DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Objections
Claim 1 objected to because of the following informalities: in line 5, the word “a” should be inserted before “digital twin.”
In line 11 of the claim, the word “the” should be deleted or changed to “a” before communication”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim recites the limitation of a physical network layer “where physical objects in a city are located” which is unclear. It is assumed that the physical network layer is the collection of physical elements (such as the sensors) and their connection to one another, which are located within a city, however it is not understood how they would be located in the network layer and what is meant by the limitation. Additionally, it is assumed the “physical objects” are sensors or similar, but there is no indication as to what would not be physical elements since any real-world area would have physical elements within it.
The claim recites the limitation of a sensor that collects and transmits data “on all relevant parameters” in the city, however it is not clear as to what would be considered “relevant” or how a single sensor would be able to collect all of them within a city which would span a large area and seemingly a sensor at one end would not be able to measure parameters at a different end, potentially miles away.
The claim recites the limitation of an IIoT gateway that receives the data from the sensor and provides data transfer and synchronization “between physical objects and digital twin.” It is not clear as to what “physical objects” are being referred to here and if it is the physical objects of the city, or the sensor. Additionally, it is not clear as to what the digital twin is a twin of.
In line 6, the claim refers to “the physical network.” It is unclear if this is the same as the physical network layer previously disclosed or a new, separate network. If the latter, then then the limitation would lack antecedent basis.
It is not clear what is meant by the “brain layer” and what a brain layer would comprise as it does not appear to be a common term in the art. For the purpose of examination, it will be interpreted as processing or control layer in the system.
It is not clear as to what a YA-DA modeling unit is as no definition has been provided and there is no widely accepted definition for the term.
It is not clear as to what is meant by the YANG-based data model “using IIoT and digital twin technologies” and what the digital twin technologies comprise. Additionally, the claim recites the IIoT and digital twin technologies “in which air quality key performance parameters are defined.” It is not clear as to what these key performance parameters are, and how they are “defined” in the IIoT and digital twin technologies.
The claim recites in the final line that the air quality “in the relevant region” is displayed, however, it is not clear as to what is the “relevant” region or how it is determined to be relevant.
Regarding claim 2, the claim recites the limitation that the digital twin layer and brain layer are “placed in the cloud.” It is not clear as to what is meant by “placed in” and whether it is the same as stored in, for example.
It is not clear as to what is meant by the term “scale the system requirements” in the claim. Additionally, the term “the system requirements” lacks antecedent basis and it is not clear which system is being referred to.
Regarding claim 3, the claim recites the limitation of “providing cyber-physical interaction” which is indefinite as it is unclear as to what this step entails and what would be considered the cyber and physical elements interacting. For the purpose of examination, it will be assumed to be the sensing of data by the sensors and transfer to the twin layer.
The claim further recites the limitation of “the physical objects” which are undefined and lack antecedent basis. For the purpose of examination, it will be assumed that the physical objects are the sensors previously disclosed.
The claim recites the step of “creation of a real-time copy of the physical network at the digital twin” which is unclear since a copy of the physical network would be physical in nature. For the purpose of examination, it will be assumed that the copy is a digital copy.
It is not clear as to what the step of “data analysis” would comprise or which element would be performing such an analysis. Additionally, the display of air quality would not be a natural step to follow as no such determination of air quality has been made in the process.
Regarding claim 4, it is not clear as to what step is being performed by the “examining the air quality through air quality monitoring module and the data modeling of the YA-DA modeling unit in the digital twin network located in the cloud” since an air quality monitoring module and digital twin network located in the cloud have previously been disclosed, but rather just a generic monitoring module.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over ul Samee et al. “An Application of IoT and Machine Learning to Air Pollution Monitoring in Smart Cities” (herein after referred to as Samee), Ivanov “Digital Twin of City: Concept Overview” (hereinafter referred to as Ivanov), and Hausermann et al. US 2022/0255805.
Regarding claim 1, Samee discloses an air quality monitoring system comprising a physical network layer (Perception Layer, page 3), a sensor which collects and transmits data on all relevant parameters in the city (“multiple sensors which includes sensors for measurement SO2 (Sulfur dioxide), CO (carbon monoxide) and NO2 (nitrogen dioxide) and cellular network (LTE, 4G etc.), ZigBee (IEEE 802.15.4), Bluetooth (IEEE 802.15.1), Wi-Fi (IEEE 802.11) etc.” page 3), an IIoT gateway1 that receives the data from the sensor and provides data transfer and synchronization between the physical objects and digital (the cluster head transmits the sensor data through a gateway to a cloud server and Application Layer for long-term storage of data, see fig. 3, page 3 and page 4). Samee further discloses the use of AI or a neural network for the data processing (abstract) and comparing measured pollution data to a threshold (page 1) which can then be displayed to warn a client about the air quality in the relevant region (“when the quality of air reached at the defined threshold, the system generates warning for the client,” page 1 and fig. 3). Samee does not explicitly teach the digital twin network, the YA-DA modeling unit or the YANG-based data model in the manner claimed.
