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 .
DETAILED ACTION
The action is in response to the Applicant’s communication filed on 11/08/2024.
Claims 1-19 are pending, where claims 1 and 19 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/08/2024 has been filed on the filing date of the application. The submission is in-compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification objections (Title)
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: SAFEGUARDING A HUMAN TO PROTECT FROM HAZARD SOUIRCES ROBOT OR MACHINE IN AN INDUSTRIAL ENVIRONEMENT. MPEP 606.01
Claim Objections
Claim 15 is/are objected to because of the following informalities:
Claim 15 recites the terminology "and/or", what is actually being performed by the alternatively claimed language. However, it will be assumed "or” for the purposes of examination.
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 15 and 17 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
a) Claim 15 recite the limitation “different interference”. The term “different” recites indefinite to the level of ordinary skill in the pertinent art because the elements not actually disclosed or clearly defined in the specification, and it is a broad term rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d).]
b) Claim 17 recite the limitation “likewise”. The phrase “likewise” renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "or the like") and it is a broad term, thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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 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.
Claims 1-19 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Vu, et al. USPGPub No. 20220379474 A1.
As to claim 1, Vu discloses A method of safeguarding a machine in which a sensor monitors the machine and generates data thereon that are evaluated so that a hazardous situation is recognized and the machine is safeguarded in the event of a hazardous situation, wherein a check is made in a detection capability check whether an estimation of a hazardous situation is possible and the machine is otherwise safeguarded (Vu [0050-105] “3D workcell 100 monitored by a plurality of cameras - - includes a robot 106 controlled by a conventional robot controller 108 - due to the presence of the person P, at least some portion of robot controller 108 occluded from all cameras - classify workcell regions as occupied, unoccupied, or unknown - camera affirmatively detects no obstructions in this intervening space - objects in the workcell 100 previously identified and captured by control system 112 - control system 112 maintains an internal representation of the workcell 100 at the voxel level, with voxels marked as occupied, unoccupied, or unknown - classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans - classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans” [0107-113] [0002-38] [abstract] see Fig. 1-7, sensors, camera periodically updates intensity-based 3D sensor detects, analysis module detects objects based on signal intensity, achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides sensor monitors the machine and generates data thereon that are evaluated so that a hazardous situation is recognized and the machine is safeguarded in the event of a hazardous situation, wherein a check is made in a detection capability check whether an estimation of a hazardous situation is possible and the machine),
wherein the sensor data are evaluated in a process of machine learning having at least one figure of quality in the detection capability check and an estimation of a hazardous situation is only considered possible with a sufficient figure of quality (Vu [0050-105] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - marked as occupied, unoccupied or unknown - sensor detects - signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects - classify objects - analysis module 342 configured to identify intruding objects unexpected or humans - classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, control system with camera periodically updates intensity-based 3D sensor detects and classify objects based on threshold value signal intensity attenuated by objects, analysis module detects objects based on signal intensity, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides sensor data evaluated in a process of machine learning having at least one figure of quality in the detection capability check and an estimation of a hazardous situation is only considered possible with a sufficient figure of quality).
Application and the reference Vu are analogous arts from the same field of endeavor and contain overlapping structural and functional similarities and both contain safety of machine and human using sensor and machine learning algorithms.
It would be therefore obvious to one having ordinary skill in the art at the time of the invention that achieve sufficient reliability for safety ratings with multiple images based on different image properties, using analysis module and sensor or camera identifies or detects objects potentially hazardous situations are assumed as safeguarding machine with sensor monitors machine, generates evaluated data and recognizes hazardous situation.
As to the independent claim 19, the claims recite similar limitations as the independent claim 1 and rejected using same rational as stated above.
As to claim 2, the combination of Vu further discloses The method in accordance with claim 1, wherein the figure of quality is binary (Vu [0050-87] “analysis module 342 identified all objects in the monitored area 100 considered for safety purposes - variety of actions taken and control outputs generated - generate control signals - binary, indicating either safe or unsafe conditions, or can be more complex - actions safe and unsafe - binary signal indicating whether an intrusion of either occupied or potentially occupied volume detected in a particular zone - controlled machinery to stop or limit the operation of the machinery” [0107-113] [0002-38] [abstract] see Fig. 1-7).
As to claim 3, the combination of Vu further discloses The method in accordance with claim 1, wherein the figure of quality quantitatively evaluates at least one interference property of the sensor data (Vu [0050-87] “analysis module 342 identified all objects in the monitored area 100 considered for safety purposes - variety of actions taken and control outputs generated - generate control signals - binary, indicating either safe or unsafe conditions, or more complex - actions safe and unsafe - binary signal indicating whether an intrusion of either occupied or potentially occupied volume detected in a particular zone - controlled machinery to stop or limit the operation of the machinery” [0107-113] [0002-38] [abstract] see Fig. 1-7, analysis module identifies all objects with variety of actions and generate control signals for safe or unsafe or complex conditions obviously provides interference property of the sensor data).
