DETAILED ACTIONS
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
Acknowledgment is made of applicant’s claim this application being in benefit of provisional Application No. 63/542,341 filed on October 4th, 2023.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 06/11/2025 was reviewed and the listed references were noted.
Drawings
The 18-page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-39 are pending.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-39 are rejected under 35 U.S.C. 103 as being unpatentable over Dunne et al., (US 2020/0272899 A1), hereinafter referred to as Dunne, in view of Cella et al., (US 12,578,695 B2), hereinafter referred to as Cella.
Claim 1
Dunne discloses a method (Dunne, Fig. 3, Fig. 13B) performed by at least one processor in a processing system (Dunne, Fig. 2A) in an edge device for managing the flow of data and events (Dunne, Fig. 3, Fig. 13B), the method comprising:
receiving an ensemble of signals that represents aggregated sensor signals (Dunne, [0006], “collecting the dataset from sensors of the edge device”) from a plurality of input sensors (Dunne, [0072], “The sensor(s) 212 may capture data about the internal or external environment of the device, or about a remote environment that is targeted by the sensor(s) 212. In some embodiments, the multiple sensors 212 may capture a variety of data types, or orthogonal data. The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter). This is the sensor fusion paradigm.”);
processing the ensemble of signals to generate pre-processed data (Dunne, [0074], “the processor may label or tag the data, such as by performing or implementing a human-in-the-loop technique. For the example task of object detection in images, labelling in block 304 may take the form of identifying and/or highlighting positive examples of the object to be detected within the images in the dataset”, the collected data is being pre-processed by labelling or tagging them before being applied to the neural network as shown in Fig. 3);
applying the pre-processed data to one or more neural networks to generate output data (Dunne, [0006], “applying the collected dataset as inputs to the received neural network to generate activations and the overall inference result”) identifying one or more events of interest (Dunne, [0143], “an edge device on a satellite that detects ships may have an ensemble in which a first neural network (or classifier, etc.) is an object detector for boats, and a second neural network in the ensemble is a wake detector. Together these neural networks (i.e., the object detector and the wake detector) may perform better than either neural network alone. The weighting in the aggregation function for the wake detector may be less for smaller boats or stationary ships.”);
combining outputs from the one or more neural networks to generate a unified detection result (Dunne, [0042], “The term “ensemble neural network” may be used herein to refer to a neural network that includes one or more sub-networks. The overall inference result from an ensemble neural network may be a weighted combination of the inference result of the individual neural networks in the ensemble. The processing nodes (e.g., neuron models, etc.) in an ensemble neural network are typically smaller than their corresponding single network equivalents that perform the same or similar prediction functions. As such, ensemble neural networks may be more suitable for receiving updates over a low-bandwidth channel than their non-ensemble counterparts.”, [0104], “the device processor may receive the overall inference result of the ensemble neural network and the individual ensemble outputs (e.g., the overall inference result of each neural network in the ensemble neural network) from the edge device”);
predicting a future event based on the unified detection result and historical data (Dunne, [0082], “This enables the neural network to be customized to the hardware of the edge device 110 early in the life cycle of the edge device 110, which may result in valuable time savings. In particular, these embodiments may overcome the technical challenge of not being able to gather training data from the device before launch. For example, a neural network that is configured to perform predictive analytics in accordance with the embodiments (e.g., crop failure prediction based on hyperspectral sensor data from satellites, classification of land as fire risk due to its spectral signature as seen in earth observing sensors, etc.) could be effective immediately after deployment of the edge device 110 and thereby provide instant predictions without first requiring a data collection and retraining cycle.”).
Dunne does not explicitly disclose generating one or more labels corresponding to the identified events of interest, determining a priority classification of the unified detection result or predicted future event, determining a priority classification of the unified detection result or predicted future event, triggering a low-latency alert in response to determining that the priority classification exceeds the high-priority event threshold, and storing non-high-priority data and predicted events in association with the generated labels in an onboard non-volatile storage of the edge device in response to determining that the priority classification does not exceed the high-priority event threshold.
