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 § 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 1-15 are rejected under U.S.C. 101 because the claimed invention is directed to judicial exception, in particular an abstract idea falling under mental processes without significantly more.
Step 1: The claims in question are primarily to a process. (Step 1: YES)
Step 2A. Prong One: Step 2A Prong One of the eligibility analyses evaluates whether the claim recites judicial exception (Law of Nature, A Natural Phenomenon, or an Abstract Idea). MPEP 2106.4, subsection II, states a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Claim 1 at a high level recites determining an object in the vehicle cabin (element A), determining a number of features of the object (element B), determining a danger level of the object based on the features (element C), and outputting a warning signal based on the danger level meeting a criterion.
Element A recites determining an object in the vehicle cabin which encompasses observation of an image and evaluating the objects in the scene.
Element B recites determining a number of features of the object which encompasses evaluating the image and objects in the image for features.
Element C recites determining a danger level of the object based on the features which encompasses evaluation of the features and a judgement of the danger associated with the object and features.
Element D recites outputting a warning signal based on the danger level meeting a criterion which encompasses evaluation of the comparison of the danger level with the criterion.
Such mental observation and evaluations fall under mental grouping processing (performed in the human mind including observations, evaluations, judgements, and opinions). For example, an image analyst could analyze the image to identify an object and analyzing the object in the image for features, while a reconstruction engineer could use engineering and physics principles to assess the hazards and trajectories of the objects based on the features identified. Claim 2 recites at a high level tracking the movement of an object in images and determining the danger level based on the movement state of the object which encompasses evaluating images to identify an object over a period of time and providing a judgement of the movement to provide a danger level. Similarly, to claim 1, this could be performed by an image analyst for tracking the object and a reconstruction engineer for judging the impact of the object movement. Claim 3 recites at a high level determining bounding boxes for the object which encompasses evaluating the image and providing a judgment to determine the bounding box of the object in the images. Claim 4 recites at a high-level executing machine learning algorithms or using reference model for tracking an object, which encompasses observation of the reference model and providing evaluations to determine the movement of the object compared to the reference. An image analyst could perform the evaluation observing the in-vehicle images and the reference model to track the object. Claim 5 recites at a high level determining the features of the object based on reference characteristics which encompasses observing the reference characteristics and the image to provide a judgement of features of the objects. Claim 6 recites at a high level determining a person in the vehicle cabin based on the images, determining a position of the person, and determining a danger level based on the position which encompasses evaluating an image to identify a person and the position of the person and a judgement to determine the danger level associated. The evaluation could be performed by an image analyst to identify the person and the position of the person within the image and the judgement can be performed by a reconstruction engineer to determine the impact of the persons position of the danger levels of the environment. Claim 7 recites at a high level determining the danger level based on estimating the impact of an object on safety, estimating the weight of an object, velocity of the vehicle, position of the object, securement of the object, and whether the person is a driver or passenger. Claim 7 encompasses providing a judgement of the danger level based on the evaluation of the impact of the object, weight of an object, the velocity of the vehicle, the securement of the object, and the status of the occupants. The judgement of the danger level base on provided details could be performed by a reconstruction engineer determining the impact of the situation, the reconstruction engineer could additionally provide the evaluation of the impact of the object and the velocity of the vehicle. An image analyst could evaluate image for the securement of the object as well as the status of the occupants. A medical professional or reconstruction engineer could evaluate an object and provide estimate weight. Claim 8 recites at a high level determining a danger level when the car is started and dynamically based on the velocity which encompasses judgement and evaluation to provide the danger level and evaluate the velocity of the vehicle. Claim 9 recites at a high-level outputting a warning signals including blocking vehicle operation and outputting control signals based on the occupancy status and danger level which encompasses evaluating and judging the occupancy status and danger level. Claim 10 recites at a high-level outputting a warning signal based on the who the warning signal is addressed to which encompasses evaluating the intended recipient of the warning signal. Claim 11 recites at a high-level taking images of the vehicle cabin using visible light, infrared cameras, or in-cabin radar located inside the vehicle cabin with illuminating the scene with infrared light or radio waves which encompasses observation. Claim 12 recites at a high level surveilling a footwell of the vehicle, which encompasses observation of the footwell. Claim 13 recites at a high-level determining movement of an object in the vehicle cabin using a sensor system, data processing system, and an interface which encompasses evaluating the movement of an object. Claim 14 recites at a high-level vehicle with a system for determining movement of an object, which encompasses evaluation of the object for movement. Claim 15 is analogous to claim 1 and is analyzed similarly.
