travel starting point, or a travel end point corresponding to each piece of historical vehicle trajectory data from the same cluster"), wherein the similarity scores are based on the attribute importances (Figure 8; element 804, 806)
Regarding claim 11, Wang teaches: A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising (Paragraph [27]): determining, automatically by a logistics engine (element 104) executing on one or more hardware processors (Figure 13, 14; “Processor”) and using real-time sensor data and operational logs generated by mobile entities operating in a domain (Paragraph [54], "The underlying SDK or the underlying application may obtain the vehicle trajectory data acquired by the sensor of the vehicle in real time, to report the vehicle trajectory data to the server in real time or at a specified time"), a context of a mobile entity operating in a domain (Figure 1, 2; element S202), the context including a current task performed by the mobile entity (Paragraph [56], "a travel scenario corresponding to going to work, going off work, shopping, and touring"), the current task being identified from timestamped operational logs, manifests (Paragraph [58], "For example, based on the travel time, the travel route…the general information of the target travel scenario is determined to be going to work"), or monitoring data indicating start and end times of tasks (Paragraph [58], "For example, based on ... the travel starting point, and the travel end point…the general information of the target travel scenario is determined to be going to work"); generating, by the logistics engine, a database of task-bound rich trajectories by segmenting historical trajectories of multiple mobile entities into sub-trajectories (Paragraph [84], "In this embodiment, trajectory matching is performed on the current vehicle trajectory data and the each piece of historical vehicle trajectory data in the historical vehicle trajectory data set to obtain a plurality of matching degrees") defined as sequences of timestamped position collections between successive recorded positions (Paragraph [58], "and historical vehicle trajectory data B corresponds to a travel time from 3:10 to 3:35"), each sub- trajectory including aggregated sensor attributes (Paragraph [54], "The underlying SDK or the underlying application may obtain the vehicle trajectory data acquired by the sensor of the vehicle in real time, to report the vehicle trajectory data to the server in real time or at a specified time"), associating each sub-trajectory with a corresponding task in the operational logs (Paragraph [56], "for example, a travel scenario corresponding to going to work, going off work, shopping, and touring"), and storing the sub-trajectories as context-annotated nodes (Paragraph [50], "The vehicle trajectory data includes sampling data of at least one sampling point. The sampling data includes at least one of a sampling position"); filtering, based on the determined context, the database of task-bound rich trajectories (Figure 3; element S302) to identify candidate trajectories that are each associated with the same context as the mobile entity (Figure 3; element S304); and determining a similar trajectory from the candidate trajectories (Figure 3; "maximum matching degree"), wherein a similarity score for each of the candidate trajectories accounts for attribute importances (Paragraph [58], "Perform feature extraction on historical vehicle trajectory data in the same cluster to obtain travel feature information corresponding to each cluster"), wherein the similar trajectory has a highest similarity score, wherein the similar trajectory is considered as an expected trajectory for the mobile entity (Figure 3; element S304); and issuing, by the logistics engine, a control instruction to the mobile entity (Figure 2, 3; element S210, S306) or to a supervisory control system to adjust movement of the mobile entity in accordance with the expected trajectory (Figure 9)
While Wang teaches the limitations as stated above, it does not expressly teach:
determined by a machine-learning model
However, Chai et al. teaches: determined by a machine-learning model (Paragraph [22], "a vehicle, e.g., an autonomous or semi-autonomous vehicle, can use a trained machine learning model, referred to in this specification as a “trajectory prediction system,”")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data of Wang, to include the use of a machine-learning model, as taught by Chai et al. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model.
Regarding claim 12, Wang teaches: The non-transitory storage medium of claim 11, wherein the context of the mobile entity further comprises automatically parsing timestamped operational logs to identify a task identifier (Paragraph [58], "...For example, based on the travel time, the travel route, the travel starting point, and the travel end point of the target travel scenario, the general information of the target travel scenario is determined to be going to work.") associated with the mobile entity within a defined time window (Paragraph [58], "...In this case, the travel time from 3:00 to 3:35 may be used as description information of the target travel scenario for describing the travel time. Further, each piece of description information of the target travel scenario may also be summarized and analyzed by an analyst, so that general information of the target travel scenario is obtained for overall description of a travel scenario...")
