DETAILED ACTION
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 .
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.
Claims 1, 3-6, 9, 11, 13-16, 17, 19, 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Katz [US 2023/0347903] in view of Kondrad et al [US 2022/0144140]
Consider claim 1. (Currently Amended) A method for controlling vehicle-riding safety
(the DMS and OMS for monitoring and tracking in-vehicle dynamic driver, occupant or
other person gaze tracking, see abstract, para [0042-0047]), comprising:
determining a target object from objects that ride in a vehicle, wherein the target object
comprises at least one of a child with a preset age interval from 0 year to 12 years, a
person having mental retardation and a disabled person having difficulty in moving
(read upon the occupant monitoring system "OMS" monitors and determines one or
more occupants of a vehicle to be monitored and tracked, such as a particular
child/baby being seated on a person's lap, 4 children in rear seat or the like, and
including of passenger/person with mental distress, physical condition, sickness, see
Figs. 1-5, para [0042, 0045, 0046, 0050, 0093]);
obtaining an action trajectory of the target object (the image sensors 6 such as CCD or
CMOS, camera and/or video to obtain the images or videos to indicate action, motion,
movement, posture and/or orientation of one or more body parts of a driver, object,
passenger, location of users or users' body parts, see Figs. 1-5, para [0071, 0075,
0093, 0100, 0137, 0138]);
based on the action trajectory, predicting a target action of the target object at a future
moment (the processor 12 is predicting gestures, motion, body posture, features
associated with object 24, occupant, child or another person attempted to open the
door/window or to reaching out of the door/window, item or object in back seat, see Fig.
1, para [0072, 0089-0094, 0135, 0138-0141]);
based on the target action and a vehicle state of the vehicle, determining a dangerous
situation of the target object at the future moment; and executing a control strategy
corresponding to the dangerous situation, wherein the control strategy comprises at
least a prompting message, and the prompting message is for prompting other objects
than the target object that ride in the vehicle that the target object is about to be in
danger (the processor 12 provides at least one of the message, command, or alert may
be associated with at least one of: a first indication of a level of danger of picking up or
interacting with the mobile device; or a second indication that the driver can safely
interact with the mobile device, wherein the at least one processor 12 is further
configured to determine the first indication or the second indication using information
associated with at least one of: a road condition, a driver condition, a level of driver
attentiveness to the road, a level of driver alertness, one or more vehicles in a vicinity of
the driver's vehicle, a behavior of the driver, a behavior of other occupants or
passengers or other individual's action, an interaction of the driver with other
passengers, the driver actions prior to interacting with the mobile device, one or more
applications running on a device in the vehicle, a physical state of the driver, or a
psychological state of the driver. In some embodiments, an indication of levels of
danger, as well as what is classified by the system to be "dangerous" or "safe," may be
preprogrammed in one or more rule sets stored in memory or accessed by the at least
one processor, or may be determined by a machine learning algorithm trained
using data sets indicative of various types of behaviors and driving events, and
outcomes indicative of actual or potential harm to persons or property. Wherein the at
least one generated message, command, or alert causes an output device to
communicate to the individual a warning associated with a level of danger of the
interaction or the attempted operation, see Figs. 5, 8, para [0072, 0089, 0090, 0320,
0322, 0341, 0564, 0565]); and
wherein the step of, based on the target action and the vehicle state of the vehicle,
determining the dangerous situation of the target object at the future moment comprises:
obtaining a target region where the target object is located when the target object executes the target action (the OMS processor 12 may comprise a system that tracks the target driver and driver’s intervention-actions or actions accordingly to the driver’s detected state, driver’s gazing shifts from region to region, physical condition, emotional condition, cognitive load, actions, behaviors, driving performance, attentiveness, alertness, drowsiness (see Figs. 1, 9, para [0042, 0064, 0222-0224]); and
when at least local region of the target region is not located within a safe region that is
predetermined, based on the vehicle state of the vehicle and a dangerous region where the at least local region is belonged, determining the dangerous situation of the target object at the future moment (the DMS processor 12 determines when a driver is traveling on a safe road to a future dangerous/unsafe target road conditions or road accident occurs, see para [0058, 0059, 0133]);
wherein the step of obtaining the target region where the target object is located when the target object executes the target action comprises: obtaining an age of the target object (the OMS processor 12 executes to monitor the occupancy of a vehicle's cabin, detecting and tracking target people and objects, and acts according to their presence, position, pose, identity, age, gender, physical dimensions, state, emotion, health, head pose, gaze, gestures, facial features and expressions, then to determine driver’s intervention-actions or actions, accordingly, see Figs. 1, 9, para [0045, 0093, 0222-0224]). But
Katz fails to disclose from a predetermined corresponding relation between ages and body-weight thresholds, looking up a target body-height threshold of the target object corresponding to an age. However,
Katz teaches that the DMS and OMS processor 12 monitor and process the age and posture of a user different between a child, an adult or gender, and a predetermined a dynamic threshold or scale determined for the individual user, see abstract, Figs. 1-3, para [0050, 0093, 0094]).
