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 .
Claim Objections
The previous claim objections of Claims 3, 4, 14, 15, 19 and 20 has been addressed and is hereby withdrawn.
Claim Rejections - 35 USC § 101
The previous claim rejections of Claims1-20 has been addressed and is hereby withdrawn.
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.
Claims 1-6, and 8-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 10497250 B1, Hayward et al. (hereinafter Hayward) in view of US 9874489 B1, Olivier et al. (hereinafter Olivier).
Regarding Claim 1, 12, and 17, Hayward discloses a computer-implemented method (Fig. 1 (100) real property monitoring system) of mitigating or preventing water damage within a structure (Fig 1, [Col. 13 Lines 45-47] the intelligent monitoring system controller (106) to adjust the flow of water in and around the building (130) [Col. 13 Lines 50-52] (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)) the method comprising:
generating, by one or more sensors (Fig. 1 (112) sensors), sensor data (Fig. 1 (146) database that receives data from sensors) indicative of one or more conditions associated with the structure (Fig. 1 (130) building), the one or more conditions including at least one characteristic of air ([Col. 19 Lines 53-57] sensors 112 detect or sense various dynamic characteristics and/or conditions of the building 130, examples of dynamic characteristics (e.g., temperature, flow, density, etc.)), construction materials ([Col. 8 Lines 6-10] inputs to a machine learning model may be harvested from historical claims may include type of home, materials used in building the home, etc.), or water within the structure ([Col. 14 Lines 7-12] water sensors (112) send input into model for water flow, presence of water, leaks within the roof or building);
receiving, by one or more processors (Fig. 1 (162) processor), the sensor (Fig. 1 (112) sensors) data and data (Fig. 1 (146) database) indicative of one or more other factors associated with an interior of the structure (Fig. 1, [Col. 12 Lines 9-12] The intelligent monitoring system controller 106 may use the monitoring application 222 to receive and process data that is generated by the intelligent building products 110, 112, 114, 116, 118);
detecting or predicting, by the one or more processors (Fig. 1 (162) processor), water damage to the structure (Fig. 1 (130) building) by jointly analyzing the sensor (Fig. 1 (112) sensors) data and the data (Fig. 1 (146) database) indicative of the one or more other factors ([Col. 4 Lines 15-18] the analytics model, thereby discovering or predicting at least one of the one or more conditions associated with the building, one of which may be particular damage to the building that is associated with the event), wherein detecting or predicting the water damage includes:
generating a baseline model for the structure (Hayward, [Col 3 Line 26-32] The system may apply the trained-analytics model to at least one of the dynamic characteristic data corresponding to the building or additional characteristic data corresponding to the building to thereby discover or predict at least one of the one or more conditions associated with the building) based at least in part upon first sensor data associated with a first time or time range (Hayward, [Col 19 Line 35-40] data generated by the intelligent building products 110-118 may be time-series data where each data point includes a value and a corresponding indication of time at which the value was collected, observed, or generated by the respective intelligent building product, [Col 8 Line 6-7] Other inputs to a machine learning/training model may be harvested from historical claims, [Col 4 Line 48-52] a computer-implemented method of determining damage to property may include inputting, e.g., via one or more processors, historical claim data into a machine learning algorithm to train the algorithm to identify one or more insured assets).
causing, by the one or more processors, (i) a water supply to a location associated with the detected or predicted water damage to be cut off or (ii) an appliance associated with the detected or predicted water damage to be shut down (Hayward, [Col 13 Line 42-52] the control device 110 may be an automated circuit breaker that can be adjusted according to input from the intelligent monitoring system controller 106 to automatically and/or remotely apply or remove power to the entire building 130. The control device 110 may be an automated water valve that can be adjusted according to inputs from the intelligent monitoring system controller 106 to adjust the flow of water in and around the building 130 (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)).
