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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on January 17, 2025, December 23, 2025, March 5, 2026 and June 25, 2026 are in compliance with 37 CFR 1.97 and 1.98. Accordingly, the IDSs have been considered by the examiner and placed in the file.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The BRIs are used for purposes of searching for prior art, but cannot be incorporated into the claims. Claim limitations must be given their plain meaning unless such meaning is inconsistent with the specification. MPEP 2111.01. BRIs for some of the claim limitations are provided below. Should Applicant believe that other interpretations are warranted, Applicant should point to the portions of the present disclosure that clearly show that a different interpretation is appropriate.
Regarding the use of alternative or optional language used in the claims such as “or”, “and/or”, etc., under MPEP 2111.04, claim scope is not limited by claim language that suggests or makes optional but does not require elements or steps. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 fall into the statutory class of process. Notwithstanding that these claims fall into statutory classes, these claims are directed to ineligible subject matter, namely, an abstract idea.
The USPTO has enumerated groupings of abstract ideas that are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent (See MPEP §2106.04(a)). The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Under Step 2A, Prong One of the Alice/Mayo test, a determination is made as to whether the claim recites one of the judicial exceptions, i.e., an abstract idea, a law of nature or a natural phenomenon. The operations recited in claim 1 comprise a mental process that falls under enumerated grouping 3). All of the operations recited in independent claim 1 can be performed in the mind of a human being. In particular, a human being can determine a rate of event signals generated by an event-based sensor in response to changes associated with a specific object by, for example, viewing a time vs. magnitude plot of event signals generated by an event-based sensor is displayed on a display device and counting the number of event signals that occur over a period of time to determine the rate. Similarly, if a report is generated of the occurrence of event signals generated by an event-based sensor over time, a user can view the report and determine the rate of event signals. With regard to determining a brightness of the specific object, this step can also be performed in the mind of a human who views the display or report data and determines brightness based on the displayed or reported data.
Once it has been determined that the claim under examination recites an abstract idea, then Step 2A, Prong Two of the Alice/Mayo test must be performed to determine whether any additional elements are recited in the claim that integrate the abstract idea into a practical application (See MPEP §§2106.04(d), 2106.05(a)-(c) and (e)-(h)).
In claim 1, no additional elements are recited other than the mental process steps. Likewise, claims 2-20 recite additional mental process steps that can be performed in the mind of a human based on event signal data reported or displayed, but do not recite additional elements over and above the mental process. Therefore, claims 2-20 are also directed to ineligible subject matter and are therefore rejected under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publ. Appl. No. 2025/0225665 A1 to Kenig (hereinafter referred to as “Kenig”) in view of U.S. Publ. Appl. No. 2024/0236519 A1 to Kodama (hereinafter referred to as “Kodama”).
Regarding claim 1, Kenig discloses a method for remote monitoring (Abstract: “The invention is a system capable of detecting, classifying, tracking, and calculating an aerial objects' trajectory”), comprising:
determining, from event signals obtained from an event-based vision sensor, a
rate of the event signals generated in response to changes associated with a specific object (Para. [0053] discloses determining the rate of the event signals generated by the event-based vision sensor (sensors of the neuromorphic camera 100, Figs. 1A and 1B) based in the polarities, p, of the brightness changes of the sensor outputs over a given time period, Δt. Para. [0059] discusses the event-based sensors of the camera 100 and the output 105 of the sensors), and
determining a brightness of the specific object from exposure measurements associated with the event signals (Kenig does not explicitly disclose determining a brightness of a specific object from exposure measurements, although Kenig does disclose classifying specific objects based on the brightness changes, p, of the event signals, para. [0049]: “[t]he system may classify the object and calculate its flying trajectory using event-based image processing and AI algorithms.” See also para. [0045]: “[t]he aerial objects may be aerial vehicles or projectiles such as drones, balloons, birds, artillery, rockets, mortars, missiles and bullets. By analyzing the captured object frequencies, shape, size and behavior, the camera can distinguish between objects even with only a few captured pixels.”).