Regarding claim 13, Wang teaches: The non-transitory storage medium of claim 11, wherein generating the database of task-bound rich trajectories further comprises clustering sub- trajectories (Paragraph [58], "In an embodiment, the server may obtain historical vehicle trajectory data of the target vehicle, perform cluster analysis on massive historical vehicle trajectory data to obtain a plurality of clusters") according to spatial proximity (Paragraph [58], " historical vehicle trajectory data with coincidence of the travel route greater than a preset threshold are classified into the same cluster") or similarity of path geometry prior to associating each cluster with a task (Paragraph [58], "For example, historical vehicle trajectory data with the same travel starting point and travel end point are classified into the same cluster")
Regarding claim 14, Wang teaches: The non-transitory storage medium of claim 11, wherein associating each sub-trajectory with a corresponding task further comprises weighting data from multiple sensors (Paragraph [139]) according to sensor reliability (Paragraph [51], "It may be understood that the vehicle trajectory data is continuously generated once the vehicle starts") when forming the context-annotated nodes (Paragraph [50], "...sampling point...")
Regarding claim 15, Wang teaches: The non-transitory storage medium of claim 11, wherein the context annotated nodes each store aggregated sensor attributes including at least one of load weight, mast height, velocity, or orientation data corresponding to the associated tasks (Paragraph [50], "The sampling data includes at least one of a... travel speed, an azimuth angle… or the like")
Regarding claim 16, Wang teaches: The non-transitory storage medium of claim 11, wherein generating the database of task-bound rich trajectories further comprises discarding task-bound trajectories having fewer than a threshold number of nodes (Figure 3; element 304; Paragraph [58], "The server may extract at least one type of information such as a travel time") to eliminate statistically insignificant sequences (Written as an intended use)
Regarding claim 17, Wang teaches the limitations as stated above as stated in claim 1, including analyzing vehicle trajectories, it does not expressly teach:
training a machine-learning model using a sample of the task-bound trajectories
wherein an input to the model comprises attributes associated with the samples
However, Chai et al. teaches: The non-transitory storage medium of claim 11, further comprising training a machine-learning model using a sample of the task-bound trajectories (Paragraph [44]), wherein an input to the model comprises attributes associated with the samples (Paragraph [32])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model of Wang and Chai et al, to include training the machine-learning model, as taught by Chai et al. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a trained machine learning model.
Regarding claim 18, Wang teaches: The non-transitory storage medium of claim 11, further comprising determining feature importances for the task bound trajectories (Paragraph [58], "Perform feature extraction on historical vehicle trajectory data in the same cluster to obtain travel feature information corresponding to each cluster")
Regarding claim 19, Wang teaches: The non-transitory storage medium of claim 18, wherein the feature importances correspond to attribute importances (Paragraph [58], "The server may extract at least one type of information such as a travel time, a travel route, a travel starting point, or a travel end point corresponding to each piece of historical vehicle trajectory data from the same cluster"), wherein the similarity scores are based on the attribute importances (Figure 8; element 804, 806)
Claim 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230045727 A1) in view of Chai et al. (US 2021/0001897 A1) in further view of Prats (US 10946519 B1)
Regarding claim 10, Wang teaches the limitations as stated above as stated in claim 1, including analyzing vehicle trajectories, it does not expressly teach:
monitoring behaviors of mobile entities by comparing similarity scores for current tasks
with similarity scores associated with non-similar tasks but similar paths and flagging high scoring trajectories
and rectifying the task-bound trajectories based on the similarity scores for the non-similar tasks