Hayward does not disclose the baseline model including a set of parameters and expected ranges of values for the set of parameters; and
Detecting or predicting the water damage using the baseline model and second sensor data associated with a second time or time range occurring after the first time or time range, at least in part by detecting anomalous values, for the set of parameters, relative to the expected range of values.
However, Olivier teaches the baseline model including a set of parameters (Olivier, [Col 5 Line 38-39] Historical data (e.g., 13 months) can be used to generate one or more thresholds) and expected ranges of values for the set of parameters (Olivier, [Col 12 Line 25-31] Anomaly detection. The mean for this cycle is compared to the nominal mean as determined by benchmarking, as well as a threshold selected during calibration. The standard deviation of the cycle is also compared to thresholds. If either value is out of the expected range, an anomaly has been detected); and
Detecting or predicting the water damage (Olivier, [Col. 5 Line 53-54] More importantly, detection of the leak prevented physical damage to the property) using the baseline model and second sensor data associated with a second time or time range occurring after the first time or time range (Olivier. [Col 5 Line 58-68] It has been found that the error signal tends to creep up (slowly increase) before a significant leak occurs. A warning threshold can be set at a fraction (a third, or other fraction, for example) of the alert threshold to bring attention to a developing leak. In one instance, a leak was predicted three days before it actually happened using this technique. For example, later in September, the statistical indicator (lower sigmoid) increased again and reached the warning threshold (FIG. 5). A warning email was sent predicting a leak. A few days later, the statistical indicator reached the alert threshold (FIG. 5) indicating a leak. An alarm text message was sent to the user of the irrigation system), at least in part by detecting anomalous values, for the set of parameters, relative to the expected range of values (Olivier, [Col 11 Line 30-37] The daily or hourly analysis looks at the electrical usage from the new and previous internals and looks for problems and also estimates water used. There are several different algorithms used, depending on the pump type (large well pump, VFD, pressurized, canal pump). Each of these algorithms has two parts: detect a problem, then classify what kind of problem was found. The calibration done during the benchmark determines the “normal” parameters).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art would combine Hayward and Olivier’s teaching because both references are directed to detecting or predicting water damage in building structures using sensor data analytics. Hayward discloses detecting or predicting water damage based on sensor data over time but does not expressly disclose establishing expected parameter ranges for anomaly detection. Olivier teaches generating a model that includes a set of parameters and corresponding expected range of values based on historical data, and detecting anomalies when values fall outside those expected ranges. One of ordinary skill in the art would have been motivated to incorporate Olivier’s modeling and anomaly detection techniques into Hayward in order to improve the accuracy and reliability of detecting or predicting water .
Regarding Claim 2, 13, and 18, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein the sensor (Fig. 1 (112) sensors) data (Fig. 1 (146) database) is indicative of at least one characteristic of air within the structure (Fig. 1 (130) building), the at least one characteristic of air including one or more of (Fig 1, [Col. 19 Lines 53-57] sensors (112) detect or sense various dynamic characteristics and/or conditions of the building (130), examples of dynamic characteristics (e.g., temperature, flow, density, etc.)):
air temperature (Fig. 1, [Col. 19 Lines 41-45] control devices (110) generate data within the building of state changes like on/off, opened/closed, degree or amount (e.g., of temperature, amount of light, airflow));
air humidity (Fig. 1, [Col. 21 Lines 65 to Col. 22 Lines 4] sensors (112) generate signals associated within the building (130) such as movement, motion, temperature, moisture, humidity, presence of smoke and/or gas, on/off (e.g., of various devices, appliances, etc.), open/closed (e.g., of various windows, doors, etc.));
air movement; or airborne particle count (Fig. 1, [Col. 19 Lines 41-45] control devices (110) generate data within the building of state changes like on/off, opened/closed, degree or amount (e.g., of temperature, amount of light, airflow)).
