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 .
This is a response to U.S. Patent Application No. 18/423,143 filed on 01/25/2024 in which Claims 1 – 10 were presented for examination.
Status of the Claims
Claims 1, 3, 5 and 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, Claims 1, 2 and 5 – 10 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) and Claims 3 and 4 are rejected under 35 U.S.C. 103.
Examiner Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Objections
Claims 2, 4 and 5 are objected to because of the following informalities:
Claims 2, 4 and 5 recite “the visual alarm system”, however, claim 1 recites ”a visual alert system”. Accordingly, the Claims should be amended to be consistent with the terms and avoid antecedent basis problems.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation 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 broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processing unit configured with algorithms for analyzing…” and “ a module for predicting…” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5 and 8-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “a processing unit configured with algorithms for analyzing…” and “ a module for predicting…” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the functions in the claim. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 recites the limitation "the system" in the fourth limitation (limitation labeled d.). There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the trajectory prediction module” in line 1 – 2 of claim 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites “characterized by the substitution of traditional sound-based alarms with a visual alarm system that projects lights paths”. This claim language is indefinite because it is unclear how the system is substituting traditional sound-based alarms with a visual alarm system that projects light paths, furthermore, it is unclear what is considered traditional sound based alarms.
For purposed of examination, the examiner interpreted the claims as the capability of implementing a visual alarm system as disclosed in Pandya.
Claim 5 further recites “wherein the visual alarm system uses visible, light scattered light paths in the air, offering a clear and immediate visual cue for collision avoidance and safety guidance, particularly effective in noisy industrial environments where sound-based alarms may be less discernible and lacking directional guidance to avoid collision”.
A broad limitation together with a narrow limitation that falls within the broad limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 5 recites the broad recitation “wherein the visual alarm system uses visible, light scattered light paths in the air”, and the claim also recites “offering a clear and immediate visual cue for collision avoidance and safety guidance, particularly effective in noisy industrial environments where sound-based alarms may be less discernible and lacking directional guidance to avoid collision” which is the narrower statement of the limitation. The claim is considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 8 recites “calculating trajectory and collision risks using advanced computational methods”, the term “advanced” is a relative term which renders the claim indefinite. The term “advanced” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Furthermore, after despite reviewing of the disclosure, the examiner cannot determine what is considered “advanced computational methods”.
For examination purposes, the term "advanced" will be given no weight and the claims is interpreted as calculating trajectory and collision risks as disclosed in Pandya.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2 and 5 – 10 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Pandya et al. (US 11,676,291) (hereinafter, Pandya).
Regarding Claim 1, Pandya teaches an imaging system capable of capturing data in a variety of industrial environments (Pandya teaches an adaptive multimodal system for managing safety in an industrial environment and acquiring an image data by a computer vision component (See Pandya’s Abstract). Pandya in col 3 lines 6 – 9, further teaches that computer vision (CV) techniques or computer vision systems have been used to process images to extract high-level understanding of the scene (e.g., industrial workplace, construction site, etc.);
a. A processing unit configured with algorithms for analyzing data to identify and track moving entities and assess potential hazard (Pandya in Col. 8 lines 14 – 32, teaches that the multimodal safety system may be a location and/or time-based system that may utilize real-time multimodal sensor data for incident detection. The multimodal safety system can analyze data collected from multi-modal sensory systems or devices to generate contextual descriptions of 3D scene which may include object detection, object classification, extraction of the scene depth and estimation of relative positions of objects, detection of an unsafe situation, identify safety operation processes, detect an incident (e.g., trip, slip or fall detection), identify a hazardous situation or hazardous conditions in a work zone of a workplace, identify an efficient workflow for one or more workers and one or more groups within a workplace and various others);
b. A module for predicting the movement and possible collision courses if detected entities, using motion analysis techniques (Pandya in Col. 13 – line 62 – Col. 14 line 9, teaches receiving data from the personnel device such as the mobile tag device 105 or wearable device (e.g., motion data physiological data, etc.). Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag);
c. A visual alert system, operating on principles of light scattering, to project discernible alerts in response to predicted risks (Pandya in Col. 10 lines 60 – 65, teaches that upon the prediction of an impending adverse event (e.g., entering a hazardous work zone, reaching a fatigue level, etc.), intervention such as rhythmic cue, audio, visual, or tactile stimulus may be delivered to the worker via the wearable device, mobile tag device 105 or sensors. Pandya in Col. 18 lines 46 – 58, that the LIDAR system may configure distribution and density of pixels/measurement points dynamically in response to real-time conditions. For instance, the LIDAR system may be capable of dynamically adjusting the resolution of laser beam points emitted into selected region in 3D space, and the x and/or y resolution of pixels in selected region in a 3D point cloud image 200, 210. In some situations, non-uniform pixels (points) distribution may be preferred so that dense light spots may be emitted into a selected region to provide more details in the region of interest. For instance, light spots may be preferred to be denser in a region where a target object is detected and further details are desired. Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached);
d. Wherein the system provides dynamic, real-time visual guidance and warnings, adaptable to different industrial settings (Pandya in Col. 14 lines 10 – 20, teaches that data may be transmitted from the edge computing server 140 to the personnel device which may include, for example, alert, warning, feedback instructions/guidance of worker behaviors or navigational information. Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag. Pandya in Col. 24 lines 13 – 24, further teaches that the control signals/commands may control an optical assembly of a selected imaging device 321-1 to achieve a desired zoom level in a region of interest. The optical assembly may comprise components that are useful for adjusting a light path, line of sight, field of view and the like).