However, Cella teaches generating one or more labels corresponding to the identified events of interest (Cella, [Col. 88, lines 1-14], “Once optimized, the objective function may provide the machine learning model 3000 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 3000 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model AILD102T, a user and/or an information source to label new data points with desired outputs. Statistical classification may include identifying, by the machine learning model 3000, to which a set of subcategories, i.e., subpopulations, a new observation belongs based on a training set of data containing observations having known categories”), determining a priority classification of the unified detection result (Cella, [Col. 41, lines 25-35], “The hybrid adaptive intelligence systems 614 may facilitate applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence system 1160 can operate independently, cooperatively, and optionally coordinate operation to provide coordinated intelligence for operation of the value chain that produces at least one of the goods in the category of goods 3010.”) or predicted future event (Cella, Fig. 15, the output of the neural networks are the input of the supply chain management application, [Col. 3, lines 43-52], “The method may include selecting at least one of the predictive model data streams based on a corresponding priority. The method may include parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model stream. The method may include predicting, by the first device, future data values of the data source using the parameterized predictive model. In embodiments, the selected at least one predictive model data stream is associated with a high priority.”), determining whether the priority classification exceeds a high-priority event threshold (Cella, “wherein the selected at least one predictive model data stream is associated with a priority that is above a threshold”, [Col. 3, lines 43-52], “The method may include selecting at least one of the predictive model data streams based on a corresponding priority. The method may include parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model stream. The method may include predicting, by the first device, future data values of the data source using the parameterized predictive model. In embodiments, the selected at least one predictive model data stream is associated with a high priority.”), triggering a low-latency alert in response to determining that the priority classification exceeds the high-priority event threshold (Cella, [Col. 171, lines 21-25, “in some embodiments, configuration of a digital twin may include providing inputs as to the requirements of the digital twin for low-latency, high quality-of-service, high accuracy, high granularity, high reliability, or the like, based on, for example, the priority of the mission served by the data type”, [Col. 350, lines 61-67], “, an alerts and notifications system 10640 provides alerts, notifications or reports about one or more events to a user or customer of the network 10130. For example, the alerts and notifications system 10640 may receive data related to certain production parameters or errors based on monitoring of the production workflow, based on which the alerts and notifications may be generated. Such alerts, notifications, or reports may then be transmitted to a computing device (e.g., a computer, tablet computer, smart phone, telephone, mobile phone, PDA, TV, gaming console and the like) of a user or customer via email, text message, instant message, phone call, and/or other communication (e.g., using the Internet or other data or messaging network)”), and storing non-high-priority data and predicted events in association with the generated labels in an onboard non-volatile storage of the edge device (Cella, [Col. 632, lines 49-50], “the edge network system 16100 has robotics as edge devices”, [Col. 631, lines 57-58], “the edge network system 16100 has an edge storage protocol.”) in response to determining that the priority classification does not exceed the high-priority event threshold (Cella, [Col. 647, lines 1- 9], “the filtering 18038 may involve ignoring, deleting, or otherwise removing data that is not important (e.g., does not match an area focus 18036, is low priority, etc.). In embodiments, data may be initially suppressed (e.g., reduced or stored for later), but conditions may further change, causing the data to be filtered (e.g., deleted, ignored). Thus, intake data management system 18028 may allow for a progressive downgrade of data by first suppressing and later filtering the data depending on conditions.”).
Dunne and Cella are both considered to be analogous to the claimed invention because they are in the same field of edge computing using ensemble neural networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Dunne to incorporate the teachings of Cella of generating one or more labels corresponding to the identified events of interest, determining a priority classification of the unified detection result or predicted future event, determining a priority classification of the unified detection result or predicted future event, triggering a low-latency alert in response to determining that the priority classification exceeds the high-priority event threshold, and storing non-high-priority data and predicted events in association with the generated labels in an onboard non-volatile storage of the edge device in response to determining that the priority classification does not exceed the high-priority event threshold. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to improve the efficiency of route selection (Cella, [Col. 12, line 52]).