Kodama, in the same field of endeavor, discloses processing the changes in brightnesses of pixels of an event-based vision sensor (EVS) to obtain gradation data indicative of the brightness of the object being imaged with the EVS (Para. [0122]: “[a]ccording to the present embodiment, as in the first embodiment, a reading circuit 24 only reads the luminance change signals of a detection pixel row, so that the EVS data can be outputted at high speeds. Furthermore, in the present embodiment, the gradation data indicating the luminance value of an object to be imaged can be obtained in addition to the EVS data indicating the presence or absence of a detected event.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the object detection, classification and tracking system and method of Kenig based on the teachings of Kodama to determine the brightness of specific objects in addition to the other information obtained by Kenig. One of ordinary skill in the art would have been motivated to make the modification to provide additional information that can be used to detect, classify and track objects. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (e.g., modifying software executed the processor of Kenig to obtain brightness values for the objects based on gradation data corresponding to rows of pixel values output by the sensors of EVS camera 100).
Regarding claim 2, Kenig discloses that the specific object is located in a region remote from the sensor, and the sensor is configured to generate event signals in response to changes it senses in the remote region (As discussed above in the rejection of claim 1, the brightness changes p of the signals output by the EVS camera 100 are event signals that correspond to changes the camera senses. The camera can be ground based and used to detect arial objects that are remote from the camera, para. [0064]: “FIG. 3 illustrates the use of the ground configuration of the event-based camera 100 to detect an aerial object 400, such as multi-rotors drones 401, fixed-wing drones 402, mortars 403, bullets 404, balloons 405, cruise missiles 406, and rockets 407. The event-based camera 100 captures the environment or scene 104 with a field of view and range depending on the optical element 101 attached to the camera 100 and outputs event data 105 in real-time.”).
Regarding claim 3, Kenig discloses that the sensor can be located on Earth (Fig. 3, para. [0064] discloses the event-based camera 100 being a “ground configuration”) and the remote region is in space (Para. [0064] discloses that the remote region can be space because the aerial object being detected, classified and tracked can be “rockets 407”).
Regarding claim 4, Kenig discloses that the specific object can be an aerial object such as a drone, a rocket, a bird, a missile, etc. (Para. [0064]), as indicated above in the rejection of claims 2 and 3.
Regarding claim 5, Kenig discloses logging variations in the rate over time and determining information about the specific object based on the variations in the rate over time (Para. [0053] of Kenig discloses determining the rate of the event signals generated by the event-based vision sensor (sensors of the neuromorphic camera 100, Figs. 1A and 1B) based in the polarities, p, of the brightness changes of the sensor outputs over time periods, Δt. Para. [0053] discloses that this information is stored as “frequency data”, which constitutes logging variations in the rate over time, and used to determine information about the specific objects being detected, classified and tracked, para. [0063]: “using that captured information, the detection system AI and Machine vision algorithms can classify the object based on shape, speed, and the frequency of the changes in the scene, such as the frequency of the spinning propellers.”).
Regarding claim 6, as indicated above in the rejection of claim 5, Kenig discloses that the determining information based on variations in the rate over time comprises identifying repeating patterns in the rate over time and determining a rotational speed of the specific object about one or more axes based on a frequency at which those patterns repeat (Para. [0063], the frequency data is used by the AI and machine vision algorithms to identify “the frequency of the spinning propellers”).
Regarding claim 16, Kenig discloses adjusting an orientation of the sensor based on athe trajectory of a said specific object (Para. [0078]: “FIG. 11 illustrates the use of an event-based camera 100 having a pan, tilt and zoom gimbal 180 configuration that could be combined with other optics element 110 such as secondary cameras, thermal camera, IR illuminator to scan an area in unison, to better coordinate tracking the detected objects 400 during movement. The processor may use the object's computed trajectory to estimate subsequent positions and thus control the camera system orientation.”).