Regarding Claim 2, Pandya teaches the limitations contained in parent Claim 1. Pandya further teaches:
wherein the visual alarm system dynamically adjusts light paths using an algorithms that responds in real-time to changes in predicted collision paths, enhancing situational awareness (Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Pandya in Col 20 line 60 – Col. 21 line 2, teaches that the safety inference engine 213 may include an input feature generation module 351 and a trained predictive model 353. For example, the detection of an incident (e.g., trip, slip, fall, collision), detection of behavior not in compliance with safety protocol, predicting a hazardous zone or condition, predicting a collision may be provided using the predictive model 353. A predictive model may be a trained model or trained using machine learning algorithm. Pandya in Col. 21 lines 12 – 22, further teaches that the input feature generation module 351 may generate input feature data to be processed by the trained predictive model 353. The input feature generation module 351 may receive data from the computer vision system 320, the LIDAR system 330, and the real-time locating system 310, extract features and generate the input feature data. The data 307 received from the computer vision system, the LIDAR system, and the RTLS may include raw sensor data. Pandya in Col. 24 lines 13 – 24, further teaches that the optical assembly may comprise components that are useful for adjusting a light path, line of sight, field of view and the like).
Regarding Claim 5, Pandya teaches the limitations contained in parent Claim 1. Pandya further teaches:
characterized by the substitution of traditional sound-based alarms with a visual alarm system that projects light paths, wherein the visual alarm system uses visible, light scattered light paths in the air, offering a clear and immediate visual cue for collision avoidance and safety guidance, particularly effective in noisy industrial environments where sound-based alarms may be less discernible and lacking directional guidance to avoid collision (Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag).
Regarding Claim 6, Pandya teaches a method for enhancing safety in industrial environments (Pandya teaches an adaptive multimodal system for managing safety in an industrial environment and acquiring an image data by a computer vision component (See Pandya’s Abstract). Pandya in col 3 lines 6 – 9, further teaches that computer vision (CV) techniques or computer vision systems have been used to process images to extract high-level understanding of the scene (e.g., industrial workplace, construction site, etc.), involving:
a. capturing environmental data using an imaging system (Pandya in col 3 lines 6 – 9, further teaches that computer vision (CV) techniques or computer vision systems have been used to process images to extract high-level understanding of the scene (e.g., industrial workplace, construction site, etc.);
b. analyzing the data with a processing unit to identify potential hazard (Pandya in Col. 8 lines 14 – 32, teaches that the multimodal safety system may be a location and/or time-based system that may utilize real-time multimodal sensor data for incident detection. The multimodal safety system can analyze data collected from multi-modal sensory systems or devices to generate contextual descriptions of 3D scene which may include object detection, object classification, extraction of the scene depth and estimation of relative positions of objects, detection of an unsafe situation, identify safety operation processes, detect an incident (e.g., trip, slip or fall detection), identify a hazardous situation or hazardous conditions in a work zone of a workplace, identify an efficient workflow for one or more workers and one or more groups within a workplace and various others);
c. predicting movement and collision courses of detected entities using a motion analysis module (Pandya in Col. 13 – line 62 – Col. 14 line 9, teaches receiving data from the personnel device such as the mobile tag device 105 or wearable device (e.g., motion data physiological data, etc.). Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag);
d. projecting visual alerts using a light scattering-based alarm system in response to predicted risks (Pandya in Col. 10 lines 60 – 65, teaches that upon the prediction of an impending adverse event (e.g., entering a hazardous work zone, reaching a fatigue level, etc.), intervention such as rhythmic cue, audio, visual, or tactile stimulus may be delivered to the worker via the wearable device, mobile tag device 105 or sensors. Pandya in Col. 18 lines 46 – 58, that the LIDAR system may configure distribution and density of pixels/measurement points dynamically in response to real-time conditions. For instance, the LIDAR system may be capable of dynamically adjusting the resolution of laser beam points emitted into selected region in 3D space, and the x and/or y resolution of pixels in selected region in a 3D point cloud image 200, 210. In some situations, non-uniform pixels (points) distribution may be preferred so that dense light spots may be emitted into a selected region to provide more details in the region of interest. For instance, light spots may be preferred to be denser in a region where a target object is detected and further details are desired. Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Pandya in Col. 23 lines 10 – 16, further teaches that the computer vision system 330 may adopt any suitable optical techniques to generate the computer vision (CV) output data (e.g., 3D or depth information of the target scene). For example, the CV output data may be generated using passive methods that only require images, or active methods that require controlled light to be projected into the target scene);