Kondrad et al suggest that the vehicle includes a passenger compartment, a plurality of seating assemblies, an imager, and a controller. The plurality of seating assemblies are positioned within the passenger compartment. The imager is mounted on the vehicle with a field of view of the imager being oriented toward a vehicle-exterior environment. The imager collects an image of a prospective user. The controller references the imager and determines an arrangement of the plurality of seating assemblies within the passenger compartment based upon the images of the prospective user. The controller includes a microprocessor and memory. The memory comprises routines stored therein (see Figs. 1, 7, para [0003]).
Referring again to FIG. 44, upon obtaining an estimated height of the smaller-statured user at step 1516, the estimated height may be referenced against a database at step 1520. In various examples, the database may be stored within the memory 112 of the controller 104. In some examples, the database may be stored external to the controller 104, with the controller 104 being communicatively coupled to the database. In referencing the database at step 1520, the controller 104 can infer an age of the smaller-statured user at step 1524 based upon the estimated height and the information contained within the database. Once an age of the smaller-statured user has been inferred at step 1534, the controller 104 may compare the inferred age and/or height against a predetermined threshold at decision point 1528. The predetermined threshold may be determined by one or more occupant safety recommendations or standards. If the controller 104 determines at decision point 1528 that the smaller-statured user is below the predetermined threshold, then the arrangement of the passenger compartment 140 can be adjusted to the child seat arrangement at step 1532. Alternatively, if the controller 104 determines at decision point 1528 that the smaller-statured user is not below the predetermined threshold, then the arrangement of the passenger compartment 140 may be maintained in the current arrangement at step 1508 (see Fig. 44, para [0162]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to modify and/or implement the controller to compare the inferred age and height against a predetermined threshold of Kondrad et al to the DMS and OMS processor of Katz for controlling and adjusting the child seat arrangement for greater safety and secure and to preventing of injury when an accident or collision occur.
Claim 2. Cancelled
Consider claim 3. (Original) The method for controlling the vehicle-riding safety
according to claim 1, wherein the step of obtaining the action trajectory of the target
object comprises: obtaining a video containing the target object (as cite in respect to
claim 1 above);
obtaining human-body gestures of the target object in each frame image contained in
the video, wherein each of the human-body gestures is formed by a plurality of
articulation points (the body's parts of a driver, occupant, passenger, person or user,
see para [0045, 0063, 0069, 0070, 0074, 0075]); and
based on a variation trend of position information of same articulation points in the
human-body gestures corresponding to each frame image, determining the action
trajectory of the target object (the user body gestures, see para [0032, 0045, 0063,
0093]).
Consider claim 4. (Currently Amended) The method for controlling the vehicle-riding
safety according to claim 2, wherein the step of obtaining the target region where the
target object is located when the target object executes the target action comprises:
obtaining an age of the target object (see para [0042, 0045]);
from a predetermined corresponding relation between ages and body-height thresholds,
looking up a target body-height threshold of the target object corresponding to an age;
based on the target body-height threshold and the human-body gestures of the target
object, obtaining contours of body parts of the target object (as the combination between Dunnum and Kondrad et al in respect to claim 1 above);
determining a target body part executing the target action of the target object; and
based on position information of a contour of the target body part at a current time,
determining the target region where the contour of the target body part is located when
the target body part is executing the target action (as cited in respect to claims 1 and 3
above, and including the contour of a portion of the user's body, see para [0033, 0153,
0181]).
Consider claim 5. (Currently Amended) The method for controlling the vehicle-riding
safety according to claim 1, wherein the step of, based on the action trajectory,
predicting the target action of the target object at the future moment comprises:
obtaining text information of the target object corresponding to voice information; and
based on the text information and the action trajectory, predicting the target action of the
target object at the future moment (as cited in respect to claim 1 above, and wherein the
predict actions include talking, texting message, speech and/or voice, see para [0043,
0047, 0086, 0090]).