Regarding Claim 3, 14, and 19, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein the sensor (Fig. 1 (112) sensors) data (Fig. 1 (146) database) is indicative of at least one characteristic of construction materials ([Col. 8 Lines 6-11] inputs to a machine learning model may be harvested from historical claims may include type of home, materials used in building the home, etc.) within the structure (Fig. 1 (130) building), the at least one characteristic of construction materials including one or more of:
moisture of the construction materials (Fig. 1, [Col. 21 Lines 65-col. 22 line 10] plurality of sensors (112) may generate signals indicative of sensed, respective dynamic, physical characteristics associated with the building, such as movement, motion, temperature, moisture, humidity, presence of smoke and/or gas); or
impedance of the construction materials (Hayward does not explicitly disclose “impedance” as a characteristic of the construction materials. However, Hayward expressly teaches moisture of the construction materials (Fig. 1, [Col. 21 Lines 65-col. 22 line 10]), which meets the claimed limitations. Therefore, the limitation is anticipated by Hayward).
Regarding Claim 4, 15, and 20, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein the sensor (Fig. 1 (112) sensors) data (Fig. 1 (146) database) is indicative of at least one characteristic of water within the structure (Fig. 1 (130) building), the at least one characteristic of water ([Col. 14 Lines 7-8] the sensor (112) may be a water sensor which may send input to the intelligent monitoring system controller) including one or more of:
a presence of pooled water within the structure ([Col. 14 Lines 9-12] indicating, for example, the flow rate of a faucet, the presence of water in the basement, a roof leak in the attic, whether the sprinkler system is turned on, etc.); or
a flow of water within the structure (Fig 1, [Col. 13 Lines 45-49] the intelligent monitoring system controller (106) to adjust the flow of water in and around the building (130) [Col. 13 Lines 50-52] (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)) .
Regarding Claim 5 and 16, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 4, wherein the sensor (Fig. 1 (112) sensors) data (Fig. 1 (146) database) is indicative of an amount of time that water is pooled or flowing ([Col. 23 Lines 42-43] third-party input data may be descriptive and/or indicative of the impacting event and/or of various characteristics of the impacting event, and optionally respective measurements, amounts, or indications of magnitudes of various portions of the event. Respective timestamps may capture the dates/times at which the various third-party input data points were collected or observed. [Col. 23 Lines 65- Col. 24 Line 1] third-party input may describe a tornado, and thus the resulting high wind speeds detected by the sensors. In another example, sensors at the building may detect rising waters in the basement).
Regarding Claim 6, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein detecting or predicting water damage (Fig 1, [Col. 13 Lines 45-47] the intelligent monitoring system controller (106) to adjust the flow of water in and around the building (130) [Col. 13 Lines 50-52] (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)) to the structure (Fig. 1 (130) building).
Hayward does not disclose using a machine learning model to analyze the one or more conditions and the one or more other factors.
However, Olivier teaches using a machine learning model (Olivier, [Col 6 Line 2-10] The method described above can be enhanced to not only detect when a leak occurs but also where it is located. This can save many man-hours looking for the leak on a large field. Various leaks create different signatures in the electrical load (FIG. 6 shows electrical signatures before a catastrophic leak). The signatures can be recognized by machine learning algorithms provided with data sets that assign signatures to various parcels on the property) to analyze the one or more conditions and the one or more other factors (Olivier, [Col 2 Line 61-63] machine learning programs can further identify the type of anomaly by recognition of electrical signatures of a water pump).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Hayward’s system to incorporate Olivier’s machine learning model to analyze the data. Hayward already teaches detecting and predicting water damage from sensor data, while Olivier teaches a machine learning model trained on image data to detect anomalies, structural changes, and environmental changes. Combining these teachings would have improved Hayward’s monitoring system, evolving a more reliable detection and prediction of water damage under diverse conditions.