Regarding claim 17, Kenig necessarily discloses that location information associated with the event signals is adjusted to account for changes in the orientation of the event-based camera 100 because Kenig discloses changing the orientation of the camera during tracking of moving objects. If the location information associated with the event signals is not adjusted when changing the orientation of the camera 100, it would not be possible to project or predict the trajectory of the object being tracked in order to intercept it, as discussed in paras. [0051], [0057] and [0070]-[0073].
Regarding claim 18, the rejection of claim 5 applies mutatis mutandis to claim 18.
Regarding claim 19, Kenig discloses inferring or determining information about the specific object based on variations over time in the rate together with: (i) information on the specific object obtained from sources other than the or each said sensor (Fig. 13, Para. [0080]: “The event-based camera 100 can work as a standalone or integrated 102 with laser rangefinder device 111, which can help the event-based camera 100 to aim a pulsed laser 111A to specific aerial object and detect the laser reflection 111B, back to the laser rangefinder 111C or 111D to be detected by the event-based camera 100 to calculate the time took to the laser to fire out from the laser diode and back to the source, by using the known light speed and basic calculation the distance to the pointed target can be calculated.”).
Regarding claim 20, Kenig discloses that the information on the specific object obtained from sources other than the event-based camera 100 comprises data from other sensors (Fig. 13, Para. [0080], laser rangefinder 111C is another sensor that is being used to make time-of-flight (TOF) measurements).
Claims 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kenig in view of Kodama as applied to claims 1-6 and 16-20 and further in view of an article entitled “Event-Based Color Segmentation With A High Dynamic Range Sensor”, published April 11, 2018 in Frontiers In Neuroscience (hereinafter referred to as “Marcireau”).
Regarding claim 8, Kenig does not explicitly disclose determining the rate of the event signals from two EVSs having different color filters. Marcireau, in the same field of endeavor, discloses an event-based color segmentation system that uses multiple EVSs with different color filters to generate event signals based on scene changes (The Introduction section discusses the neuromorphic cameras used and Section 2.1 discloses the system setup, which includes three event-based color sensor equipped with red, green or blue bandpass filters. Section 2.2 discusses the color event signals that are produced based on changes in colors in the scene captured by the cameras).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the object detection, classification and tracking system and method of Kenig as modified by Kodama further based on the teachings of Marcireau to use multiple EVSs with respective color filters for generating the event signals. One of ordinary skill in the art would have been motivated to make the modification to provide additional information in terms of color that can be used to detect, classify and track objects. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (e.g., using multiple cameras equipped with color filters as described in Marcireau and shown in Fig. 3).
Regarding claim 9, Kenig does not explicitly disclose determining material characteristics of the specific object based on differences in the event rate data. Marcireau discloses determining material characteristics of the specific object, namely the color of the object, based on differences in the event rate data from each of the sensors (Section 4 of Marcireau discloses that the outputs of the three sensors are used to determine absolute color of the object and that this is sufficient to perform object tracking).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the object detection, classification and tracking system and method of Kenig as modified by Kodama further based on the teachings of Marcireau to use multiple EVSs with respective color filters for generating the event signals and using the event rate data to determine material characteristics of objects such as color. One of ordinary skill in the art would have been motivated to make the modification to provide additional information in terms of color that can be used to detect, classify and track objects. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (e.g., using multiple cameras equipped with color filters as described in Marcireau and shown in Fig. 3).
Regarding claim 10, the BRI for limitation (i), based on para. [0011] of the present disclosure, is that the center of the specific object is determined from event signals generated over time based on event signals associated with events corresponding to pixel positions that are a predetermined distance from the center of the object. Kenig does not explicitly disclose this limitation.
This limitation is disclosed in Marcireau. Specifically, Marcireau discloses determining the center of the specific object based on event signals corresponding to event positions that are a predetermined distance from the center. Specifically, Section 2.5 of Marcireau discloses associating an event with a specific object by obtaining a count of the number of prior events associated with the same object that were generated less than one second before in a six-by-six square window around the event’s position and used to determine the object’s center position. Only events with at least thirty neighbors in this spatio-temporal are taken into account for updating the object’s mean position.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the object detection, classification and tracking system and method of Kenig as modified by Kodama further based on the teachings of Marcireau to use the center computation algorithm of Marcireau to determine the centers of the objects based on the color event signals. One of ordinary skill in the art would have been motivated to make the modification to improve object tracking by also using color information to track objects. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (e.g., using multiple cameras equipped with color filters as described in Marcireau and shown in Fig. 3).
