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 Claims
This is the first Office Action on the merits. Claims 1-14 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP22179165.0, filed on 12/12/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/12/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 4-10 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend from any other multiple dependent claims. See MPEP § 608.01(n). Accordingly, claims 4-10 have not been further treated on the merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title
Claims 1-3 and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1. A computer-implemented method of assisting in the operation of a vehicle, the method comprising the steps of:
with at least one sensor, sensing an environment of the vehicle thereby obtaining sensor data;
deriving spatial information of the environment and semantic information from the sensor data;
generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells, the grid cells comprising occupying information and a dynamic state represented by a set of particles;
assigning the grid cells and the particles semantic information derived from the sensor data, wherein the semantic information is represented by a set of categories;
predicting new particle positions on the grid;
determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells with the semantic information assigned to the particles based on their predicted new particle positions;
obtaining new sensor data;
updating the predicted semantic information assigned to the grid cells and the semantic information assigned to the particles from the new sensor data;
deriving an automated driving action based on the updated semantic information and a new dynamic state of the grid cells.
Claim 11. A system for assisting in the operation of a vehicle, comprising:
at least one sensor sensing an environment of the vehicle thereby obtaining sensor data;
a control system comprising one or more processors operatively connected to the sensor, the one or more processors configured to perform the steps comprising:
deriving spatial information of the environment and semantic information from the sensor data;
generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells the grid cells comprising occupying information and a dynamic state represented by a set of particles;
assigning the grid cells and the particles semantic information derived from the sensor data, wherein the semantic information is represented by a set of categories;
predicting new particle positions on the grid;
determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells with the semantic information assigned to the particles based on their predicted new particle positions;
obtaining new sensor data;
updating the predicted semantic information assigned to the grid cells and the semantic information assigned to the particles from the new sensor data;
deriving an automated driving action based on the updated predicted semantic information and a new dynamic state of the one or more grid cells.
101 Analysis – Step 1: Statutory category – Yes
The claim recites a device (i.e. machine). This claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis – Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitation can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claims recites the limitations of deriving spatial information of the environment and semantic information from the sensor data; generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells, the grid cells comprising occupying information and a dynamic state represented by a set of particles; assigning the grid cells and the particles semantic information derived from the sensor data, wherein the semantic information is represented by a set of categories; predicting new particle positions on the grid; determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells with the semantic information assigned to the particles based on their predicted new particle positions; updating the predicted semantic information assigned to the grid cells and the semantic information assigned to the particles from the new sensor data; and deriving an automated driving action based on the updated semantic information and a new dynamic state of the grid cells. These limitations, as drafted, are simple process that, under its broadest reasonable interpretation, covers performance in the human mind or with the aid of a pen and paper but for the recitation of by “computer” or “processor”, respectively. That is, other than reciting “computer” or “processor” nothing in the claim elements preclude the step from practically being perform by using a pen and paper. For example, but for the “computer” or “processor” language, the claim could implicate observing information, classifying information, modeling information, predicting from the information, and making a determination based on the information. The mere nominal recitation of “computer” or “processor” does not take the claims limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis – Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical
application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of at least one sensor sensing an environment of the vehicle thereby obtaining sensor data; a control system comprising one or more processors operatively connected to the sensor, the one or more processors configured to perform the steps comprising: with at least one sensor, sensing an environment of the vehicle thereby obtaining sensor data; and obtaining new sensor data. The sensors, control system, and processor are recited at a high level of generality (i.e. as a general means of gathering information and processing information). Furthermore, obtaining sensor data amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of a control system, one or more processors, and obtaining data via a sensor are well-understood, routine, and conventional components because the detailed description of embodiment does not indicate that the control system and processors are anything other than a conventional processing unit within a computer, and the detailed description further describes that the sensor are conventional sensors for obtaining data related to the environment of a vehicle (i.e., a laser range sensor, a camera, a lidar, a radar, or a sonar). MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-3 and 12-14 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-3 and 12-14 are not patent eligible under the same rationale as provided for in the rejection of the claim 1 and 11.
Therefore, claims 1-3 and 11-14 are ineligible under 35 USC § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Tanzmeister (US20150310146A1) in view of Vatavu et al. ("From Particles to Self-Localizing Tracklets: A Multilayer Particle Filter-Based Estimation for Dynamic Grid Maps"), hereinafter Tanzmeister and Vatavu, respectively.