e. dynamically adjusting he visual alert in real-time based on changes in predicted collision paths (Pandya in Col. 18 lines 46 – 58, that the LIDAR system may configure distribution and density of pixels/measurement points dynamically in response to real-time conditions. For instance, the LIDAR system may be capable of dynamically adjusting the resolution of laser beam points emitted into selected region in 3D space, and the x and/or y resolution of pixels in selected region in a 3D point cloud image 200, 210. In some situations, non-uniform pixels (points) distribution may be preferred so that dense light spots may be emitted into a selected region to provide more details in the region of interest. For instance, light spots may be preferred to be denser in a region where a target object is detected and further details are desired. Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached).
Regarding Claim 7, Pandya teaches the limitations contained in parent Claim 6. Pandya further teaches
wherein the step of projecting visual alerts includes using a light-scattering technique to generate discernible light paths along the trajectories of predicted collisions (Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag. Pandya in Col. 23 lines 10 – 16, further teaches that the computer vision system 330 may adopt any suitable optical techniques to generate the computer vision (CV) output data (e.g., 3D or depth information of the target scene). For example, the CV output data may be generated using passive methods that only require images, or active methods that require controlled light to be projected into the target scene. Pandya in Col. 24 lines 13 – 24, further teaches that the optical assembly may comprise components that are useful for adjusting a light path, line of sight, field of view and the like).
Regarding Claim 8, Pandya teaches a computer-implemented process for collision prediction in industrial settings (See Pandya Abstract and Col. 10 lines 60 – 65), including:
A. real-time data capture from a depth-perceptive imaging system (Pandya in Col 3 lines 6 – 16, teaches that computer vision (CV) techniques or computer vision systems have been used to process images to extract high-level understanding of the scene (e.g., industrial workplace, construction site, etc.). CV techniques may have the capabilities of object detection, object tracking, action recognition or generating descriptions of a scene (e.g., object detection, object classification, extraction of the scene depth and estimation of relative positions of objects, extraction of objects' orientation in space, anomaly detection, detection of an unsafe situation, etc.) Pandya in Col. 11 lines 59 – 61, further teaches that the image data captured by the camera may be grayscale image with depth information at each pixel coordinate (i.e., depth map));
b. utilizing algorithms for object recognition and movement tracking (Pandya in Col. 8 lines 14 – 32, teaches that the multimodal safety system may be a location and/or time-based system that may utilize real-time multimodal sensor data for incident detection. The multimodal safety system can analyze data collected from multi-modal sensory systems or devices to generate contextual descriptions of 3D scene which may include object detection, object classification, extraction of the scene depth and estimation of relative positions of objects, detection of an unsafe situation, identify safety operation processes, detect an incident (e.g., trip, slip or fall detection), identify a hazardous situation or hazardous conditions in a work zone of a workplace, identify an efficient workflow for one or more workers and one or more groups within a workplace and various others);;
c. analyzing frame-by-frame data to identify and assess movement of entities (Pandya in Col. 27 lines 22 – 36, further teaches that the visual input may be adjusted by controlling one or more acquisition parameters of the visual input device (e.g., camera zoom level, image acquisition rate, focal length, tilt/pan angle, etc.) to track the object of interest and to provide further detailed analysis. The computer vision processing methods or processes may be adjusted (operation 415) based at least in part on the identity of the object of interest to produce further analysis result (e.g., worker behavior, PPE verification, equipment movement tracking, etc.);