Consider claim 6. (Currently Amended) The method for controlling the vehicle-riding
safety according to claim 2, wherein the safe region comprises a first safe region (the
driver seat region) and/or a second safe region (the front seat region or the rear seat
region for passengers seated inside the vehicle), and the safe region is determined by:
from a predetermined corresponding relation between ages and body-height thresholds,
looking up a target body-height threshold of the target object corresponding to an age (as the combination between Dunnum and Kondrad et al in respect to claim 1 above);
and when the target object is sitting in a safety seat, determining, when an object having
the target body-height threshold is correctly sitting in the safety seat, a region where the
object is located to be the first safe region (as cited in respect to claim 4 above, wherein
the seat validity or seatbelt for a child, an adult, pregnant person or gender to sit in the
seat, see para [0093]); and/or
when the target object is not sitting in the safety seat, determining a region other than a
predetermined dangerous region to be the second safe region, wherein the dangerous
region comprises at least one of a region where a door handle is located, a region
where a car-window opening press key is located and a region where a car-door gap is
located (as cited above, and including the danger of opening a window, getting in or out
of the vehicle, a driver attempts to closing/opening a door or window, see para [0043,
0048, 0090]).
Claim 8. (Cancelled)
Consider claim 9. (Currently Amended) An electronic device, wherein the electronic
device comprises comprising: a processor (see para [0034-0036]); and
a memory configured to store an instruction executable by the processor (see para
[0034, 0040]); and
wherein the processor is configured to execute the instruction to implement the method
for controlling the vehicle-riding safety according to claim 1 (as the combination between Dunnum and Kondrad et al in respect to claim 1 above and including the processor is programmed to execute the instructions, see para [0073, 0079]).
Claim 10. (Cancelled)
Consider claim 11. (Currently Amended) A non-transitory computer-readable storage
medium, wherein when an instruction in the non-transitory computer-readable storage
medium is executed by a processor of an electronic device, the electronic device is
enabled to be capable of implement the method for controlling the vehicle-riding safety
according to claim 1 (as cited in respect to claims 1 and 9 above, and including the non-
transitory computer, see para [0590, 0591, 0613]).
Claim 12. Cancelled
Consider claim 13. The electronic device according to claim 9, wherein the operation of,
obtaining the action trajectory of the target object comprises: obtaining a video
containing the target object (the image sensors 6 such as CCD or CMOS, camera
and/or video to obtain the images or videos to indicate action, motion, movement,
posture and/or orientation of one or more body parts of a driver, object, passenger,
location of users or users' body parts, see Figs. 1-5, para [0071, 0075, 0093, 0100,
0137, 0138]);
obtaining human-body gestures of the target object in each frame image contained in
the video, wherein each of the human-body gestures is formed by a plurality of
articulation points (as cited in respect to claim 3 above, and including the capturing or
tagging frames or images from the video, see para [0038, 0039, 0106]); and
based on a variation trend of position information of same articulation points in the
human-body gestures corresponding to the each frame image, determining the action
trajectory of the target object (as cited in respect to claims 1, 3-5 above).
Consider claim 14. The electronic device according to claim 12, wherein the operation
of, obtaining the target region where the target object is located when the target object
executes the target action comprises: obtaining an age of the target object; from a
predetermined corresponding relation between ages and body-height thresholds,
looking up a target body-height threshold of the target object corresponding to an age (as the combination between Dunnum and Kondrad et al in respect to claim 1 above);
based on the target body-height threshold and the human-body gestures of the target
object, obtaining contours of body parts of the target object (as the combination between Dunnum and Kondrad et al in respect to claims 1 and 6 above);
determining a target body part executing the target action of the target object; and
based on position information of a contour of the target body part at a current time,
determining the target region where the contour of the target body part is located when
the target body part is executing the target action (the captured contour images and
activities of an individual, driver or passenger during the current driving cession in real
time, see para [0033, 0051, 0060, 0063, 0070, 0094, 0255, 0256, 0268]).
Consider claim 15. The electronic device according to claim 9, wherein the operation of,
based on the action trajectory, predicting the target action of the target object at the
future moment comprises: obtaining text information of the target object corresponding
to voice information; and based on the text information and the action trajectory,
predicting the target action of the target object at the future moment (as cited in respect
to claim 5 above).