Regarding claim 1, Tanzmeister teaches of a computer-implemented method of assisting in the operation of a vehicle ("The method described above has been tested in the real environment. For this purpose, a vehicle has been equipped with a laser scanner that has been installed under the front license plate of the vehicle", [0084]), the method comprising the steps of: with at least one sensor, sensing an environment of the vehicle thereby obtaining sensor data ("The method includes the following steps: c) measuring the location of real objects by a sensor in an area including the locations of the particle car", [0010]); deriving spatial information of the environment and semantic information from the sensor data ("calculating the location of the particles at a later point in time (t+Δt) …and assigning the particles to the cells of the particle card that correspond to the newly calculated location", [0011], "at least two continuous classification values, describing the probability of a respective cell being assigned to a specific class, are assigned to the cells …two or more of the following classes are differentiated as to whether the cell has an object; whether the cell has a static object; whether the cell has a dynamic object; whether the cell represents a free space", [0024]); generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells, the grid cells comprising occupying information and a dynamic state represented by a set of particles ("a particle card is generated. The particle card includes cells in a two-dimensional arrangement, each cell representing a specific location in the real world. The cells include cell values describing objects and/or free spaces that are location at the respective locations represented by the cells", [0053]); assigning the grid cells derived from the sensor data, wherein the semantic information is represented by a set of categories ("the cells are assigned to the following classes: a) a cell that has a static object {S}; b) a cell that has a dynamic object {D}; c) a cell that has a dynamic or static object {S, D}; d) a cell that represents a free space {F}; e) a cell of which it is not known", [0062]); predicting new particle positions on the grid ("calculating the location of the particles at a later point in time (t+Δt) by a predetermined time step (Δt) vis-a-vis specific point in time (t) from step a) and assigning the particles to the cells of the particle card that correspond to the newly calculated location", [0011]); determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells ("cells of the particle cards are classified as cells including static objects when their particles have an average velocity below a predetermines threshold value and/or their particles have a velocity variance and/or a variance of the direction above a predetermines threshold", [0038], "new particles are preferably distributed in the cells of the particle card according to the classification values for static objects and/or dynamic objects and/or according to the classification values for objects of which it is not determined whether they have dynamic or static objects", [0040], "Cells describing dynamic particles in particular 'survive' particles that have approximately the direction and velocity of the dynamic object", [0040], teaches continuously determining cell semantic info using the existing stored semantic information); obtaining new sensor data ("repeating steps a) through d) and, in step a) new particles are added to the particles not deleted in step d)", [0014], "c) measuring the location of real objects by means of a sensor" , [0012]); updating the predicted semantic information assigned to the grid cells from the new sensor data ("all evidence masses of the historical environment model are linked with the corresponding evidence masses of the sensor environmental model", [0078], this represents the cells being updated); and deriving an automated driving action based on the updated semantic information and a new dynamic state of the grid cells ("the system is a component of a driver assistance system of a motor vehicle, for example, a lane-change assistance, braking assistance, emergency or collision-avoidance assistance systems, of a system for automated driving or a driver assistance system for controlling the motor vehicle in a fully automated manner", [0049], "a decision on the basis of the environmental model may be made more reliably in a driving assistance system", [0033]).
However, Tanzmeister does not teach of assigning the particles semantic information; the semantic information assigned to the particles based on their predicted new particle positions; and the semantic information assigned to the particles.
Vatavu, in the same field of endeavor, teaches of assigning the particles semantic information ("every tracklet is represented by an appearance vector At, which combines the raw sensor data of the occupancy and semantic channels. The appearance vector At is thus completely described by a belief mass for occupies
m
t
o
, a belief mass for free
m
t
f
, and a semantic label
l
t
", pg. 6, Section IV.B, Eq. 4, "At this step, all particles are initialized with the same occupancy masses and semantic values that are received from the corresponding input channels", pg. 7, Section V.A.1); the semantic information assigned to the particles based on their predicted new particle positions ("the semantic likelihood is defined by a dissimilarity metric given the particle's semantics…
d
s
=
1
-
η
l
∙
h
(
l
p
,
,
l
m
)
", pg. 8, Section V.A.3, Eq. 13, "the measurement model consists of three components: a measurement cell likelihood
p
(
z
t
d
|
s
t
i
)
, a landmark based likelihood
p
(
z
t
l
|
s
t
i
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, and a semantic likelihood
p
(
z
t
s
|
s
t
i
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", pg. 8, Section V.A.3, teaches particle-level semantic information); and the semantic information assigned to the particles ("every tracklet is represented by an appearance vector At, which combines the raw sensor data of the occupancy and semantic channels. The appearance vector At is thus completely described by a belief mass for occupies
m
t
o
, a belief mass for free
m
t
f
, and a semantic label
l
t
", pg. 6, Section IV.B, Eq. 4, "At this step, all particles are initialized with the same occupancy masses and semantic values that are received from the corresponding input channels", pg. 7, Section V.A.1)
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teaching of Tanzmeister with the teaching of Vatavu to assign semantic information to grid cells with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to enable the system to distinguish between different object types (such as pedestrians, bicycles, and vehicles) at the cell and particle level, improving the reliability (Vatavu, Section III).