Step 2A. Prong Two: Step 2A, Prong Two of the eligibility analysis evaluates whether the claim
as a whole integrates the recites judicial exception into a practical application of the exception or
whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying
whether there are any additional elements recited in the claim beyond the judicial exception, and
(2) evaluating those additional elements individually and in combination to determine whether
the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
‘Additional elements’ are generally limitations are generally feature/limitations/steps recited in the
claim beyond the judicial exceptions. MPEP 2106 comprises of limitations that indicate whether or not
there is integration. MPEP 2106.05(a) improvements, (b) particular machine, (c) particular transformation, and (e) other meaningful limitations generally concern limitation that are indicative of
integration, whereas 2106.05(d) well-understood, routine, conventional activity, (f) mere instructions to
apply, (g) insignificant extra-solution activity, and (h) field of use generally concern limitations that are
not indicative of integration. Claim 1 recites outputting a warning signal in element D which amounts to mere data outputting, and thus is insignificant extra-solution activity. MPEP 2106.05(g) states insignificant extra-solution activity is generally understood as activities incidental to the primary process or product that are merely nominal or tangential addition to the claim (examples include transmitting, storing, and outputting information). Claim 9, 10, and 13 similarly recites insignificant extra-solution activities of outputting data and signals that are well-understood, routine, and conventional activities. Claim 11 recites insignificant extra-solution activity of receiving data. Claims 4 and 11-14 the use of machine learning algorithms, cameras, vehicle, or the use of a sensor system, data processing system, and interface, which is recited at a high level of generality and amounts to no more than implementing abstract ideas on generic computer elements. The machine learning algorithms is used to generally apply the abstract ideas without limiting the function. This limitation only recites that machine learning algorithms are used to execute a task without any detail about how the task is accomplished. Claims 2-3 and 5-8 do not include additional elements beyond abstract ideas of mental processes in the form of observation, evaluation, judgement. The additional elements that are present do not integrate the recited judicial exception into practical application (Step 2A, Prong Two: NO), and the claims are direction to the judicial exception. (Step 2A; YES).
Step 2B: Step 2B of the eligibility analysis evaluates whether the claim as a whole amount 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 explain in Step 2A, claims 1, 9-11, and 13 contain additional elements related to the receiving and transmitting data. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that Step 2B requires the consideration of the claim as a combination of the limitation, see MPEP 2106.05 subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity takes into account whether or not the activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). the recitation of receiving image information is recited at a high level of generality and amounts to receiving and transmitting data, which is well-understood, routine, conventional activity. The limitations remain insignificant extra-solution activity even upon reconsideration. Claims 2-3 and 5-8 do not include additional elements. Claims 4 and 11-14 the include the generic use of machine learning algorithms and generic computer elements without additional elements. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-6, 8-9, and 11-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gassmann US20220055620 (included in the IDS) (hereinafter “Gassmann”).
Regrading claim 1, Gassmann teaches a computerized method for vehicle cabin safety, the method comprising (see paragraph 0026, the in-vehicle safety system that uses a digital twin to detect in-cabin safety issues):
determining, based on one or more images showing an interior of a vehicle cabin, at least one object in the vehicle cabin (see paragraph 0028, the safety system may obtain interior object data for one or more objects associated with the vehicle using interior sensory which include cameras [0039], thus the data is image data);
determining a number of features of the at least one object (see paragraph 0029, the collected interior object data may include an array of information about each interior object include each object’s basic properties);
determining a danger level for the at least one object based on the number of features (see paragraph 0031 and 0036, the safety system generated the digital twin from the interior object data, and the digital twin is provided to the safety score module to generate a safety score); and
in response to determining that the danger level meets a given criteria, outputting a warning signal (see paragraph 0038, the passenger message module may generate warning for the driver or passengers related to the interior objects and their scored operating behaviors).
Regarding claim 2, Gassmann teaches the method of claim 1 further comprising:
tracking, based on multiple images showing the interior of the vehicle cabin, a movement state of the object (see paragraph 0040, the safety system may analyze the collected internal object data [the interior object data is obtain using camera sensors for image data and the process can include receiving multiple instances that are received from the vehicle over a period of time, 0039 and 0062] in an object analysis module to perform object tracking [the object may be in a transitional state, 0051]),
wherein determining the danger level is additionally based on the movement state of the object (see paragraph 0040, the result from the object analysis module [including the tracking] is used to create the digital twin. The digital twin is used to produce the safety score, 0036).