Claim 2
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein receiving the ensemble of signals that represents aggregated sensor signals (Dunne, [0006], “collecting the dataset from sensors of the edge device”) from a plurality of input sensors (Dunne, [0072], “The sensor(s) 212 may capture data about the internal or external environment of the device, or about a remote environment that is targeted by the sensor(s) 212. In some embodiments, the multiple sensors 212 may capture a variety of data types, or orthogonal data. The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter). This is the sensor fusion paradigm.”) comprising receiving a signal that represents aggregated sensor signals (Dunne, [0006], “collecting the dataset from sensors of the edge device”) from at least one or more of:
a camera (Dunne, [0072], “The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter”);
an infrared sensor (Dunne, [0072], “The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter”);
a pressure sensor;
a temperature sensor;
a microphone; or
a sensor configured to detect ultraviolet or infrared light (Dunne, [0072], “The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter”).
Claim 3
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein receiving the ensemble of signals that represents aggregated sensor signals (Dunne, [0006], “collecting the dataset from sensors of the edge device”) from a plurality of input sensors (Dunne, [0072], “The sensor(s) 212 may capture data about the internal or external environment of the device, or about a remote environment that is targeted by the sensor(s) 212. In some embodiments, the multiple sensors 212 may capture a variety of data types, or orthogonal data. The combination of the data from various sensor types may be processed by the edge device 110 (or a processor 204 in the edge device 110, AI processor, etc.) in order to determine a more valuable result (e.g., a heads-up-display used by a fire-fighter may combine multiple types of captured data from multiple sensing paradigms such as visible spectrum, short-wave infrared, thermal infrared, night-vision, ultrasonic sounds to create a more holistic combined result relating to the immediate environment of the firefighter). This is the sensor fusion paradigm.”) comprising receiving a signal that synchronizes data collection from the plurality of input sensors and aligns temporal and spatial characteristics of the sensor signals (Cella, [Col. 29, lines 20-31], “the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652. For example, one or more video streams or other sensor data collected of or with respect to a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like.”).
Claim 4
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), further comprising processing the ensemble of signals in real-time to handle high-throughput data streams from the plurality of input sensors (Cella, [Col. 132, lines 35-45], “Machine twin 1770 receives data from data storage systems 624 on a real-time or near real-time basis. The data storage systems 624 may store different types of data in different datastores. For example, machine datastore 5202 may store data related to machine identification and attributes, machine state and event data, data from maintenance records, historical operating data, notes from machine operator, etc. Sensor datastore 5204 may store sensor data from operation such as temperature, pressure, and vibration that may be stored as signal or time series data.”).
Claim 5
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein applying the pre-processed data to the one or more neural networks to generate output data (Dunne, [0006], “applying the collected dataset as inputs to the received neural network to generate activations and the overall inference result”) identifying one or more events of interest (Dunne, [0143], “an edge device on a satellite that detects ships may have an ensemble in which a first neural network (or classifier, etc.) is an object detector for boats, and a second neural network in the ensemble is a wake detector. Together these neural networks (i.e., the object detector and the wake detector) may perform better than either neural network alone. The weighting in the aggregation function for the wake detector may be less for smaller boats or stationary ships.”) comprises applying the pre-processed data to two or more of: a convolutional neural network (CNN); a recurrent neural network (RNN); and a deep neural network (DNN) (Dunne, [0034], “Examples of AI models include network models, neural network models, inference models, neuron models, classifiers, random forest models, spiking neural network (SNN) models, convolutional neural network (CNN) models, recurrent neural network (RNN) models, deep neural network (DNN) models, generative network models, and genetic algorithm models. In some embodiments, an AI model may include an architectural definition (e.g., the neural network architecture, etc.) and one or more weights (e.g., neural network weights, etc.).”).