Regarding claim 2, modified Tanzmeister teaches of all limitations of claim 1 as stated above, additionally, wherein the updating is performed via Bayes inference ("This linking and time filtering is preferably achieved using Jøsang's cumulative operator …which corresponds to a base filter and represents the Bayes filter in the evidence theory according to Dempster & Shafer", [0044]).
Regarding claim 3, modified Tanzmeister teaches of all limitations of claim 1 or 2 as stated above, further comprising the steps of: resampling the dynamic occupancy grid model; and generating new particles ("filtering the particles in the individual cells as a function of the measured objects, and in cells in which no object is measured, more particles are deleted than in cells in which an object is located", [0013], "repeating steps a) through d) and, in step a) new particles are added to the particles not deleted in step d)", [0014], "after measuring the location of real objects in step c), in step a) only new particles are distributed in cells in which a real object has been measured", [0020]).
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Tanzmeister in view of Vatavu as applied above, and further in view of Natroshvili et al. (US20190049239A1), hereinafter Natroshvili.
Regarding claim 1, Tanzmeister teaches of a system for assisting in the operation of a vehicle ("Preferably, the system is a component of a driver assistance system of a motor vehicle", [0049]), comprising: at least one sensor sensing an environment of the vehicle thereby obtaining sensor data ("The method includes the following steps: c) measuring the location of real objects by a sensor in an area including the locations of the particle car", [0010]); comprising: deriving spatial information of the environment and semantic information from the sensor data ("calculating the location of the particles at a later point in time (t+Δt) …and assigning the particles to the cells of the particle card that correspond to the newly calculated location", [0011], "at least two continuous classification values, describing the probability of a respective cell being assigned to a specific class, are assigned to the cells …two or more of the following classes are differentiated as to whether the cell has an object; whether the cell has a static object; whether the cell has a dynamic object; whether the cell represents a free space", [0024]); generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells, the grid cells comprising occupying information and a dynamic state represented by a set of particles ("a particle card is generated. The particle card includes cells in a two-dimensional arrangement, each cell representing a specific location in the real world. The cells include cell values describing objects and/or free spaces that are location at the respective locations represented by the cells", [0053]); assigning the grid cells derived from the sensor data, wherein the semantic information is represented by a set of categories ("the cells are assigned to the following classes: a) a cell that has a static object {S}; b) a cell that has a dynamic object {D}; c) a cell that has a dynamic or static object {S, D}; d) a cell that represents a free space {F}; e) a cell of which it is not known", [0062]); predicting new particle positions on the grid ("calculating the location of the particles at a later point in time (t+Δt) by a predetermined time step (Δt) vis-a-vis specific point in time (t) from step a) and assigning the particles to the cells of the particle card that correspond to the newly calculated location", [0011]); determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells ("cells of the particle cards are classified as cells including static objects when their particles have an average velocity below a predetermines threshold value and/or their particles have a velocity variance and/or a variance of the direction above a predetermines threshold", [0038], "new particles are preferably distributed in the cells of the particle card according to the classification values for static objects and/or dynamic objects and/or according to the classification values for objects of which it is not determined whether they have dynamic or static objects", [0040], "Cells describing dynamic particles in particular 'survive' particles that have approximately the direction and velocity of the dynamic object", [0040], teaches continuously determining cell semantic info using the existing stored semantic information); obtaining new sensor data ("repeating steps a) through d) and, in step a) new particles are added to the particles not deleted in step d)", [0014], "c) measuring the location of real objects by means of a sensor" , [0012]); updating the predicted semantic information assigned to the grid cells from the new sensor data ("all evidence masses of the historical environment model are linked with the corresponding evidence masses of the sensor environmental model", [0078], this represents the cells being updated); and deriving an automated driving action based on the updated semantic information and a new dynamic state of the grid cells ("the system is a component of a driver assistance system of a motor vehicle, for example, a lane-change assistance, braking assistance, emergency or collision-avoidance assistance systems, of a system for automated driving or a driver assistance system for controlling the motor vehicle in a fully automated manner", [0049], "a decision on the basis of the environmental model may be made more reliably in a driving assistance system", [0033]).