d. calculating trajectory and collision risks using advanced computational methods (Pandya in Col. 13 – line 62 – Col. 14 line 9, teaches receiving data from the personnel device such as the mobile tag device 105 or wearable device (e.g., motion data physiological data, etc.). Pandya in Col. 19 lines 13 – 33, further teaches that the target of interest 201 such as identity of the target of interest and the location may be generated by the real-time locating system (RTLS). For instance, location or trajectory of a worker or equipment in a construction site may be provided by a mobile tag device and upon receiving such location tracking per identity data, a region of interest may be determined and the LIDAR system may allocate denser pixel distribution to the region of interest to provide detailed 3D measurement or further LIDAR analysis data (e.g., collision detection). As an example, a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Similarly, payload carried by a crane with no RTLS tag attached to it may be tracked by LIDAR more precisely when coming into dangerous proximity to a worker carrying RTLS tag);
e. activating a responsive visual alarm system based on the calculated risks (Pandya in Col. 10 lines 60 – 65, teaches that upon the prediction of an impending adverse event (e.g., entering a hazardous work zone, reaching a fatigue level, etc.), intervention such as rhythmic cue, audio, visual, or tactile stimulus may be delivered to the worker via the wearable device, mobile tag device 105 or sensors. Pandya in Col. 18 lines 46 – 58, that the LIDAR system may configure distribution and density of pixels/measurement points dynamically in response to real-time conditions. For instance, the LIDAR system may be capable of dynamically adjusting the resolution of laser beam points emitted into selected region in 3D space, and the x and/or y resolution of pixels in selected region in a 3D point cloud image 200, 210. In some situations, non-uniform pixels (points) distribution may be preferred so that dense light spots may be emitted into a selected region to provide more details in the region of interest. For instance, light spots may be preferred to be denser in a region where a target object is detected and further details are desired. Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached).
Regarding Claim 9, Pandya teaches the limitations contained in parent Claim 8. Pandya further teaches:
further including adapting the visual alarm system to be compatible with various industrial environments and safety protocols (Pandya teaches an adaptive multimodal system for managing safety in an industrial environment and acquiring an image data by a computer vision component (See Pandya’s Abstract). Pandya in Col. 7 lines 55 – 67, further teaches provide situational awareness functionality, safety management based on location tracking and unsafe situation detection that may be used in various contexts, including construction site, shipping, mining, healthcare, manufacturing environments and various other industries. The real-time location tracking, behavior enforcement and situational awareness may be used for various uses, such as Internet of Things (IoT) platforms, health-monitoring software applications and business processes or industrial workplace management, and for organizations in energy, manufacturing, aerospace, automotive, chemical, pharmaceutical, telecommunications, healthcare, the public sector, and others).
Regarding Claim 10, Pandya teaches the limitations contained in parent Claim 8. Pandya further teaches:
wherein the visual alarm system includes optimizing the light paths for visibility under diverse environmental lighting conditions (Pandya in Col. 19 lines 13 – 33, further teaches that a large piece of moving equipment, such as a crane with an extended three-dimensional shape may be identified by a mobile tag device attached thereto, and the 3D position and movement of the crane is more precisely tracked in the 3D scene relative to other objects by the LIDAR system by directing denser light spots into the region of interest for collision avoidance with objects that have no RTLS tags attached. Pandya in Col 20 line 60 – Col. 21 line 2, teaches that the safety inference engine 213 may include an input feature generation module 351 and a trained predictive model 353. For example, the detection of an incident (e.g., trip, slip, fall, collision), detection of behavior not in compliance with safety protocol, predicting a hazardous zone or condition, predicting a collision may be provided using the predictive model 353. A predictive model may be a trained model or trained using machine learning algorithm. Pandya in Col. 21 lines 12 – 22, further teaches that the input feature generation module 351 may generate input feature data to be processed by the trained predictive model 353. The input feature generation module 351 may receive data from the computer vision system 320, the LIDAR system 330, and the real-time locating system 310, extract features and generate the input feature data. The data 307 received from the computer vision system, the LIDAR system, and the RTLS may include raw sensor data. Pandya in Col. 23 lines 10 – 16, further teaches that the computer vision system 330 may adopt any suitable optical techniques to generate the computer vision (CV) output data (e.g., 3D or depth information of the target scene). For example, the CV output data may be generated using passive methods that only require images, or active methods that require controlled light to be projected into the target scene. Pandya in Col. 24 lines 13 – 24, further teaches that the optical assembly may comprise components that are useful for adjusting a light path, line of sight, field of view and the like).
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Pandya in view of Silva et al. (US 2021/0370921) (hereinafter Silva).