Ivanov discloses a system for monitoring a city by creating a digital twin thereof, including a real-time copy which can include air quality data (page 179) and can use an artificial neural network which uses a series of layers to monitor the city. It would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Ivanov with those of Samee in order to provide a digital twin which would store the data generated by the sensors an system of Samee, to allow for a virtual model of the city to be created and monitored via real-life IIoT sensors in order to obtain the most accurate representation of the corresponding real object based on analysis of data from sensor networks and other sources (Ivanov page 179). In combination, the modeling of Ivanov can be done through a number of models including Faster R-CNN, Mask R-CNN, RetinaNet, DensePose and YOLOv3. Ivanov does not explicitly teach the use of the YA-DA modeling unit with a YANG-based model however.
Hausermann teaches a communication system for an IIoT network comprising sensors (paragraph 0012) which can use YANG modeling (paragraph 0092). Since the use of a YANG model for use in artificial intelligence neural networks was well-known at the time of filing, and the present application does not provide any details or support as to why a YANG based data model would be an improvement over the prior art which uses similar modeling to perform the same tasks, it would have been obvious to one of ordinary skill in the art at the time of filing to have used any model such as a YANG model to handle the data transmission in between the sensors and the digital twin automatically.
Regarding claim 2, in combination as above, the digital twin layer and the brain layer are located within the digital twin network and can be stored in the cloud (see Ivanov, page 182) which would allow for scaling of system requirements therein.
Regarding claim 3, Samee discloses an air quality monitoring method comprising transmission of data collected via sensors [“multiple sensors which includes sensors for measurement SO2 (Sulfur dioxide), CO (carbon monoxide) and NO2 (nitrogen dioxide) page 3“] located in a physical network layer (perception Layer, page 3), an IIoT gateway2 via cellular network (LTE, 4G etc.), ZigBee (IEEE 802.15.4), Bluetooth (IEEE 802.15.1), Wi-Fi (IEEE 802.11) etc.” page 3), that receives the data from the sensor and provides data transfer and synchronization via cyber-physical interaction (the cluster head transmits the sensor data through a gateway to a cloud server and Application Layer for long-term storage of data, see fig. 3, page 3 and page 4). Samee further discloses the use of AI or a neural network for the data processing or analysis to (abstract) compare measured pollution data to a threshold (page 1) which can then be displayed to warn a client about the air quality in the relevant region (“when the quality of air reached at the defined threshold, the system generates warning for the client,” page 1 and fig. 3). Samee does not explicitly teach the digital twin network, the YA-DA modeling unit or the YANG-based data model in the manner claimed.
Ivanov discloses a system for monitoring a city by creating a digital twin thereof, including a real-time copy which can include air quality data (page 179) and can use an artificial neural network which uses a series of layers to monitor the city. It would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Ivanov with those of Samee in order to provide a digital twin which would store the data generated by the sensors an system of Samee, to allow for a virtual model of the city to be created and monitored via real-life IIoT sensors in order to obtain the most accurate representation of the corresponding real object based on analysis of data from sensor networks and other sources (Ivanov page 179). In combination, the modeling of Ivanov can be done through a number of models including Faster R-CNN, Mask R-CNN, RetinaNet, DensePose and YOLOv3. Ivanov does not explicitly teach the use of the YA-DA modeling unit with a YANG-based model however.
Hausermann teaches a communication system for an IIoT network comprising sensors (paragraph 0012) which can use YANG modeling (paragraph 0092). Since the use of a YANG model for use in artificial intelligence neural networks was well-known at the time of filing, and the present application does not provide any details or support as to why a YANG based data model would be an improvement over the prior art which uses similar modeling to perform the same tasks, it would have been obvious to one of ordinary skill in the art at the time of filing to have used any model such as a YANG model to handle the data transmission in between the sensors and the digital twin automatically.
Regarding claim 4, in combination, the method of Samee, Ivanov and Hausermann discloses examining the air quality through air quality monitoring module (sensor network) and using the YA-DA modeling, twin network and cloud to process the data (Samee teaches use of a cloud on page 2 for example).
Conclusion
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/MARK A SHABMAN/ Primary Examiner, Art Unit 2855
1 Although described as an IoT system, the addition of sensors to the network satisfies the limitation of an Industrial Internet of Things system.
2 Although described as an IoT system, the addition of sensors to the network satisfies the limitation of an Industrial Internet of Things system.