DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 9, and 17, the following 2-step analysis is applied for analyzing the 35 U.S.C. § 101 subject matter eligibility of the claims.
Step 1: The Statutory Categories
Claim(s) 1, 9, and 17 recite(s) a method, a computer-readable storage medium, and a device, which fall within the statutory categories of invention.
Step 2A: The Judicial Exceptions
Prong 1: does/do the claim(s) recite an exception?
Claim(s) 1, 9, and 17 is/are directed to the abstract idea of collecting, analyzing, and processing data to predict an outcome. Specifically, the claims recite classifying data, integrating data, identifying correlations using mathematical concepts, and predicting an occupancy state based on those correlations.
Prong 2: is the exception integrated into a practical application?
Claim(s) 1, 9, and 17 does/do not integrate the abstract idea into a practical application. The claims merely apply the abstract idea of data analysis and prediction using a generic "networked environment," "processor," and "storage medium." The claims lack any additional elements that improve the functioning of a computer or control a physical technological process (e.g., controlling building automation systems), effectively monopolizing the abstract idea of predicting occupancy via data correlation.
Step 2B: The Inventive Concept
Does/do the claim(s) amount to "significantly more" than the exception?
The additional elements in the claims, such as a generic processor, computer-readable medium, and networked environment, are recited at a high level of generality. They represent well-understood, routine, and conventional computer components performing their basic functions of receiving, processing, and storing data. They do not add an inventive concept to the abstract idea.
Conclusion: Claim(s) 1, 9, and 17 is/are directed to an abstract idea and lacks an inventive concept. Claim(s) 1, 9, and 17 is/are rejected as ineligible subject matter under 35 U.S.C. § 101.
Regarding dependent claims 2-8, 10-16 and 18-20: limitations in these dependent claims have been examined in a similar way as to the above independent claims. It was determined that claims 2-8, 10-16 and 18-20 are ineligible subject matter under 35 U.S.C. § 101:
Claims 2, 10, 18: Ineligible. Merely limits the type of data being gathered (micro-motion data).
Claims 3, 11: Ineligible. Merely limits the type of data being gathered (network connection/disconnection data).
Claims 4, 12, 19: Ineligible. Uses well-understood, routine, and conventional generic hardware (DSP, Wi-Fi access point) to gather data (DSP engine, CSI/CFR data).
Claims 5, 13: Ineligible. Directed to the abstract mathematical concept/data organization of timestamp alignment (aligning timestamps).
Claims 6, 14: Ineligible. Directed to abstract mathematical relationships and logic rules (utilizing rules).
Claims 7, 15: Ineligible. A machine learning model is a mathematical algorithm/concept (machine learning model).
Claims 8, 16, 20: Ineligible. Merely applies a field-of-use restriction to the abstract idea (occupancy state comprises human presence/count).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 3, 5-9, 11, 13-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US20180350219A1) in view of Schnitzler et al (EP3457635A1).
Regarding claims 1, 9 and 17, Correnti teaches a method comprising:
classifying motion data and network device usage data in a networked environment;
(Correnti, "receive sensor data from the one or more sensors, determine usage data that reflects a level of usage of the one or more connected electronic devices", [0005]; "The sensors 220 may include a contact sensor, a motion sensor", [0040]; "The communication link 224, 226, and 228 228 may include a local network, such as, 802.11 “Wi-Fi” wireless Ethernet", [0044]; Schnitzler, "obtaining a sequence of information items representative of a temporal presence of a communication device in the environment", [0005]; "obtaining the sequence of information items comprises monitoring whether the communication device is connected to a wireless network of the environment.", [0006]; "movement sensors", [0036]; Correnti and Schnitzler functionally teach classifying motion data from movement/motion sensors and network device usage data, such as a communication device's connection status on a wireless local area network)
integrating the classified motion data and the network device usage data to create a combined dataset;
(Correnti, "The monitoring server 114 may collect and aggregate data received from the control unit 112 over a period of time ... The aggregated data may include all events sensed by the in-home monitoring system during the period of time", [0022]; integrating the sensor data and connected device data into an aggregated/combined dataset over a period of time)
processing the combined dataset to identify correlations between the motion data and the network device usage data; and
(Correnti, "The monitoring server 114 may analyze the aggregated data ... detects patterns of recurring events within the aggregated data", [0022]; "identifying dependencies using association rule mining.", [0024]; Schnitzler, "detect recurrent patterns from data collected from sensors of the environment, resulting from user activities, and to collect other data representative of a temporal presence or absence of a communication device in the environment by for example, tracking the connections and disconnections of the communication device to a wireless network of the environment. Specific patterns are advantageously identified among all detected patterns by matching the occurrence time of these detected patterns to periods of presence of the communication device using statistics", [0003]; "associating a temporal pattern of the set of temporal patterns with the sequence of information items based on evaluating a temporal matching of the temporal pattern to the sequence of information items", [0005]; Correnti teaches processing the aggregated dataset to find patterns and dependencies. Schnitzler teaches explicitly processing sensor patterns to match and correlate them with network connection/disconnection data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Schnitzler into the system or method of Correnti in order to match the occurrence times of sensor events with periods of wireless network connections to specifically identify correlations between physical motion and device network presence, thereby improving the system's ability to accurately attribute activity to specific human users. The combination of Correnti and Schnitzler also teaches other enhanced capabilities.
The combination of Correnti and Schnitzler further teaches:
using the identified correlations to predict an occupancy state of the environment.