Regarding claim 3, Gassmann teaches the method of claim 2 wherein tracking the movement state of the object includes determining bounding boxes for the object in subsequent images (see paragraph 0029 and 0040, the collected interior object data includes a bounding box of each interior object which is analyzed in the object analysis module to perform object tracking. The digital twin database contains multiple instances of digital twin data received from the vehicle over time which is created through the interior object analysis and determine portions of the data that have changed over time, thus tracking with bounding boxes of interior objects is performed for subsequent image data, 0062, 0058 and Figure 2).
Regarding claim 5, Gassmann teaches the method of claim 1 wherein determining the features of the object is based on a set of reference characteristics (see paragraph 0029-0030, the collected interior data includes object properties including object classification and subclassification [which use references to assign labels] which may include additional data depending on classifications).
Regarding claim 6, Gassmann teaches the method of claim 1 further comprising:
determining, based on the one or more images, at least one person in the vehicle cabin (see paragraph 0029, the collected interior object data may include object classification with the category of person to determine if a person is present); and
determining a position of the at least one person in the vehicle cabin (see paragraph 0029-0030, the collected interior data may include a position of the object within the vehicle as well the pose of the person)
wherein determining the danger level is additionally based on the position of the at least one person (see paragraph 0030-0031 and 0036, the interior object data include the safety risk associated with the person and the position of each object within the vehicle. The interior object data is uses to produce the digital twin which is used to produce the safety score).
Regarding claim 8, Gassmann teaches the method of claim 1 wherein the danger level is initially determined when the vehicle is started and dynamically re-determined based on a driving velocity of the vehicle (see paragraph 0036 and 0046 and Figure 3, the scoring process is started by a pre-selected operating behavior [includes acceleration, speed, braking, and gear shifting] which may a have a pre-set value, and evaluate the operating behaviors that may occur).
Regarding claim 9, Gassmann teaches the method of claim 1 wherein outputting the warning signal:
is further based on an occupancy status of the vehicle (see paragraph 0062-0063 and Figure 4, the analytics and learning module can be used to analyze the interior objects including the empty seats, number of passengers, and number of passengers compared to capacity [interpreted as occupancy status]. The output from the analytics and learning module can be used to alter the scoring, which is used to generate the warning [0037]);
includes blocking, based on the danger level, the vehicle to be started and/or to be operated (see paragraph 0066, the in-vehicle messages [warnings] may be used to restrict operation of the vehicle); and
includes outputting one or more control signals changing, based on the danger level, the occupancy status of the vehicle when the vehicle is driving (see figure 4 and paragraph 0061 and 0063, the output of the analytics and learning module including empty seats, number of passengers, and number of passengers compared to capacity are used to update the scoring results which is used to output instructions or warning messages [0056]).
Regarding claim 11, Gassmann teaches the method of claim 1 wherein:
the one or more images includes images that are taken by one or more visible light and/or infrared cameras and/or an in-cabin radar (see paragraph 0039, the interior sensor directed towards the interior for collecting interior object data includes cameras and radar sensors);
the one or more cameras are located inside the vehicle cabin and/or include images taken by time-of-flight cameras or structured light cameras or stereo cameras (see paragraph 0039, the system includes interior sensors directed to the interior of the vehicle for collecting interior object data, where the interior sensor may include cameras and LIDAR sensors);
and the method further includes illuminating the vehicle cabin with infrared light and/or radio waves (see paragraph 0039, the sensor system includes Light Detection and Ranging (LiDAR) sensor which emits light [in the infrared spectrum] to illuminate the area).
Regarding claim 12, Gassmann teaches the method of claim 11 wherein at least one of the one or more cameras surveils a footwell area of the vehicle (see paragraph 0028, the safety system [including the interior sensor for example cameras] may obtain interior object data inside the vehicle which includes the cabin [interpret to include the whole cabin including the footwell]).