As to claim 4, the combination of Vu further discloses The method in accordance with claim 1, wherein the process of machine learning has a classifier (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques Euclidean clustering, K-means clustering and Gibbs-sampling clustering - to determine optimal clustering - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, analysis module, machine learning, clustering techniques and classification of regions obviously provides process of machine learning has a classifier).
As to claim 5, the combination of Vu further discloses The method in accordance with claim 1, wherein the process of machine learning has a neural network (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques Euclidean clustering, K-means clustering and Gibbs-sampling clustering - to determine optimal clustering - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, analysis module, machine learning, neural networks obviously provides the process of machine learning has a neural network).
As to claim 6, the combination of Vu further discloses The method in accordance with claim 1, wherein the sensor is a camera or a 3D sensor (Vu [0050-87] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - nature of 3D data - space map 345 a voxel grid - marked as occupied, unoccupied or unknown - sensor detects -- signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects” [0107-113] [0002-38] [abstract] see Fig. 1-7).
As to claim 7, the combination of Vu further discloses The method in accordance with claim 1, wherein the evaluation of the sensor data has an object detector (Vu [0050-105] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - nature of 3D data - space map 345 a voxel grid - marked as occupied, unoccupied or unknown - sensor detects -- signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects” [0107-113] [0002-38] [abstract] see Fig. 1-7, analysis module and sensor or camera detects objects).
As to claim 8, the combination of Vu further discloses The method in accordance with claim 7, wherein the object detector is configured to recognize foreign objects in the environment of the machine (Vu [0050-105] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - nature of 3D data - space map 345 a voxel grid - marked as occupied, unoccupied or unknown - sensor detects -- signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects - classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that moving in unpredictable ways and that can be harmed by the machinery - classify people versus other unknown foreign objects - machinery comes into contact with workpieces , hazardous for machinery to come into contact with people - analysis module 342 - distinguish between workpieces and unknown foreign objects” [0107-113] [0002-38] [abstract] see Fig. 1-7, analysis module and sensor or camera detects objects including foreign objects).
As to claim 9, the combination of Vu further discloses The method in accordance with claim 1, wherein the process of machine learning is trained by supervised learning in which a plurality of training examples from sensor data having a known associated figure of quality are used as the training data (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques Euclidean clustering, K-means clustering and Gibbs-sampling clustering - to determine optimal clustering - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning obviously provides process of machine learning is trained by supervised learning in which a plurality of training examples from sensor data having a known associated figure of quality are used as the training data).
As to claim 10, the combination of Vu further discloses The method in accordance with claim 7, wherein the training examples are changed by at least one interference property at at least one interference intensity to generate further training examples (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques Euclidean clustering, K-means clustering and Gibbs-sampling clustering - to determine optimal clustering - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning obviously provides changed by at least one interference property at at least one interference intensity to generate further training examples).
As to claim 11, the combination of Vu further discloses The method in accordance with claim 10, wherein the sensor data have images and at least one of the following interference properties is used: Image at least regionally too light or too dark, image at least regionally blurred, movement artefacts, static or/and dynamic image noise, image regions swapped over, address errors, image incomplete (Vu [0050-105] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - nature of 3D data - space map 345 a voxel grid - marked as occupied, unoccupied or unknown - sensor detects -- signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects - classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that moving in unpredictable ways and that can be harmed by the machinery - classify people versus other unknown foreign objects - machinery comes into contact with workpieces , hazardous for machinery to come into contact with people - analysis module 342 - distinguish between workpieces and unknown foreign objects” [0107-113] [0002-38] [abstract] see Fig. 1-7, control system with camera periodically updates voxel grid space map, sensor detects, threshold value depend on sensor, intensity-based 3D sensor (ToF camera) detects and classify objects based on threshold value signal intensity attenuated by objects, analysis module detects objects based on signal intensity obviously provides sensor data, Image at least regionally too light or too dark, image at least regionally blurred, movement artefacts, static or/and dynamic image noise, image regions swapped over, address errors, image incomplete).
As to claim 12, the combination of Vu further discloses The method in accordance with claim 10, wherein the training data are evaluated to determine whether a hazardous situation has been recognized despite the interference property and to associate a figure of quality with the training example depending on the result (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides determine whether a hazardous situation has been recognized despite the interference property and to associate a figure of quality with the training example depending on the result).
As to claim 13, the combination of Vu further discloses The method in accordance with claim 12, wherein the training data are evaluated by an object detector (Vu [0050-87] “cameras registered, control system 112 periodically updates space map 345 at a high fixed frequency - nature of 3D data - space map 345 a voxel grid - marked as occupied, unoccupied or unknown - sensor detects -- signal and threshold value depend on the type of sensor being used - intensity-based 3D sensor (ToF camera) the threshold value a signal intensity, attenuated by objects” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties include supervised learning, analysis module and sensor or camera detects objects obviously provides evaluated by an object detector).
As to claim 14, the combination of Vu further discloses The method in accordance with claim 12, wherein the training data are evaluated by the same process that is also used for recognizing a hazardous situation in the safeguarding of the machine (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides recognizing a hazardous situation in the safeguarding of the machine).
As to claim 15, the combination of Vu further discloses The method in accordance with claim 10, wherein the training data are changed with increasing interference intensity and/or different interference properties until an evaluation of the changed training data no longer recognizes a hazardous situation (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides changed with increasing interference intensity and/or different interference properties until an evaluation of the changed training data no longer recognizes a hazardous situation).
As to claim 16, the combination of Vu further discloses The method in accordance with claim 9, wherein a figure of quality is already associated with training examples corresponding to a no longer present detection capability in which the evaluation has still recognized a hazardous situation to provide a safety margin (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides corresponding to a no longer present detection capability in which the evaluation has still recognized a hazardous situation to provide a safety margin).
As to claim 17, the combination of Vu further discloses The method in accordance with claim 1, wherein the evaluation of the sensor data for recognizing a hazardous situation likewise has a process of machine learning (Vu [0050-87] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module and sensor or camera identifies or detects objects potentially hazardous situations obviously provides evaluation of the sensor data for recognizing a hazardous situation likewise has a process of machine learning).
As to claim 18, the combination of Vu further discloses The method in accordance with claim 17, wherein the process of machine learning is a process of machine learning of the detection capability check in a dual function (Vu [0050-106] “classification of regions - if control system 112 is monitoring space - no objects at all during normal operation - monitor an area in which there are at least some objects during normal operation - analysis module 342 configured to identify intruding objects unexpected or humans -classification to cluster individual occupied voxels into objects analyzed at a higher level - analysis module 342 implement - clustering techniques - to determine optimal clustering - tracking clusters of points employed to identify incorrect and potentially hazardous situations - techniques employed to enable analysis module 342 to distinguish between workpieces and humans - deep-learning algorithms convolutional neural networks (CNNs) - recurrent neural networks (RNNs) - multiple algorithms or neural networks based on different image properties used - achieving sufficient reliability for safety ratings - multiple images tracked over time, further enhancing reliability” [0107-113] [0002-38] [abstract] see Fig. 1-7, machine learning using multiple algorithms or neural networks based on different image properties to achieve sufficient reliability for safety ratings with multiple images tracked over time for further enhancing reliability includes supervised learning, analysis module with sensor or camera identifies or detects objects in real time effective monitoring for dynamic determination of safe zones obviously provides machine learning of the detection capability check in a dual function).
Citation of Pertinent Prior Art
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2141.02 VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, i.e., as a whole and 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record:
Satat, USPGPub No. 2025/0086950 A1 discloses a sensor system of an autonomous vehicle for fusing raw sensor data captured by a camera sensor and one or more depth sensors to generate a dense depth map.
Braband, USPGPub No. 2024/0296386 A1 discloses a supervised machine learning method for performing a technical process trained by comparing the output data calculated with input data describing the correct process result and calculating probability of failure based on comparison.
Hammes, et al. USPGPub No. 2020/0290205 A1 discloses a safety system for safeguarding a machine having sensor for producing safe and/or non-safe data and evaluation result of non-safe data checked with safe data.
D’Ercoli, et al. USPGPub No. 2020/0189103 A1 discloses a predictive system, and process predicts safety system activation in industrial environments when collaborative robots, automated guidance vehicles, and other robots are interacting between one another or a robot and human.
Metzler, et al. USPGPub No. 2022/0005332 A1 discloses a method for surveillance of a facility, surveillance sensors adapted for surveillance of a plurality of the facility elements and for generation of surveillance data, and state derivation to analyze surveillance data and derive state facility elements.
Schuster, USP No. 10,325,485 B1 discloses an industrial safety system to mitigate hazardous or potentially damaging interactions within monitored area.
Giering, et al. USP No. 11,422,546 B1 discloses a method includes fusing multi-modal sensor data from a plurality of sensors detected based on region of interest using deep convolutional neural network.
Honal, et al. USPGPub No. 2021/0302544 A1 discloses an optoelectronic sensor and method for detecting distance measurement data of objects in a monitoring area.
Galera, et al. USPGPub No. 2015/0332463 A1 discloses an industrial safety to integrate industrial control with optical area monitoring using an imaging sensor performing selective time-of-flight analysis on specified pixel array.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Md Azad whose telephone @(571)272-0553 or email: md.azad@uspto.gov. The examiner can normally be reached on Mon-Thu 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Md Azad/
Primary Examiner, Art Unit 2119
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