(Correnti, "train, using the sensor data, the usage data, and the occupancy data, a predictive model that is configured to determine a likely occupancy level of the property ... based on applying the current usage data and the current sensor data to the predictive model, determine a likely current occupancy level of the property", [0005]; using the processed patterns to train a predictive model that predicts the occupancy state/level of the monitored environment)
Regarding claims 3 and 11, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein the network device usage data includes data associated with one or more of a connection of a device to a network, a disconnection of the device from the network, or network requests associated with the device.
(Schnitzler, "tracking the connections and disconnections of the communication device to a wireless network of the environment.", [0003]; using connection and disconnection data from the wireless network as the network device usage data.)
Regarding claims 5 and 13, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein integrating the classified motion data and the network device usage data comprises aligning the motion data and network device usage data based on timestamp data and using the alignment to determine relationships between network device usage data and motion data.
(Correnti, "The sensor data may also be timestamped.", [0057]; "The data received from the one or more connected devices may be timestamped.", [0058]; Schnitzler, "obtaining a first sequence of timestamped data items from the first sensor", [0008]; "each information item of the sequence is associated with a time value, the information item indicating whether the communication device is connected to or disconnected from the wireless network of the environment at the time value.", [0007]; Correnti and Schnitzler functionally teach relying on timestamped data for both the sensors and network devices to align and temporally match relationships between them)
Regarding claims 6 and 14, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein processing the combined dataset to identify correlations includes utilizing a plurality of rules, the plurality of rules defining relationships between device usage patterns and detected movements.
(Correnti, "The monitor control unit may be configured to use a rule method to determine when to run an occupancy simulation", [0060]; "identifying dependencies using association rule mining.", [0024]; using rule methods and association rule mining to identify dependencies/relationships in the datasets)
Regarding claims 7 and 15, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein processing the combined dataset to identify correlations includes utilizing a machine learning model, the machine learning model being trained to identify correlations between device usage patterns and detected movements, the training being based on previously recorded and labeled data of device usage and detected movements.
(Correnti, "In some implementations, the predictive model may be trained using machine learning techniques.", [0060]; " train the predictive model that is configured to determine a likely occupancy level of the property by training the predictive model that is configured to determine the likely occupancy level of the property using sensor data, usage data, the occupancy data", [0011]; using a machine learning model trained on historical sensor and usage data to determine patterns and occupancy)
Regarding claims 8, 16 and 20, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination does not expressly disclose but Zandifar teaches the method of claim 1, wherein the occupancy state comprises one or more of a presence of humans or a count of humans in the networked environment.
(Correnti, " train the predictive model that is configured to determine a likely occupancy level of the property by training the predictive model that is configured to determine the likely occupancy level of the property using sensor data, usage data, and occupancy data from other properties that have a same number of residents as the property.", [0011]; Schnitzler, "identifying the user by a detection of a subsequent pattern", [0005]; Correnti and Schnitzler functionally teach predicting the presence of specific humans and evaluating the number/count of residents in the environment)
Claim(s) 2, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US20180350219A1) in view of Schnitzler et al (EP3457635A1) and further in view of Napholz et al (US20240013642A1).
Regarding claims 2, 10 and 18, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination does not expressly disclose but Napholz teaches the method of claim 1, wherein the motion data includes data related to micro-motions, the micro-motions indicating movements within the environment that do not significantly change a physical position of a device.
(Napholz, "detection of heartbeats and/or breathing of a living being by means of changes of a signal strength or a wireless radio signal, in particular of a Wi-Fi radio signal ... even the rising and falling of a chest of a breathing living being is measurable by means of recognizing variations of a radio signal intensity of the radio signal received by the receiver.", [0004]; the motion data includes detecting micro-motions, such as the rising and falling of a chest (breathing) or heartbeats, which signify movements within the environment that do not significantly change the physical position of the device)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Napholz into the modified system or method of Correnti and Schnitzler in order to enhance the sensitivity and accuracy of the occupancy prediction model by allowing the system to detect living subjects who are stationary (e.g., sleeping or sitting still) through the recognition of micro-motions. The combination of Correnti, Schnitzler and Napholz also teaches other enhanced capabilities.
Claim(s) 4, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US20180350219A1) in view of Schnitzler et al (EP3457635A1) and further in view of Zandifar et al (US20210333351A1).
Regarding claims 4, 12 and 19, the combination of Correnti and Schnitzler teaches its/their respective base claim(s).
The combination does not expressly disclose but Zandifar teaches the method of claim 1, wherein classifying motion data comprises detecting motion events using a Digital Signal Processing (DSP) engine based on one or more of Channel State Information (CSI) or Channel Frequency Response (CFR) data received from a Wi-Fi access point.
(Zandifar, "digital signal processing (DSP)", [0061]; "The solution studies the changes in WiFi signals, represented by channel state information (CSI) through time, due to human body presence and motion in the observed environment", [0011]; "Several signals are broadcasted or emitted in type of frames by the stations (STA) and Access Points (APs) in WiFi networks", [0010]; detecting motion events using a Digital Signal Processing (DSP) engine based on Channel State Information (CSI) data extracted from signals transmitted by Wi-Fi Access Points (APs))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Zandifar into the modified system or method of Correnti and Schnitzler in order to leverage pervasive, existing Wi-Fi infrastructure as a passive, device-free motion sensor, thereby improving the granularity and cost-effectiveness of the motion classification system without requiring dedicated physical motion hardware. The combination of Correnti, Schnitzler and Zandifar also teaches other enhanced capabilities.
Conclusion
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 6/27/2026
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