Regarding claim 11, this claim further defines step (ii) of claim 10. However, step (ii) is optional in claim 10 and is also not required by the language of claim 11. Consequently, the limitations of claim 11 are optional. Therefore, the rejection of claim 10 applies to this claim.
Regarding claim 12, Kenig does not explicitly disclose re-determining the center of the specific object periodically based on subsequent event signals that are associated with the specific object. As indicated above in the rejection of claim 10, Mariceau discloses periodically updating, i.e., re-determining, the center position of the specific object as part of the object tracking process by using event signals in a 6x6 window that includes the previously determined center, Section 2.5: “[o]nce an event is associated with an object, we count the number of prior events associated with the same object that were generated less than one second before in a six-by-six square window around the event’s position. Only events with at least thirty neighbors in this spatio-temporal window are taken into account for updating the object’s mean position.”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the object detection, classification and tracking system and method of Kenig as modified by Kodama further based on the teachings of Marcireau to use the center computation and updating algorithm of Marcireau to determine and re-determine the centers of the objects based on the color event signals. One of ordinary skill in the art would have been motivated to make the modification to improve object tracking by also using color information to track objects. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (e.g., using multiple cameras equipped with color filters as described in Marcireau and shown in Fig. 3).
Allowable Subject Matter
Claims 7 and 13-15 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome the rejection under 35 U.S.C. 101.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 7, none of the prior art teaches or suggests calculating a temporal length of a spike in the rate and/or brightness to determine an angular width of a specular reflection that caused that spike.
Regarding claim 13, none of the prior art teaches or suggests that determining the rate of those event signals generated in response to changes associated with the specific object comprises:
determining, for each pixel associated with those event signals, the time interval between each of those event signals associated with that pixel;
determining a moving mean of those time intervals for a predetermined number of event signals; and
determining the rate at the time of each of those event signals to be the inverse of the moving mean at that time.
As indicated above in the rejections, Kenig and Marcineau both disclose determining, for each pixel associated with those event signals, the time interval between each of those event signals associated with that pixel. In addition, Marcireau also discloses determining a moving mean of those time intervals for a predetermined number of event signals (Sections 2.5 and 3). However, none of the prior art teaches or suggests, in combination with the other limitations recited in claim 1, determining the rate at the time of each of those event signals to be the inverse of the moving mean at that time.
Claim 14 recites allowable subject matter due to its dependence from claim 1.
Regarding claim 15, none of the prior art teaches or suggests determining the brightness of the specific object from the exposure measurements comprises summing all associated said exposure measurements within a predetermined distance from the center of the specific object.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publ. Appl. No. 2026/0143248 A1 discloses an EVS system and method that utilize an event detecting unit that acquires a difference between the luminance value represented by the luminance signal and a predetermined reference value and, in a case in which the difference exceeds an event detection threshold of a positive side or an event detection threshold of a negative side, detects an occurrence of the event and outputs the event data representing details of the event. An additional information generating unit that generates pixel information added to data of each pixel as additional information that is additionally disposed in event data on the basis of the event data; and a data transmitting unit that transmits pixel information in a frame structure in which the pixel information is embedded in the event data.
An article entitled “Event-Based Object Detection and Tracking for Space Situational Awareness”, by Afshar et al., published December 15, 2020 in IEEE Sensors Journal, Vol. 20, No. 24, discloses systems that use event-based sensors to provide high temporal resolution imaging data of the sparse space environment allowing rapid sensor fusion, low bandwidth communication and operation during continuous operation during day and night time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL J SANTOS whose telephone number is (571)272-2867. The examiner can normally be reached M-F 9-5.
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/DANIEL J. SANTOS/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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