However, Prats teaches: The method of claim 1, further comprising monitoring behaviors of mobile entities by comparing similarity scores for current tasks (Paragraph [Col 9, lines 64- Col 10, lines 5], "In some implementations, when robot 100 receives a command to perform a task that requires traversal of end effector 106 between start and target positions, those start and target positions may be compared to indexed start/target position pairs of the table in FIG. 3. The row indexed by the start/target position pair that is most similar (e.g., in Euclidian distance) to the start and target positions associated with the task may be determined, e.g., by online trajectory engine 130") with similarity scores associated with non-similar tasks but similar paths and flagging high scoring trajectories (Paragraph [Col 10, lines 18-27], "A precalculated joint trajectory may be compatible with a state of robot 100 when, for instance, robot 100 can implement a joint trajectory that traverses end effector 106 along the precalculated joint trajectory without violating any kinematic or other constraints of robot 100. For example, if the joint trajectory robot 100 would implement in order to achieve the end effector trajectory poses an unacceptable risk (e.g., greater than a threshold risk) of reaching robot singularity, online trajectory engine 130 may move to the next precalculated joint trajectory"), and rectifying the task-bound trajectories based on the similarity scores for the non-similar tasks (Paragraph [Col 10, lines 34-35], "which may eliminate precalculated trajectories that might pose a risk")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model of Wang and Chai et al, to include acknowledging similar trajectories that pose a risk and eliminating them, as taught by Wang. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a trained machine learning model, and acknowledging similar trajectories to the current vehicle trajectory that pose a risk and eliminating them.
Regarding claim 20, Wang teaches the limitations as stated above as stated in claim 11, including analyzing vehicle trajectories, it does not expressly teach:
monitoring behaviors of mobile entities by comparing similarity scores for current tasks
with similarity scores associated with non-similar tasks but similar paths and flagging high scoring trajectories
and rectifying the task-bound trajectories based on the similarity scores for the non-similar tasks
However, Prats teaches: The non-transitory storage medium of claim 11, further comprising monitoring behaviors of mobile entities by comparing similarity scores for current tasks (Paragraph [Col 9, lines 64- Col 10, lines 5], "In some implementations, when robot 100 receives a command to perform a task that requires traversal of end effector 106 between start and target positions, those start and target positions may be compared to indexed start/target position pairs of the table in FIG. 3. The row indexed by the start/target position pair that is most similar (e.g., in Euclidian distance) to the start and target positions associated with the task may be determined, e.g., by online trajectory engine 130") with similarity scores associated with non-similar tasks but similar paths and flagging high scoring trajectories (Paragraph [Col 10, lines 18-27], "A precalculated joint trajectory may be compatible with a state of robot 100 when, for instance, robot 100 can implement a joint trajectory that traverses end effector 106 along the precalculated joint trajectory without violating any kinematic or other constraints of robot 100. For example, if the joint trajectory robot 100 would implement in order to achieve the end effector trajectory poses an unacceptable risk (e.g., greater than a threshold risk) of reaching robot singularity, online trajectory engine 130 may move to the next precalculated joint trajectory"), and rectifying the task-bound trajectories based on the similarity scores for the non-similar tasks (Paragraph [Col 10, lines 34-35], "which may eliminate precalculated trajectories that might pose a risk")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a machine learning model of Wang and Chai et al, to include acknowledging similar trajectories that pose a risk and eliminating them, as taught by Wang. Such modification would have been obvious because such application would have been well within the level of skill of a person having ordinary skill in the art and would have yielded predictable results. The predictable results including: generating recommended information to a vehicle based on matching historical vehicle trajectory data to current vehicle trajectory data using a trained machine learning model, and acknowledging similar trajectories to the current vehicle trajectory that pose a risk and eliminating them.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSE TRAMANH TRAN whose telephone number is (703)756-5879. The examiner can normally be reached M-F 8:30am-5pm ET.
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, Khoi Tran can be reached at 571-272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.T.T./Examiner, Art Unit 3656