Claim 6
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein generating one or more labels corresponding to the identified events of interest comprises generating the one or more labels based on inference results produced by the one or more neural networks (Cella, [Col. 88, lines 1-14], “Once optimized, the objective function may provide the machine learning model 3000 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 3000 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model AILD102T, a user and/or an information source to label new data points with desired outputs. Statistical classification may include identifying, by the machine learning model 3000, to which a set of subcategories, i.e., subpopulations, a new observation belongs based on a training set of data containing observations having known categories”).
Claim 7
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein combining outputs from the one or more neural networks to generate the unified detection result comprises combining outputs from the one or more neural networks using weighted voting or averaging techniques (Dunne, [0045], “A classifier may include multiple decision nodes and a feature vector may include multiple features. As such, applying a feature vector to a classifier may generate a plurality of answers to a plurality of different test conditions. Each of these answers may be represented by a numerical value. The device processor may multiply each of these numerical values with their respective weight value to generate a plurality of weighted answers. The device processor may then compute or determine a weighted average based on the weighted answers, compare the computed weighted average to one or more threshold values, and perform a responsive action (e.g., classify/label the collected data, etc.) based on the results of the comparison. For example, if the computed weighted average is “0.79” and the threshold value for “scooter-specific micromovement (shake)” is “0.75,” the device processor could determine that the collected dataset is suitable (or that it is not suitable) for use in training a neural network for a e-scooter edge device.).
Claim 8
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein predicting the future event based on the unified detection result and the historical data comprises generating a prediction based on detected patterns, trends in historical data, and the unified detection result (Dunne, [0082], “This enables the neural network to be customized to the hardware of the edge device 110 early in the life cycle of the edge device 110, which may result in valuable time savings. In particular, these embodiments may overcome the technical challenge of not being able to gather training data from the device before launch. For example, a neural network that is configured to perform predictive analytics in accordance with the embodiments (e.g., crop failure prediction based on hyperspectral sensor data from satellites, classification of land as fire risk due to its spectral signature as seen in earth observing sensors, etc.) could be effective immediately after deployment of the edge device 110 and thereby provide instant predictions without first requiring a data collection and retraining cycle.”).
Claim 9
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein determining the priority classification of the unified detection result or the predicted future event (Cella, “wherein the selected at least one predictive model data stream is associated with a priority that is above a threshold”, [Col. 3, lines 43-52], “The method may include selecting at least one of the predictive model data streams based on a corresponding priority. The method may include parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model stream. The method may include predicting, by the first device, future data values of the data source using the parameterized predictive model. In embodiments, the selected at least one predictive model data stream is associated with a high priority.”) comprises determining the priority classification of the unified detection result or the predicted future event based on inputs from one or more of: an onboard timer; an onboard computer; or an onboard fault detector (Cella, [Col. 132, lines 54-67], “physical models 5320 may be selected if the intended use of machine twin 1770 is to simulate what-if scenarios and predict how the machine will behave under such scenarios. Fault Detection and Diagnostics Models 5322 may be selected to determine the current health of the machine and any fault conditions. A simple fault detection model may use one or more condition indicators to distinguish between regular and faulty behaviors and may have a threshold value for the condition indicator that is indicative of a fault condition when exceeded. A more complex model may train a classifier to compare the value of one or more condition indicators to values associated with fault states and returns the probability of presence of one or more fault states.”).
Claim 10
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), further comprising applying machine learning operations (MLOps) to update the one or more neural networks (Dunne, [0003], “using the received neural network information to update all or a part of the trained neural network, generating updated neural network information based on the updated neural network, and sending the updated neural network information to the edge device”).