Regarding Claim 8, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein detecting or predicting the water damage (Fig 1, [Col. 13 Lines 45-47] the intelligent monitoring system controller (106) to adjust the flow of water in and around the building (130) [Col. 13 Lines 50-52] (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)) includes determining, based at least upon the sensor (Fig. 1 (112) sensors) data (Fig. 1 (146) database), that there is insufficient air movement ([Col. 19 Lines 41-52] Control devices may generate data indicative of changes of state of various devices at the building , such as on/off, opened/closed, degree or amount (e.g., of temperature for thermostat, of amount of light for a light dimmer, of airflow for a fan, etc.), and/or… sensors may generate data indicative of a sensed characteristic or condition such as, for example, motion, heat, light, water, smoke, etc.) in (i) a location where water intrusion is detected and/or (ii) a location at which pooled water is detected ([Col. 22 Lines 54-59] for example, the foundation of the building may be subjected to rising ground waters (a detectable dynamic condition associated with the building), and the foundation itself may suffer structural damage due to the exposure to rising ground waters (another detectable dynamic condition associated with the building)).
Regarding Claim 9 and 21, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, further comprising causing, by the one or more processors, an indication of the detected or predicted water damage (Fig 1, [Col. 13 Lines 45-47] the intelligent monitoring system controller (106) to adjust the flow of water in and around the building (130) [Col. 13 Lines 50-52] (e.g., turning on or turning off sprinklers, turning on a pump to prevent the basement from flooding, etc.)) to be presented to the user at least in part by:
generating an alert (Fig. 2, [Col. 12 Lines 34-38] system may use the graphical user interface (220) to display alerts from the data received from the building sensors); and
transmitting the alert to a device of the user ([Col. 25 Lines 57-59] the method may transmit an indication of the discovered condition).
Regarding Claim 10, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 1, wherein the data (Fig. 1 (146) database) indicative of one or more other factors associated with the interior of the structure (Fig. 1 (130) building) includes data (Fig. 1 (146) database) indicative of one or more known characteristics of the structure ( [Col. 8 Lines 6-10] inputs to a machine learning/training model may be harvested from historical claims may, and may include make, model, year of appliances in the house (e.g., water heater, toilet, dishwasher, etc.), type of home, materials used in building the home).
Regarding Claim 11, Hayward in view of Olivier discloses the computer-implemented method (Fig. 1 (100) real property monitoring system) of claim 10, wherein the data (Fig. 1 (146) database) indicative of the one or more known characteristics of the structure (Fig. 1 (130) building) includes data indicative of one or more of:
a presence or absence of water outlets and/or water inlets in a particular area ([Col. 14 Lines 7-12] water sensors (112) send input into model for water flow, presence of water, leaks within the roof or building); of the structure (Fig. 1 (130) building);
a configuration of walls and/or appliances in a particular area of the structure ([Col. 3 Lines 46-49] sensors may be fixedly disposed at respective locations at the building, and at least some of the sensors may be fixedly attached to the building); or
an absorption rate of walls and/or floors in a particular area of the structure (Fig. 1, [Col. 21 Lines 65 to Col. 22 Lines 4] sensors (112) generate signals associated within the building (130) such as movement, motion, temperature, moisture, humidity, presence of smoke and/or gas, on/off (e.g., of various devices, appliances, etc.), open/closed (e.g., of various windows, doors, etc.))
Response to Arguments
35 USC§ 101
Applicant’s arguments with respect to claims 1-20 of the 35 USC§ 101 rejection have been considered and the amendments with respect to claims 1, 12, and 17 addresses the rejection and are hereby withdrawn. The amendments to the following claims add additional limitations directed to detecting or predicting water damage within a structure using sensor data, a baseline model, and causing a physical action in response to the detected or predicted water damage. These limitations integrate the recited abstract idea into a practical application and therefore overcome the 101 rejection.
35 USC§ 103
Applicant’s arguments with respect to claims 1-5, and 8-20 of the 35 USC§ 102 rejection and with respect to claims 6-7 of the 35 USC§ 103 rejection have been fully considered but are unpersuasive and/or moot in view of the new grounds of rejection as set forth above over Hayward in view of Olivier.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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/IBRAHIM NAGI SHOHATEE/
Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857