Regarding claim 13, Gassmann teaches a system for determining movement of an object in a vehicle cabin in a vehicle, the system comprising (see paragraph 0028 and 0033, the safety system analyzes the in-vehicle safety of internal objects which can move in, out , and about the vehicle, and be re-asses as there may be changes during the course of the trip):
a sensor system (see paragraph 0072 and 0074 and Figure 6, the apparatus includes one or more sensors for receiving data associated with the interior object data)
a data processing system (see paragraph 0073 and Figure 6, the apparatus includes processor 610 which includes one or more processor units which are configured to receive interior object data); and
interfaces for outputting a warning signal (see paragraph 0038, the passenger message module may provide a message on the screen indicating the risk. The apparatus 600 may include any of safety system features of safety system 100, 200, 300, 400, or 500 which includes the message module, 0072),
wherein the system is configured to perform the method of claim 1.
Regarding claim 14, Gassmann teaches a vehicle comprising: the system of claim 13 (see Figure 6, the apparatus includes sensors [0039 ] in the vehicles as well as on-vehicle processors and displaying the warning within the vehicle [0038]).
Claim 15 is analogous to the method of claim 1, thus is analyzed and rejected similar to claim 1.
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 4, 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Gassmann in view of Gronau US20240265554 (hereinafter “Gronau”).
Regarding claim 4, Gassmann teaches the method of claim 2.
Gassmann does not explicitly teach tracking the movement state of the object includes executing machine learning algorithms and/or being based on a three-dimensional (3D) reference model of the vehicle cabin.
Gronau teaches teach tracking the movement state of the object includes executing machine learning algorithms and/or being based on a three-dimensional (3D) reference model of the vehicle cabin (see paragraph 0162, tracking the objects in depth images [3D images] using correlation trackers [correlation trackers estimate an object’s position in new frames using previous images (depth images)]. The use of machine learning algorithms to detect the location and orientation of objects, 0134).
Gronau and Gassmann are analogous art because they are from the same field of endeavor of a system for the detection of objects within the interior cabin of a vehicle to determine safety risks associated with the status of the objects.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify Gassmann to include the use of machine learning algorithms or 3D reference models for the tracking of objects as taught by Gronau. The motivation for doing so would have been to allow for the tracking the number of passengers and the movements of passengers within the vehicle (Gronau, paragraphs 0196).
Regarding claim 7, Gassmann teaches the method of claim 6 wherein determining the danger level is further based on (see Figure 1 and 0036, the safety system generated the digital twin from the interior object data, vehicle situation data, and vehicle configuration data, and the digital twin is provided to the safety score module to generate a safety score:
an estimation of an impact of the object on driving safety (see paragraph 0036, evaluate the safety impact of an occurrence of each operating behavior to the object or the objects in the vehicle and then generate a safety score);
an estimation of a weight of the at least one object (see paragraph 0029, the collected interior object data includes an estimated weight each interior object;
a driving velocity of the vehicle (see paragraph 0035, the vehicle situation data [used to produce the digital twin, see Figure 1] may include the speed);
a position of the at least one object in the vehicle (see paragraph 0029, the collected interior object data may include an array of information about each interior object include a position of the object within the vehicle);
a determination of whether the object is secured at its position in the vehicle and, in response to the determination being positive, decreasing the danger level (see paragraph 0031, the interior object data may also include safety-related attributes for each interior object including whether the person or object restraint or secured with straps. The safety scoring module may determine a lower level of risk based on an object’s transitional state and securement); and
Gassmann does not teach determine whether the at least one person is a driver or a passenger of the vehicle.
Gronau teaches determine whether the at least one person is a driver or a passenger of the vehicle (see paragraph 0126, determining if the identified occupant is the vehicle s driver or the vehicle’s passenger to adjust the output specific to the occupant’s position).
Gronau and Gassmann are analogous art because they are from the same field of endeavor of a system for the detection of objects within the interior cabin of a vehicle to determine safety risks associated with the status of the objects.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify Gassmann to determining if the person or driver as taught by Gronau. The motivation for doing so would have been to allow the output to be compatible with the identified occupant (Gronau, paragraphs 0126).
Regarding claim 10, Gassmann and Gronau teaches the method of claim 7.
Gassmann teaches outputting the warning signal is further based on whether the at least one person the warning signal is addressed to is the driver or the passenger of the vehicle (see paragraph 0038, the passenger message module may generate warnings for the driver or the passengers related to the interior objects and their scored behavior).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of references cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY R. HAUK whose telephone number is (571)272-5966. The examiner can normally be reached M-F 8:00-5:00.
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, Chan Park can be reached at 571-272-7409. 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.
/EMILY ROSE HAUK/
Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
Prosecution Insights