Consider claim 16. The electronic device according to claim 12, wherein the operation
of, the safe region comprises a first safe region and/or a second safe region, and the
safe region is determined by: from a predetermined corresponding relation between
ages and body-height thresholds, looking up a target body-height threshold of the target
object corresponding to an age (as the combination between Dunnum and Kondrad et al in respect to claim 1 above); and when the target object is sitting in a safety seat,
determining, when an object having the target body-height threshold is correctly sitting
in the safety seat, a region where the object is located to be the first safe region; and/or
when the target object is not sitting in the safety seat, determining a region other than a
predetermined dangerous region to be the second safe region, wherein the dangerous
region comprises at least one of a region where a door handle is located, a region
where a car-window opening press key is located and a region where a car-door gap is
located (as the combination between Dunnum and Kondrad et al in respect to claims 1 and 6 above).
Consider claim 19. The non-transitory computer-readable storage medium according to
claim 11, wherein the operation of, obtaining the action trajectory of the target object
comprises: obtaining a video containing the target object; obtaining human-body
gestures of the target object in each frame image contained in the video, wherein each
of the human-body gestures is formed by a plurality of articulation points; and based on
a variation trend of position information of same articulation points in the human-body
gestures corresponding to the each frame image, determining the action trajectory of
the target object (as cited in respect to claims 1 and 13 above).
Consider claim 23. (New) The method for controlling the vehicle-riding safety according
to claim 1, wherein the step of obtaining the action trajectory of the target object
comprises: obtaining videos containing the target object that are collected by cameras
in different directions (the direction of cameras 1110 may face either further toward the
driver, further away from the driver, and further upward directions, or image sensor
orientation has changed, see Fig. 11, para [0263, 0266]). The vehicle includes an
image sensor 6, 1701 and 1702, IR camera and videos, see Figs. 12, 17, para [0068,
0071, 0074, 0096, 0137]).
obtaining a plurality of frame images contained in the videos (as cited in respect to claim
1 above, such as the processor 12 received images from at least one cameras, videos
and IR camera, para [0103, 0212]); and
regarding each of the plurality of frame images, obtaining a two-dimensional coordinate
(u,v) in a two-dimensional coordinate system of the target object in the image (the 2-D
image sensors 6, see Figs. 1, 2, para [0100, 0137, 0143]), and converting the two-
dimensional coordinate into a three-dimensional coordinate (X,Y,Z) (the control
boundary may be representative on orthogonal projection of the physical edges of a
device into 3D space display or head pose, gaze, face and facial attribute 3D, virtual
3D, and 3D reconstruction of the environment around the vehicle, see Fig. 1, para
[0032, 0044, 0091, 0092, 0154, 0167]).
Consider claim 24. (New) The method for controlling the vehicle-riding safety according
to claim 23, wherein after the step of regarding the each of the plurality of frame images,
obtaining the two-dimensional coordinate (u,v) in the two-dimensional coordinate
system of the target object in the image, and converting the two-dimensional coordinate
into the three-dimensional coordinate (X,Y,Z) (as cited in respect to claim 23 above), the
method further comprises:
adding the three-dimensional coordinates of the target object in the images contained in
the videos into the three-dimensional coordinate system (read upon the 3D image of a
child, driver, object or passenger is presented or added on a 3D display 4, 3D map,
physical edges of the device or some other physical dimension of the display for a user
to view, see Fig. 3, para [0032, 0174-0178]);
obtaining a plurality of three-dimensional-position sets, wherein each of the three-
dimensional-position sets include the three-dimensional coordinates corresponding to
same collection time (as above, and the processor 12 may be configured to perform
different actions based on the number of times a control boundary is crossed or a length
of the path of the gesture relative to the physical dimensions of the user's body. For
example, an action may be caused by the processor based on a number of times that
each edge or corner of the control boundary is crossed by a path of a gesture. In some
embodiment, a dimension of time may be associated with the 3D mapping, see Figs. 3,
10, para [0197, 0235]);
inputting each of the three-dimensional-position sets into a gesture identifying model
that is pre-constructed, to obtain human-body gestures corresponding to the three-
dimensional coordinates (the gesture location, as used herein, may refer to one or a
plurality of locations associated with a gesture. For example, a gesture location may be
a location of an object or gesture in the image information as captured by the image
sensor, a location of an object or gesture in the image information in relation to one or
more control boundaries, a location of an object or gesture in the 3D space in front of
the user, a location of an object or gesture in relation to a device or physical dimension
of a device, or a location of an object or gesture in relation to the user body or part of
the user body such as the user's head. For example, a "gesture location" may include a
set of locations comprising one or more of a starting location of a gesture, intermediate
locations of a gesture, an ending location of a gesture and type of gesture (see Figs. 3,
5A-5L, 6, para [0154, 0166, 0167]); and
regarding any two three-dimensional-position sets whose time is consecutive,
connecting corresponding articulation points in the human-body gestures corresponding
to the two three-dimensional-position sets, to obtain the action trajectory in the three-
dimensional coordinate system (as above, and FIGS. 5A-5L illustrate graphical
representations of example motion paths that may be associated with touch-free
gesture systems and methods consistent with the disclosed embodiments. Each
differing combination of motion path and gesture bay result in a differing action, see Fig.