However, Tanzmeister does not teach of a control system comprising one or more processors operatively connected to the sensor, the one or more processors configured to perform the steps; assigning the particles semantic information; the semantic information assigned to the particles based on their predicted new particle positions; and the semantic information assigned to the particles.
Vatavu, in the same field of endeavor, teaches of assigning the particles semantic information ("every tracklet is represented by an appearance vector At, which combines the raw sensor data of the occupancy and semantic channels. The appearance vector At is thus completely described by a belief mass for occupies
m
t
o
, a belief mass for free
m
t
f
, and a semantic label
l
t
", pg. 6, Section IV.B, Eq. 4, "At this step, all particles are initialized with the same occupancy masses and semantic values that are received from the corresponding input channels", pg. 7, Section V.A.1); the semantic information assigned to the particles based on their predicted new particle positions ("the semantic likelihood is defined by a dissimilarity metric given the particle's semantics…
d
s
=
1
-
η
l
∙
h
(
l
p
,
,
l
m
)
", pg. 8, Section V.A.3, Eq. 13, "the measurement model consists of three components: a measurement cell likelihood
p
(
z
t
d
|
s
t
i
)
, a landmark based likelihood
p
(
z
t
l
|
s
t
i
)
, and a semantic likelihood
p
(
z
t
s
|
s
t
i
)
", pg. 8, Section V.A.3, teaches particle-level semantic information); and the semantic information assigned to the particles ("every tracklet is represented by an appearance vector At, which combines the raw sensor data of the occupancy and semantic channels. The appearance vector At is thus completely described by a belief mass for occupies
m
t
o
, a belief mass for free
m
t
f
, and a semantic label
l
t
", pg. 6, Section IV.B, Eq. 4, "At this step, all particles are initialized with the same occupancy masses and semantic values that are received from the corresponding input channels", pg. 7, Section V.A.1).
However, Vatavu does not teach of a control system comprising one or more processors operatively connected to the sensor, the one or more processors configured to perform the steps.
Natroshvili, in the same field of endeavor, teaches of a control system comprising one or more processors operatively connected to the sensor, the one or more processors configured to perform the steps ("Each one of the computing devices 204 may in include a processor 206 and a memory 208 to store the generated occupancy grid(s)", [0030]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teaching of Tanzmeister with the teaching of Vatavu to assign semantic information to grid cells with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to enable the system to distinguish between different object types (such as pedestrians, bicycles, and vehicles) at the cell and particle level, improving the reliability (Vatavu, Section III).
Regarding claim 12, modified Tanzmeister teaches of all limitations of claim 12 as stated above, additionally, a vehicle comprising the system of claim 11 ("the system is a component of a driver assistance system of a motor vehicle", [0049]).
Regarding claim 13, modified Tanzmeister teaches of all limitations of claim 1 as stated above.
However, modified Tanzmeister does not teach of a non-transitory computer readable medium storing instructions that, when executed by one or more processors of a control system of a vehicle, cause the one or more processors to perform the steps of claim 1.
Natroshvili, in the same field of endeavor, teaches of a non-transitory computer readable medium storing instructions that, when executed by one or more processors of a control system of a vehicle, cause the one or more processors to perform the steps of claim 1 ("Each one of the computing devices 204 may in include a processor 206 and a memory 208 to store the generated occupancy grid(s)", [0030], "The processor 206 may be configured to compute the object tracking device 220 which generates the occupancy grid of the predetermined region and dynamically update the occupancy grid adding occupancy information about the objects that are detected in the environment, thereby successively generating a plurality of updated occupancy grids (thus, the occupancy grid may also be referred to as dynamic occupancy grid (DOG)", [0031], "The memory 208 may be configured to store the generated occupancy grids", [0032]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teaching of modified Tanzmeister with the teaching of Natroshvili to implement the dynamic occupancy grid method as stored, executable instructions on a non-transitory computer readable medium with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to enable the instructions to be stored and executed on standard embedded automotive computing hardware (Natroshvili, [0030]).
Regarding claim 14, modified Tanzmeister teaches of all limitations of claim 13 as stated above, additionally, wherein the automated driving action comprises at least one of a steering action, a lane changing action, a deceleration action, and/or an acceleration action ("the system is a component of a driver assistance system of a motor vehicle, for example, a lane-change assistance, braking assistance, emergency or collision-avoidance system for controlling the motor vehicle in a fully automated manner", [0049], "a decision on the basis of the environmental model may be more reliably in a driving assistance system", [0033]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABIGAIL LEE ESPINOZA whose telephone number is (571)272-4889. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached at (571) 270-5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ABIGAIL LEE ESPINOZA
Examiner
Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights