NON-FINAL REJECTION
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 action is in response to the original application filed on January 9th, 2023.
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 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subjected matter because the claimed invention is directed towards software per se.
With respect to independent claim 1, The claim recites a “A distributed neural network communication system comprising an edge system and a cloud system communicatively coupled to each other, wherein the edge system is configured to receive an input data stream from an input device and to conduct partial or preliminary feature extraction at the edge system, and the cloud system is configured to communicate with the edge system to receive an extracted feature vector from the edge system and to conduct further feature extraction at the cloud system to yield a final feature vector, thereby to enhance accuracy of a neural network of the distributed neural network communication system.”(Emphasis added). Also, as noted in the specification pp. 12 [0057] which recites, “Aspects of the present invention may be embodied as a system, method and/or computer program product. Accordingly, aspects of the present invention may take the form of hardware, software and/or a combination of hardware and software that may generally be referred to herein as "components", "units", "modules", "systems", "elements", or the like.” (Emphasis added). Under a broadest reasonable interpretation of the claim language, the “distributed neural network communication system” of claim 1 and its dependents may encompass computer program product, and is thus directed towards software per se. Examiner suggests amending claim 1 and its dependents to recite, “A distributed neural network communication system which contains processors that are linked to memory which contain computer readable instructions which are stored and used by the processors to execute said method, the system further comprising an edge system and a cloud system communicatively coupled to each other.”(Emphasis added). Dependent claims 2-13 depend on rejected claim 1, and are also rejected under 35 USC § 101 by virtue of this dependency. Appropriate correction is required.
Claim 19 is rejected under 35 U.S.C. § 101 because the claimed invention is directed towards non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subjected matter because the claimed invention is directed towards signals per se.
With respect to independent claim 19, The claim recites “A computer program product for distributed neural network communication, the computer program product comprising at least one computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by at least one computer to cause the at least one computer to perform a plurality of operations comprising:” (Emphasis added). Also, as noted in the specification pp. [0058] which recites, “Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable storage medium having computer-readable program code embodied thereon. A computer-readable storage medium may, for instance, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. In the context of this specification, a computer-readable storage medium may be any suitable medium capable of storing a program for execution or in connection with a system, apparatus, or device. Program code/instructions may execute on a single device, on a plurality of devices (e.g., on local and remote devices), as a single program or as part of a larger system/package.” (Emphasis added). Under a broadest reasonable interpretation of the claim language, the “computer program product” of claim 19 and its dependents may encompass transitory signals, and is thus directed towards signals per se. Examiner suggests amending claim 19 and its dependents to recite, “A computer program product for distributed neural network communication, the computer program product comprising at least one computer-readable storage medium, wherein the computer readable storage medium is non-transitory storage medium and having program instructions embodied therewith, the program instructions being executable by at least one computer to cause the at least one computer to perform a plurality of operations comprising:” (Emphasis added).
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is
directed to an abstract idea without significantly more. The analysis of the claims will
follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50
(“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1, does not fall into any of the four statutory categories and therefore is not eligible subject matter. However, should the applicant amend the claims to not recite software per se, the claims would recite a system containing processors, memory and/or generic computing systems. For examination purposes, claim 1 will be interpreted to be a system containing processors, memory and/or generic computing systems. Therefore, under this interpretation, claim 1 will fall under the statutory category of a machine and will be evaluated under 35 U.S.C. 101 as such.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“to conduct further feature extraction at the cloud system to yield a final feature vector, thereby to enhance accuracy of a neural network of the distributed neural network communication system.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine features within that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the edge system is configured to receive an input data stream from an input device and to conduct partial or preliminary feature extraction at the edge system, and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“the cloud system is configured to communicate with the edge system to receive an extracted feature vector from the edge system and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the edge system is configured to receive an input data stream from an input device and to conduct partial or preliminary feature extraction at the edge system, and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“the cloud system is configured to communicate with the edge system to receive an extracted feature vector from the edge system and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 2 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 2 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 2 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“extract a preliminary feature vector from the input data stream through a preliminary feature analysis process;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“compress and/or encrypt the preliminary feature vector; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. Compression and encryption of data consists of using mathematical functions to alter or obfuscate data. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“decrypt and/or decompress the preliminary feature vector; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. decompression and decryption of data consists of using mathematical functions to return altered or obfuscated data back to a prior state. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“analyze the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with the neural network, thereby yielding a final feature vector.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate received video data to identify features or objects. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the edge system is configured to: receive the input data stream from the input device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“transmit the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“wherein the cloud system is configured to: receive the compressed and/or encrypted preliminary feature vector from the edge system;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the edge system is configured to: receive the input data stream from the input device;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“transmit the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“wherein the cloud system is configured to: receive the compressed and/or encrypted preliminary feature vector from the edge system;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 3 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 3 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 3 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the model run by the cloud system is an artificial intelligence (Al) model designed to detect objects or events and/or draw inferences from the input data stream.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate received video data to identify features or objects from video data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 4 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 4 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 4 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the preliminary feature analysis process includes motion detection or object detection, involving dividing frames of the input data stream into frame blocks and analyzing each frame block to check structural similarity to previous frames or frame blocks.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for changes in state. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 5 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 5 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 5 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein as part of the preliminary feature analysis process, feature extraction is carried out based only on the frame blocks in respect of which significant changes were detected.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for major changes. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 6 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 6 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 6 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 7 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 7 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 7 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 8 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 8 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 8 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 9 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 9 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 9 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the distributed neural network communication system is further configured to implement a feedback mechanism.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the distributed neural network communication system is further configured to implement a feedback mechanism.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 10 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 10 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 10 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the cloud system is configured to determine whether a collection rate or sampling rate should be increased or decreased and to communicate a change to the collection rate or the sampling rate to the edge system, via the feedback mechanism, as an input instruction.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to determine sampling rates based on observations or after an evaluation. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 11 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 11 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 11 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the edge system is an edge computer directly connected to the input device and connected to the cloud system via the Internet.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the edge system is an edge computer directly connected to the input device and connected to the cloud system via the Internet.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 12 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 12 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 12 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the input device is a video camera; and the input data stream is video data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the input device is a video camera; and the input data stream is video data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 13 does not fall under any of the four statutory categories required for patent eligibility as stated above. For examination purposes, claim 13 will be interpreted to be a machine which contains processors, memory and/or generic computing systems to execute the claimed process. Therefore, claim 13 is directed to the category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the preliminary feature vector and/or final feature vector is associated with a detected object or a detected event.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the preliminary feature vector and/or final feature vector is associated with a detected object or a detected event.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 14, recites “A distributed neural network communication method, the method comprising:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“extracting, by the edge system, a preliminary feature vector from the input data stream through a preliminary feature analysis process;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“compressing and/or encrypting, by the edge system, the preliminary feature vector;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. Compression and encryption of data consists of using mathematical functions to alter or obfuscate data. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“decrypting and/or decompressing the preliminary feature vector at the cloud system; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. decompression and decryption of data consists of using mathematical functions to return altered or obfuscated data back to a prior state. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“analyzing, by the cloud system, the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with a neural network, thereby yielding a final feature vector.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate received video data to identify features or objects. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the model run by the cloud system is an artificial intelligence (Al) model designed to detect objects or events and/or draw inferences from the input data stream.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate received video data to identify features or objects from video data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the preliminary feature analysis process includes motion detection or object detection, involving dividing frames of the input data stream into frame blocks and analyzing each frame block to check structural similarity to previous frames or frame blocks, and” nder its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for changes in state. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein as part of the preliminary feature analysis process, feature extraction is carried out based only on the frame blocks in respect of which significant changes were detected.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for major changes. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system, and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system, and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. INSERTREASON. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. INSERTREASON. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 19, does not fall under any of the four statutory categories and therefore is not eligible subject matter. However, should the applicant amend the claims to not recite signals per se, the claims would recite a system containing processors, non-transitory storage medium and/or generic computing systems. For examination purposes, claim 19 will be interpreted to be a computer program product which uses processors, non-transitory storage medium and/or generic computing systems. Therefore, under this interpretation, claim 19 will fall under the statutory category of a machine and will be evaluated under 35 U.S.C. 101 as such.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“extracting, by the edge system, a preliminary feature vector from the input data stream through a preliminary feature analysis process;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a stream of video data and evaluate it for features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“compressing and/or encrypting, by the edge system, the preliminary feature vector;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. Compression and encryption of data consists of using mathematical functions to alter or obfuscate data. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“decrypting and/or decompressing the preliminary feature vector at the cloud system; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. decompression and decryption of data consists of using mathematical functions to return altered or obfuscated data back to a prior state. These functions can be performed by a human with pen and paper. This claim discloses a math operation and therefore is ineligible.
“analyzing, by the cloud system, the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with a neural network, thereby yielding a final feature vector.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate received video data to identify features or objects. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 20 recites, “The computer program product as claimed in claim 19, wherein the computer readable storage medium is a non-transitory storage medium.”, therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the computer- readable storage medium is a non-transitory storage medium.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the computer- readable storage medium is a non-transitory storage medium.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al., (Wang et al., “SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning”, 2020, pp. 2519-2528, hereinafter “Wang”).
Regarding claim 1, Wang teaches, “A distributed neural network communication system comprising an edge system and a cloud system communicatively coupled to each other, wherein the edge system is configured to receive an input data stream from an input device and to conduct partial or preliminary feature extraction at the edge system, and the cloud system is configured to communicate with the edge system to receive an extracted feature vector from the edge system and to conduct further feature extraction at the cloud system to yield a final feature vector, thereby to enhance accuracy of a neural network of the distributed neural network communication system.” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server. As shown in Fig. 2(b), the workflow of SurveilEdge can be divided into offline and online stages. The offline stage is conducted on the Cloud, where cameras of similar scenes are categorized into the same context-specific clusters, each of which produces a context-specific training dataset. At the online stage, when a user inputs a new query command, a CNN with respect to this query will be trained on the Cloud, based on the context-specific training dataset established in the offline stage. Therefore, we call this CNN as a context and query specific (CQ-Specific) CNN. Here, a CQ-specific CNN is a few-classification CNN that forgoes the full generalization of classifying. However, the model complexity is reduced, and the capability of identifying certain kinds of objects is enhanced. The training process normally takes less than a minute with a normal Cloud GPU (e.g. NVIDIA Telsa P4). The trained CQ-Specific CNN is then deployed at the corresponding edge server. With the CQspecific CNN at the edge and a highly-accurate CNN (e.g. ResNet-152) on the Cloud, each video frame will be processed to produce real-time query responses (normally one to several seconds). SurveilEdge adopts an intelligent scheme including task scheduling and parameter adjustment for system load balancing and the accuracy-latency tradeoff of queries.” This article discloses a system which consists of a set of surveillance cameras connected to edge and cloud servers. The cameras will communicate with the edge servers and the edge server communicates with other edge servers and cloud servers. The edge server will perform an evaluation on data sent from the connected cameras. If further evaluation is need or if the cloud server is available, the edge server will send data to the could server for a more accurate evaluation of the camera data. This system is designed for surveillance and object detection in video feeds.)
Regarding claim 2, Wang teaches, “wherein the edge system is configured to: receive the input data stream from the input device;” (Moving Object Detection and Query Target Recognition, pp. 2521; “As shown in Fig.2 (b), when the fine-turned CQ-CNN achieves satisfactory accuracy, it will be deployed at corresponding edge devices. Then, SurveilEdge will process video frames to respond to the user-defined query.” The proposed system in this article consists of a network of video cameras which collect video data. This data is then evaluated at an edge server. Fig. 2(b) discloses that raw video frames are sent from surveillance cameras to an edge device where object detection and object classification is performed.)
“extract a preliminary feature vector from the input data stream through a preliminary feature analysis process;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and related information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D.” As seen in fig. 2(b), the edge device receives the raw video footage and will detect objects in those images. The detected images and meta data from the camera are processed at the task allocator to be sent to an available server for classification.)
“compress and/or encrypt the preliminary feature vector; and” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. Further the edge nodes use SQlite which is a relational database management system which contains its own encryption methods)
“transmit the compressed and/or encrypted preliminary feature vector to the cloud system;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The inference outputs an identification confidence f representing the probability that this image is a query object. If f is above the upper threshold α, this image is confidently considered as a query object. If f is below the bottom threshold β, this image is confidently considered as not a query object. If f is between α and β, this image cannot be confidently classified by the CQ-CNN at the edge. In this case, this image will be transmitted to the Cloud and be classified with the highly-accurate CNN (e.g. ResNet-152).” This system is able to filter the data sent to the cloud server. If further evaluation is need on the images, they are sent from the edge server to the cloud server where a more robust classification is performed.)
“wherein the cloud system is configured to: receive the compressed and/or encrypted preliminary feature vector from the edge system;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The inference outputs an identification confidence f representing the probability that this image is a query object. If f is above the upper thresholdα, this image is confidently considered as a query object. If f is below the bottom threshold β, this image is confidently considered as not a query object. If f is between α and β, this image cannot be confidently classified by the CQ-CNN at the edge. In this case, this image will be transmitted to the Cloud and be classified with the highly-accurate CNN (e.g. ResNet-152).” This system is able to filter the data sent to the cloud server. If further evaluation is need on the images, they are sent from the edge server to the cloud server where a more robust classification is performed. Since the cloud needs to perform further evaluation, it must receive the data which is sent to it.)
“decrypt and/or decompress the preliminary feature vector; and” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. This teaches the uses of set protocol for sending and receiving data, this includes encryption/decryption.)
“analyze the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with the neural network, thereby yielding a final feature vector.” (Methodology, pp. 2524; “We applied MobileNet-v2 as the skeleton of specific CNNs deployed on edge devices, and ResNet- 152 as the high-accuracy classifier deployed on the Cloud.” This system uses a classification model on the cloud server to further evaluate data. For their experiment they used ResNet-152 which is Deep Residual Learning neural network used for Image Recognition. This model will produce labels for input images.)
Regarding claim 3, Wang teaches, “wherein the model run by the cloud system is an artificial intelligence (Al) model designed to detect objects or events and/or draw inferences from the input data stream.” (Methodology, pp. 2524; “We applied MobileNet-v2 as the skeleton of specific CNNs deployed on edge devices, and ResNet- 152 as the high-accuracy classifier deployed on the Cloud.” This system uses a classification model on the cloud server to further evaluate data. For their experiment they used ResNet-152 which is Deep Residual Learning neural network used for Image Recognition. This model will produce labels for input images.)
Regarding claim 4, Wang teaches, “wherein the preliminary feature analysis process includes motion detection or object detection, involving dividing frames of the input data stream into frame blocks and analyzing each frame block to check structural similarity to previous frames or frame blocks.” (Moving Object Detection and Query Target Recognition, pp. 2521; “The basic idea of the algorithm is as follows: the detector extracts consecutive frames from real-time surveillance video streams with a regular time interval (e.g., one second), and checks pixel differences between selected frames. If pixels within a certain area change apparently across frames, it can be considered that a foreground object appears in this area.” The model in this article uses an algorithm to determine new objects in a frame. This will extract images based on a time interval, which would be a block of time.)
Regarding claim 5, Wang teaches, “wherein as part of the preliminary feature analysis process, feature extraction is carried out based only on the frame blocks in respect of which significant changes were detected.” (Moving Object Detection and Query Target Recognition, pp. 2521; “The basic idea of the algorithm is as follows: the detector extracts consecutive frames from real-time surveillance video streams with a regular time interval (e.g., one second), and checks pixel differences between selected frames. If pixels within a certain area change apparently across frames, it can be considered that a foreground object appears in this area. Detailed operations are as follows.” This is the overview of the moving object detection method. This will evaluate chunks of data and locate objects which are moving in the different frames.) and (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D.” The detected moving object are located and that information is passed to a task allocator for further processing. This will only perform on the detected foreground objects and not every image.)
Regarding claim 6, Wang teaches, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system.” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server.” This proposed system contains video surveillance devices connected to edge servers which communicate with a cloud server.) and (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” As stated above, this system uses an implementation of the MQTT protocol, Mosquito, for communication, which provides secure communications.)
Regarding claim 7, Wang teaches, “wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” As stated above, this system uses an implementation of the MQTT protocol, Mosquito, for communication between devices, which provides secure communications.)
Regarding claim 8, Wang teaches, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” (Task Scheduling and Parameter Adjustment, pp. 2522; “Considering spatio-temporal characteristics of surveillance videos we discussed in Subsection III-A, it is reasonable to balance the query load among edge devices with heterogeneous busy times. Due to the periodicity of the busy time, the dynamic adjustment of threshold parameters is beneficial to determine the tradeoff strategy between the latency and the accuracy of queries at different times. We have N edge devices and each edge device i maintains a queue of
Q
i
image packages waiting to be classified by its classifier. Let
t
i
denotes the estimated inference time of an image package at edge device i.” The workload in this system is split between edge devices and the cloud. If the cloud is overloaded the edge servers can send their image packages to other edge nodes to be processed. Using the broadest reasonable interpretation, this system discloses the use of multiple worker nodes performing classification inferences in parallel or separately depending on network factors.)
Regarding claim 9, Wang teaches, “wherein the distributed neural network communication system is further configured to implement a feedback mechanism.” (Task Scheduling and Parameter Adjustment, pp. 2522-2523; “Real-time task scheduling: For an edge device i, when a foreground object is detected, the real-time task scheduler will be triggered immediately to determine which device should classify this image package with the least latency, i.e. [See Equation (7)]. Each edge device i keeps a distributed database (SQLite4 in our implementation) alive to store parameters including α, β, as well as
t
i
and
Q
i
for all i. The update of
t
i
and
Q
i
is triggered by the feedback of edge classifiers. Then, the update of
t
i
or
Q
i
triggers the immediate update of α and β.” This system does incorporate a feedback mechanism between the edge server, task allocator and the could server. When α and β are altered the amount of data which gets reclassified by the more accurate cloud classifier is changed.)
Regarding claim 10, Wang teaches, “wherein the cloud system is configured to determine whether a collection rate or sampling rate should be increased or decreased and to communicate a change to the collection rate or the sampling rate to the edge system, via the feedback mechanism, as an input instruction.” (Task Scheduling and Parameter Adjustment, pp. 2523; “Real-time updates of α and β: If the classification latency exceeds the time interval of a query, we shrink the interval length of [β,α]. As a result, fewer images will be uploaded to the Cloud and classified again. Likewise, if the classification latency does not exceed the time interval of the query, the system is capable of reclassifying more images using the highly-accurate CNN on the Cloud to improve the reliability of the query. Therefore, the interval width of [β,α] will be increased, and more images will be transmitted to the Cloud after edge classifications.” This system is able to distribute network load between the edge servers and the cloud server. Depending on the α and β settings, more or less image packages will be sent to the cloud server for further classification. As stated, network inference times are taken into consideration as well when allocating image packages between edge nodes as well.)
Regarding claim 11, Wang teaches, “wherein the edge system is an edge computer directly connected to the input device and connected to the cloud system via the Internet.” (Performance of Homogeneous Edges and Cloud, pp. 2525; “Considering the satisfactory simulating results under the single edge and cloud setting, we constructed a real-world prototype with three homogeneous edge devices (each with an Intel Core i7-6700 3.4GHz quad-core CPU and 16 GB DDR4 RAM) and a public Cloud (with 8 logical cores of Intel Xeon E5-2682v4 CPU, 64 GB DDR4 RAM, and an NVIDIA Tesla P4 GPU) to evaluate the performance of SurveilEdge.” This article discloses the hardware used for the experiments. They disclose the use of a public cloud. By definition this is connected to the internet, meaning the edge devices and cameras communicate with the cloud server over the Internet.)
Regarding claim 12, Wang teaches, “wherein the input device is a video camera; and the input data stream is video data.” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server.” As seen in Fig. 2(a), this system comprises of multiple video cameras connected to edge servers. These video cameras provide video data to the edge servers for object detection and identification.)
Regarding claim 13, Wang teaches, “wherein the preliminary feature vector and/or final feature vector is associated with a detected object or a detected event.” (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and related information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D” The detected foreground object is extracted, along with meta data about the camera, and is sent to a task allocator. This sends the detected object data to a server for classification. As stated above the edge will classify the detected object and if further evaluation is needed, this object data will be sent to the cloud server.)
Regarding claim 14, Wang teaches, “A distributed neural network communication method, the method comprising:” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server. As shown in Fig. 2(b), the workflow of SurveilEdge can be divided into offline and online stages. The offline stage is conducted on the Cloud, where cameras of similar scenes are categorized into the same context-specific clusters, each of which produces a context-specific training dataset. At the online stage, when a user inputs a new query command, a CNN with respect to this query will be trained on the Cloud, based on the context-specific training dataset established in the offline stage. Therefore, we call this CNN as a context and query specific (CQ-Specific) CNN. Here, a CQ-specific CNN is a few-classification CNN that forgoes the full generalization of classifying. However, the model complexity is reduced, and the capability of identifying certain kinds of objects is enhanced. The training process normally takes less than a minute with a normal Cloud GPU (e.g. NVIDIA Telsa P4). The trained CQ-Specific CNN is then deployed at the corresponding edge server. With the CQspecific CNN at the edge and a highly-accurate CNN (e.g. ResNet-152) on the Cloud, each video frame will be processed to produce real-time query responses (normally one to several seconds). SurveilEdge adopts an intelligent scheme including task scheduling and parameter adjustment for system load balancing and the accuracy-latency tradeoff of queries.” This article discloses a system and a method which is able to evaluate images. Fig. 2(a) discloses the architecture of the network and Fig. 2(b) discloses the overall method of the system.)
“receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” (Moving Object Detection and Query Target Recognition, pp. 2521; “As shown in Fig.2 (b), when the fine-turned CQ-CNN achieves satisfactory accuracy, it will be deployed at corresponding edge devices. Then, SurveilEdge will process video frames to respond to the user-defined query.” The proposed system in this article consists of a network of video cameras which collect video data. This data is then evaluated at an edge server. Fig. 2(b) discloses that raw video frames are sent from surveillance cameras to an edge device where object detection and object classification is performed.)
“extracting, by the edge system, a preliminary feature vector from the input data stream through a preliminary feature analysis process;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and related information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D.” As seen in fig. 2(b), the edge device receives the raw video footage and will detect objects in those images. The detected images and meta data from the camera are processed at the task allocator to be sent to an available server for classification.)
“compressing and/or encrypting, by the edge system, the preliminary feature vector;” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. Further the edge nodes use SQlite which is a relational database management system which contains its own encryption methods)
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The inference outputs an identification confidence f representing the probability that this image is a query object. If f is above the upper threshold α, this image is confidently considered as a query object. If f is below the bottom threshold β, this image is confidently considered as not a query object. If f is between α and β, this image cannot be confidently classified by the CQ-CNN at the edge. In this case, this image will be transmitted to the Cloud and be classified with the highly-accurate CNN (e.g. ResNet-152).” This system is able to filter the data sent to the cloud server. If further evaluation is need on the images, they are sent from the edge server to the cloud server where a more robust classification is performed.)
“decrypting and/or decompressing the preliminary feature vector at the cloud system; and” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. This teaches the uses of set protocol for sending and receiving data, this includes encryption/decryption.)
“analyzing, by the cloud system, the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with a neural network, thereby yielding a final feature vector.” (Methodology, pp. 2524; “We applied MobileNet-v2 as the skeleton of specific CNNs deployed on edge devices, and ResNet- 152 as the high-accuracy classifier deployed on the Cloud.” This system uses a classification model on the cloud server to further evaluate data. For their experiment they used ResNet-152 which is Deep Residual Learning neural network used for Image Recognition. This model will produce labels for input images.)
Regarding claim 15, Wang teaches, “wherein the model run by the cloud system is an artificial intelligence (Al) model designed to detect objects or events and/or draw inferences from the input data stream.” (Methodology, pp. 2524; “We applied MobileNet-v2 as the skeleton of specific CNNs deployed on edge devices, and ResNet- 152 as the high-accuracy classifier deployed on the Cloud.” This system uses a classification model on the cloud server to further evaluate data. For their experiment they used ResNet-152 which is Deep Residual Learning neural network used for Image Recognition. This model will produce labels for input images.)
Regarding claim 16, Wang teaches, “wherein the preliminary feature analysis process includes motion detection or object detection, involving dividing frames of the input data stream into frame blocks and analyzing each frame block to check structural similarity to previous frames or frame blocks, and” (Moving Object Detection and Query Target Recognition, pp. 2521; “The basic idea of the algorithm is as follows: the detector extracts consecutive frames from real-time surveillance video streams with a regular time interval (e.g., one second), and checks pixel differences between selected frames. If pixels within a certain area change apparently across frames, it can be considered that a foreground object appears in this area.” The model in this article uses an algorithm to determine new objects in a frame. This will extract images based on a time interval, which would be a block of time.)
“wherein as part of the preliminary feature analysis process, feature extraction is carried out based only on the frame blocks in respect of which significant changes were detected.” (Moving Object Detection and Query Target Recognition, pp. 2521; “The basic idea of the algorithm is as follows: the detector extracts consecutive frames from real-time surveillance video streams with a regular time interval (e.g., one second), and checks pixel differences between selected frames. If pixels within a certain area change apparently across frames, it can be considered that a foreground object appears in this area. Detailed operations are as follows.” This is the overview of the moving object detection method. This will evaluate chunks of data and locate objects which are moving in the different frames.) and (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D.” The detected moving object are located and that information is passed to a task allocator for further processing. This will only perform on the detected foreground objects and not every image.)
Regarding claim 17, Wang teaches, “wherein the cloud system is coupled to a plurality of edge systems, each of the edge systems transmitting compressed and/or encrypted preliminary feature vectors to the cloud system, and” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server.” This proposed system contains video surveillance devices connected to edge servers which communicate with a cloud server.) and (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” As stated above, this system uses an implementation of the MQTT protocol, Mosquito, for communication, which provides secure communications.)
“wherein the cloud system comprises a message broker which is configured to handle streams of incoming messages from the edge systems, each message including a compressed and encrypted preliminary feature vector along with metadata associated with the preliminary feature vector.” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” As stated above, this system uses an implementation of the MQTT protocol, Mosquito, for communication between devices, which provides secure communications.)
Regarding claim 18, Wang teaches, “wherein the cloud system is configured such that analysis is conducted by separate worker nodes or worker modules substantially in parallel, each worker node/module implementing the inference engine in respect of a different feature vector or feature vector stream, thereby distributing Al tasks across difference devices, instances or processors.” (Task Scheduling and Parameter Adjustment, pp. 2522; “Considering spatio-temporal characteristics of surveillance videos we discussed in Subsection III-A, it is reasonable to balance the query load among edge devices with heterogeneous busy times. Due to the periodicity of the busy time, the dynamic adjustment of threshold parameters is beneficial to determine the tradeoff strategy between the latency and the accuracy of queries at different times. We have N edge devices and each edge device i maintains a queue of
Q
i
image packages waiting to be classified by its classifier. Let
t
i
denotes the estimated inference time of an image package at edge device i.” The workload in this system is split between edge devices and the cloud. If the cloud is overloaded the edge servers can send their image packages to other edge nodes to be processed. Using the broadest reasonable interpretation, this system discloses the use of multiple worker nodes performing classification inferences in parallel or separately depending on network factors.)
Regarding claim 19, Wang teaches, “A computer program product for distributed neural network communication, the computer program product comprising at least one computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by at least one computer to cause the at least one computer to perform a plurality of operations comprising:” (SurveilEdge Overview, pp. 2520; “We consider a network composed of cameras, edge devices, and a cloud server, as shown in Fig.2(a). Here, each edge device can serve multiple cameras, communicate with other edge devices, and communicate with the cloud server. As shown in Fig. 2(b), the workflow of SurveilEdge can be divided into offline and online stages. The offline stage is conducted on the Cloud, where cameras of similar scenes are categorized into the same context-specific clusters, each of which produces a context-specific training dataset. At the online stage, when a user inputs a new query command, a CNN with respect to this query will be trained on the Cloud, based on the context-specific training dataset established in the offline stage. Therefore, we call this CNN as a context and query specific (CQ-Specific) CNN. Here, a CQ-specific CNN is a few-classification CNN that forgoes the full generalization of classifying. However, the model complexity is reduced, and the capability of identifying certain kinds of objects is enhanced. The training process normally takes less than a minute with a normal Cloud GPU (e.g. NVIDIA Telsa P4). The trained CQ-Specific CNN is then deployed at the corresponding edge server. With the CQspecific CNN at the edge and a highly-accurate CNN (e.g. ResNet-152) on the Cloud, each video frame will be processed to produce real-time query responses (normally one to several seconds). SurveilEdge adopts an intelligent scheme including task scheduling and parameter adjustment for system load balancing and the accuracy-latency tradeoff of queries.” This article discloses a system and a method which is able to evaluate images. Fig. 2(a) discloses the architecture of the network and Fig. 2(b) discloses the overall method of the system.)