6, para [0012, 0110, 0166, 0187-0197]).
Consider claim 25. (New) The method for controlling the vehicle-riding safety according
to claim 1, wherein after determining the target object from objects that ride in the
vehicle, likes of the target object is analyzed based on an artificial intelligence (AI)
algorithm, to analyze out an article that the target object is interested in (read upon the
machine learning system may be implemented in various ways including linear and
logistic regression, linear discriminant analysis, support vector machines (SVM),
decision trees, random forests, ferns, Bayesian networks, boosting, genetic algorithms,
simulated annealing, convolutional neural networks (CNN) or AI, (see para [0073, 0079,
0080, 0082, 0267]).
Claims 7, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Katz
[US 2023/0347903] and Kondrad et al [US 2022/0144140] and further in view of Jeong [US 2018/0179790]
Consider claim 7. (Original) The method for controlling the vehicle-riding safety according to claim 6, wherein the step of, based on the vehicle state of the vehicle and
the dangerous region where the at least local region is belonged, determining the
dangerous situation of the target object at the future moment (as cited in respect to
claim 1 above), comprises:
when the vehicle state is a travelling state, the target object is sitting in the safety seat
and the dangerous region is a region other than the first safe region, determining that
the dangerous situation is that the target object disengages from the safety seat (the
dangerous includes a passenger wearing seatbelt incorrectly and/or unbuckling a
seatbelt, see para [0043, 0057, 0066, 0090]); and
when the vehicle state is that a vehicle speed is greater than a preset value and the
dangerous region is a region where the car-window opening press key is located,
determining that the dangerous situation is that the target object is about to open a car
window (which reads upon the vehicle speed acceleration/deceleration, suddenly stop
being considered as a dangerous condition of a driving behaviors or in response to an
emergency event, and the driver/passenger activities include to opening a door/window
or reaching through the door or window while the vehicle is in dangerous situation, see
para [0043, 0048, 0090, 0115]). But
Katz fails to disclose when the vehicle state is the travelling state, a child safety lock is
not turned on and the dangerous region is a region where the door handle is located,
determining that the dangerous situation is that the child safety lock is not turned on.
However,
Katz teaches that the detection system may comprise one or more
components embedded in the vehicle or be part of the mobile device, such as the
processor, camera, or microphone of the mobile device. In other embodiments, the
mobile device could be another device or system in the car, such as the entertainment
system, HVAC controls, or other vehicle systems that the driver should not be
interacting with while driving. In yet another embodiment, the detection system may be
a part of the vehicle, such as the hand brake, buttons, knobs, or door locks of the
vehicle (see para [0217]). Machine learning components can be used to detect one or
more persons, a person's age or gender, a person's ethnicity, a person's height, a
person's weight, a pregnancy state, a posture, an abnormal seating position, seat
validity (availability of a seatbelt), a posture of the person, seat belt fitting and tightness,
an object, presence of an animal in the vehicle, presence and identification of one or
more objects in the vehicle, learning the vehicle interior, an anomaly, a damaged item or
portion of the vehicle interior, a child/baby seat in the vehicle, a number of persons in
the vehicle, a detection of too many persons in a vehicle (e.g. 4 children in rear seat
when only 3 are allowed), or a person sitting on another person's lap (see para [0093]).
Jeong suggests that the vehicle internal door lock-releasing operation using a child locking member 1700 of the door latch system. The separation of the child locking member 1700 from the connected position or the disconnected position is prevented even when the external impact is applied thereto when the child locking member 1700 is in the connected position or in the disconnected position. That is, the erroneous operation of the child locking member 1700 due to the external impact is prevented. The door 1 cannot be opened from the inside of the vehicle when it is in a child locking state, but the door 1 can be opened only from the outside of the vehicle.