Claim 11
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein the low-latency alert includes at least one or more of: event type information; timestamp information; geographic information; or event summary information (Cella, [Col. 171, lines 21-25, “in some embodiments, configuration of a digital twin may include providing inputs as to the requirements of the digital twin for low-latency, high quality-of-service, high accuracy, high granularity, high reliability, or the like, based on, for example, the priority of the mission served by the data type”, [Col. 350, lines 61-67], “, an alerts and notifications system 10640 provides alerts, notifications or reports about one or more events to a user or customer of the network 10130. For example, the alerts and notifications system 10640 may receive data related to certain production parameters or errors based on monitoring of the production workflow, based on which the alerts and notifications may be generated. Such alerts, notifications, or reports may then be transmitted to a computing device (e.g., a computer, tablet computer, smart phone, telephone, mobile phone, PDA, TV, gaming console and the like) of a user or customer via email, text message, instant message, phone call, and/or other communication (e.g., using the Internet or other data or messaging network)”).
Claim 12
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), further comprising storing, in the onboard non-volatile storage, event statistics that include an alert generation frequency or generated alert type information (Cella, Col. 234, lines 27-31], “ the client application 8052 hosting the GC digital twin 8314 may track the user's actions relating to various events, notifications, alerts, or the like and may report the tracked events using the expert agent system 8008, as described herein”).
Claim 13
The combination of Dunne in view of Cella disclose the method of claim 1 (Dunne, Fig. 3, Fig. 13B), wherein determining the priority classification of the unified detection result or the predicted future event comprises dynamically determining the priority classification based on available bandwidth and storage capacity (Cella, [Col. 296, lines 31-49], “the data analysis circuit 9214 may analyze whether the data for a particular traffic flow is encrypted, compressed, has a particular format, is associated with a particular priority level (e.g., a priority level associated with a contracted data rate), or the like. The data analysis circuit 9214 may add such information to one or more corresponding network digital twins, each of which may be specific to a particular communication network carrying the data, one or more devices on the network, one or more data configurations for the network, one or more rate schedules for the network, etc. In embodiments, the data analysis circuit 9214 may analyze application-specific data that may indicate a particular application and/or one or more attributes (e.g., whether the data is payment data, customer data, whether the data is associated with a particular project, etc.). In these embodiments, the data analysis circuit 9214 may add such information to a network digital twin. Additionally or alternatively, the data analysis circuit 9214 may analyze received messages to detect information about network device”).
Claims 14-26 are rejected for similar reasons as those described in claims 1-13. The additional elements in Claims 14-26 (the combination of Dunne in view of Cella) discloses includes: an edge device (Dunne, [0006], “the methods may include the edge device receiving the trained neural network”), comprising: a processing system comprising at least one processor (Dunne, [0031], “other similar computing systems, devices or objects that include a memory, a sensor, a processor, and communications circuitry for communicating with computing devices at one or more centralized sites.”). The proposed combination as well as the motivation for combining the Dunne and Cella references presented in the rejection of Claim 1, apply to Claims 14-26 and are incorporated herein by reference. Thus, the method recited in Claims 14-26 is met by Dunne and Cella.
Claims 27-39 are rejected for similar reasons as those described in claims 1-13. The additional elements in Claims 27-39 (the combination of Dunne in view of Cella) discloses includes: a non-transitory processor-readable storage medium having stored thereon processor-executable instructions (Dunne, [0248], “the functions may be stored as one or more instructions or code on a non-transitory computer-readable medium or non-transitory processor-readable medium”) to cause at least one processor in a processing system in an edge device (Dunne, [0031], “other similar computing systems, devices or objects that include a memory, a sensor, a processor, and communications circuitry for communicating with computing devices at one or more centralized sites.”) to perform operations for managing the flow of data and events (Dunne, Fig. 3, Fig. 13B). The proposed combination as well as the motivation for combining the Dunne and Cella references presented in the rejection of Claim 1, apply to Claims 27-39 and are incorporated herein by reference. Thus, the medium recited in Claims 27-39 is met by Dunne and Cella.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENISE G ALFONSO whose telephone number is (571)272-1360. The examiner can normally be reached Monday - Friday 7:30 - 5:30.
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/DENISE G ALFONSO/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