“receiving, at an edge system which is communicatively coupled to a cloud system, an input data stream from an input device;” (Moving Object Detection and Query Target Recognition, pp. 2521; “As shown in Fig.2 (b), when the fine-turned CQ-CNN achieves satisfactory accuracy, it will be deployed at corresponding edge devices. Then, SurveilEdge will process video frames to respond to the user-defined query.” The proposed system in this article consists of a network of video cameras which collect video data. This data is then evaluated at an edge server. Fig. 2(b) discloses that raw video frames are sent from surveillance cameras to an edge device where object detection and object classification is performed.)
“extracting, by the edge system, a preliminary feature vector from the input data stream through a preliminary feature analysis process;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The detected foreground images and related information including camera ID, capture time, etc. are packaged and passed to the task allocator. The allocator performs the task scheduling algorithm and determines the destination of each image package (i.e., the package comprised of the foreground image and related information) among all edge devices, as shown in Fig.5. The detailed task scheduling algorithm will be introduced in Subsection IV-D.” As seen in fig. 2(b), the edge device receives the raw video footage and will detect objects in those images. The detected images and meta data from the camera are processed at the task allocator to be sent to an available server for classification.)
“compressing and/or encrypting, by the edge system, the preliminary feature vector;” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. Further the edge nodes use SQlite which is a relational database management system which contains its own encryption methods)
“transmitting the compressed and/or encrypted preliminary feature vector to the cloud system;” (Moving Object Detection and Query Target Recognition, pp. 2522; “The inference outputs an identification confidence f representing the probability that this image is a query object. If f is above the upper threshold α, this image is confidently considered as a query object. If f is below the bottom threshold β, this image is confidently considered as not a query object. If f is between α and β, this image cannot be confidently classified by the CQ-CNN at the edge. In this case, this image will be transmitted to the Cloud and be classified with the highly-accurate CNN (e.g. ResNet-152).” This system is able to filter the data sent to the cloud server. If further evaluation is need on the images, they are sent from the edge server to the cloud server where a more robust classification is performed.)
“decrypting and/or decompressing the preliminary feature vector at the cloud system; and” (Methodology, pp. 2524; “For the communication among different nodes, the extremely lightweight publish/subscribe message transport protocol MQTT [30] was adopted. The distributed database management system SQLite was applied on edge devices for database access control.” This system uses an implementation of the MQTT protocol. This is an open-source program protocol used for communication with server and client devices. This teaches the uses of set protocol for sending and receiving data, this includes encryption/decryption.)
“analyzing, by the cloud system, the decrypted and/or decompressed preliminary feature vector through an inference engine which implements a model associated with a neural network, thereby yielding a final feature vector.” (Methodology, pp. 2524; “We applied MobileNet-v2 as the skeleton of specific CNNs deployed on edge devices, and ResNet- 152 as the high-accuracy classifier deployed on the Cloud.” This system uses a classification model on the cloud server to further evaluate data. For their experiment they used ResNet-152 which is Deep Residual Learning neural network used for Image Recognition. This model will produce labels for input images.)
Regarding claim 20, Wang teaches, “wherein the computer- readable storage medium is a non-transitory storage medium”. (Performance of Homogeneous Edges and Cloud, pp. 2252; “Considering the satisfactory simulating results under the single edge and cloud setting, we constructed a real-world prototype with three homogeneous edge devices (each with an Intel Core i7-6700 3.4GHz quad-core CPU and 16 GB DDR4 RAM) and a public Cloud (with 8 logical cores of Intel Xeon E5-2682v4 CPU, 64 GB DDR4 RAM, and an NVIDIA Tesla P4 GPU) to evaluate the performance of SurveilEdge.” This article discloses a physical prototype of the system. The edge servers appear to be generic computers and the cloud server is implemented on a generic server. As known in the art a generic computer contains processors coupled to memory devices and where the memory devices store computer readable instructions for the processor so it can perform different functions and methods. A generic server is similar to a computer as it contains processors which are coupled to memory which holds computer readable instructions for the server to execute different functions and methods.)
Conclusion
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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