Thus, the children and the elderly can be protected from the accidents caused by the
unexpected opening and closing of the door 1. (see Figs. 24, 31-33, para [0158, 0447,
0449, 0494]).
Therefore, it would have been obvious to one skill in the art before the effective filed
date of the invention to add or implement the child lock member of Jeong to the vehicle
door locks of Katz and Kondrad et al for providing a protection, safety and security of a child seating inside the vehicle while driving, since the child safety lock is built-in the vehicle is available to the automobile industries.
Katz also fails to disclose when the vehicle state is a stationary state and the dangerous
region is a region where the car-door gap is located, determining that the dangerous
situation is that the target object is about to be squeezed by a car door.
Jeong suggests that when such reduction gear is provided, the speed of the motor
3610 is greatly reduced so that the closing operation of the door through the motor 3610
is smoothly performed and the driving torque is secured as well. In addition, since the
speed is reduced when closing the door, the door can be opened emergently when a
safety related accident happens wherein a body or clothes are squeezed by the door
(see Fig. 43, para [0549]).
Therefore, it would have been obvious to one skill in the art before the effective filed
date of the invention to add or implement the door closing operation with a safety
related accident happens wherein a body or clothes are squeezed by the door of Jeong
to the vehicle door locks of Katz and Kondrad et al for providing a secure and safety to a driver, passenger, individual or user seating inside the vehicle.
Consider claim 17. The electronic device according to claim 16, wherein the operation
of, based on the vehicle state of the vehicle and the dangerous region where the at
least local region is belonged, determining the dangerous situation of the target object
at the future moment comprises: when the vehicle state is a travelling state, the target
object is sitting in the safety seat and the dangerous region 1s a region other than the
first safe region, determining that the dangerous situation is that the target object
disengages from the safety seat; when the vehicle state is the travelling state, a child
safety lock is not turned on and the dangerous region is a region where the door handle
is located, determining that the dangerous situation is that the child safety lock is not
turned on; when the vehicle state is a stationary state and the dangerous region is a
region where the car-door gap is located, determining that the dangerous situation is
that the target object is about to be squeezed by a car door; and when the vehicle state
is that a vehicle speed is greater than a preset value and the dangerous region is a
region where the car-window opening press key 1s located, determining that the
dangerous situation 1s that the target object is about to open a car window (as cited and
the combination between Katz and Faith et al and Kondrad et al and Jeong in respect to claim 1, 7 above).
Response to Arguments
Applicant’s arguments, see the amendment, filed on 05/18/2026, with respect to the rejection(s) of claims 1, 9 under Katz have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kondrad et al [US 2022/0144140].
Applicant’s arguments:
(A) Katz only discloses the actual age. gender, physical dimensions of people and
objects, but Katz fails to disclose the specific feature of "a predetermined corresponding relation between ages and body-height thresholds". However, in claim 1 of the present application, it is recited that "from a predetermined corresponding relation between ages and body-height thresholds, looking up a target body-height threshold of the target object corresponding to an age", which is not disclosed by Katz.
(B) Claim 3-7 and 23-25 depending from claim 1, directly or indirectly.
Response to the arguments:
(A) It is obvious to combine the controller to compare the inferred age and height against a predetermined threshold of Kondrad et al to the DMS and OMS processor of Katz for controlling and adjusting the child seat arrangement for greater safety and secure and to preventing of injury when an accident or collision occur, since the references are in the field of endeavor of the invention to detecting, monitoring and tracking of vehicle monitoring system and occupant monitoring system to provide secure and safety to a driver and/or passengers during traveling on a road.
(B) The dependent claims 3-7 and 23-25 are obviously rejected according to their rejected independent claim 1 as above.
Conclusion
Examiner is very regrettable to withdraw the Final Office Action filed on 03/17/2026 and to introduce a new Non-Final Office Action based on the update search and a new reference of Kondrad et al [US 2022/0144140] to make the rejection smoother.
Any inquiry concerning this communication or earlier communications from examiner should be directed to primary examiner craft is Van Trieu whose telephone number is (571) 2722972. The examiner can normally be reached on Mon-Fri from 8:00 AM to 3:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Wang Quan-Zhen can be reached on (571) 272-3114.
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/VAN T TRIEU/
Primary Examiner, Art Unit 2685
05/19/2026
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