Regarding Claim 3, Pandya teaches the limitations contained in parent Claim 1.
However, Pandya does not specifically disclose further including an algorithm within the trajectory prediction module for detailed collision risk assessment, capable of predicting future positions of detected entities based on their movement patterns.
Silva teaches a vehicle safety system within an autonomous or semi-autonomous vehicle may predict and avoid collisions between the vehicle and other moving objects in the environment (See Silva’s Abstract). Silva in par 0011, further teaches that vehicle safety system within the vehicle may identify other objects within the environment, determine one or more perturbed trajectories for the other objects, and then predict potential collisions by determining intersections between the vehicle trajectory and the perturbed trajectories (or candidate trajectories) for the objects. For instance, the vehicle safety system may compare the trajectory (or planned path) for the vehicle to multiple possible perturbed trajectories for another object moving within the environment. To determine the perturbed trajectories for an object, the vehicle safety system may modify (or perturb) one or more parameters of the current (or perceived) trajectory of the object, such as the velocity, acceleration, and/or steering angle rate. For a perturbed trajectory or multiple perturbed trajectories, the vehicle safety system may determine whether the perturbed trajectories intersect the planned trajectory for the vehicle. If one or more perturbed trajectories intersect the planned vehicle trajectory, then the vehicle safety system may predict that a potential collision is possible between the vehicle and the object, and may determine an action for the vehicle to take based on the potential collision prediction.
Silva in par 0023, further teaches that the vehicle safety system thus may perform a wider range of possible actions based on the collision probability and related factors, to improve the operation of the vehicle in avoiding and/or mitigating potential collisions. Silva in par 0079, further teaches that probability component 544 may use the different trajectory probabilities to weight the results of the analyses of the perturbed trajectories, and/or weighting the overall collision probability calculation in favor of the more likely perturbed trajectories and against the less likely perturbed trajectories. For example, the probability component 544 may execute rules and/or models based observed movement patterns and behaviors from other vehicles, to determine more or less likely perturbed trajectories.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Silva with the teachings as in Pandya to provide in the objects of interest of Pandya the safety system of Silva. The motivation for doing so would have been to effectively take actions for the vehicle based on the collision predictions and probabilities (See Silva’s Abstract).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Pandya in view of Glatfelter et al. (US 2017/0337820) (hereinafter, Glatfelter).
Regarding Claim 4, Pandya teaches the limitations contained in parent Claim 1.
However, Pandya does not specifically disclose wherein the visual alarm system delineates safe zones not currently under predicted collision paths and illumination, with these zones being dynamic and visually distinct to guide personnel effectively.
Glatfelter teaches Systems and methods for collision avoidance include a plurality of sensors respectively disposed on a plurality of movable objects, wherein each sensor is configured to transmit signals indicating the location of the movable object (See Glatfelter’s Abstract). Glatfelter in par 0021, further teaches that the ability to sense and anticipate potential collisions is provided with the ability to notify of a potential dangerous condition (e.g., imminent danger). For example, the system is configured to illuminate potentially dangerous areas on factory/warehouse floors (e.g., areas identified as areas where a potential collision could occur).
Glatfelter in par 0051 and Fig. 5, further teaches that the alerts may be delivered to the object 106 or individual 110 in various different ways, including projection of lights. The location at which the alerts are projected may be moved as the object 106 is moved, for example, by adjusting the position of the projection device 114 to move the projected alert (e.g., light/image) that is being projected on the factory floor. Glatfelter in par 0059, further teaches that the process 500 includes, for each object, deriving all center locations of the object(s) 106 and/or individual(s) 110 and a defined radius there around at 520. This step at 520 defines an anticipatory collision avoidance area around the object(s) 106 and/or individual(s) 110 (safety zone or radius), which if another object 106 and/or individual 110 is detected therein, a warning may be provided. Glatfelter in par 0061, further teaches that based on threshold values and the comparisons at 522, if no potential collisions are determined, then at 532 an output is generated to display an indication that there are no potential collisions. This displayed information may be used by an operator of equipment moving the object 106 to confirm that continued movement of the object 106 is in a safe movement zone. If one or more potential collisions are determined, then at 534 an output (e.g., output signal) is generated and a warning is provided.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Pandya with the teachings as in Glatfelter to include the projection device of Glatfelter in the system of Pandya. The motivation for doing so would have been to effectively project floor markings that includes a message that is differently colored, thus effectively providing dynamic visual communication (see Glatfelter’s par 0025 – 0026).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/ARIEL MERCADO-VARGAS/Primary Examiner, Art Unit 2118