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 .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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.
The dependent claims 2-6, 9-10, 12-16 and 18-120 are rejected under 35 U.S.C. 101 because directed to non-statutory subject matter. Claims 2 and 12 recite (NRMSE), claims 4 and 14 recite (subtracting), claims 5 and 15 recite (average) is a mathematical concepts MPEP 2106.04(a)(2)(I) , Mathematical relationships of abstract ideas; claims 3, 6, 13, 16 and 18-19 recite Mathematical formulas or equations or Mathematical calculations mental processes MPEP 2106.04(a)(2)(III); claim 9 recite manually recorded by the operator is performed in the human mind using pen and paper, grouping a mental process which is directed to abstract idea, claim 10 recite determined and analytics the video stream is performed in the human mind using pen and paper, grouping a mental process, claim 20 recite reevaluate, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. Such step performed in the human mind observations, evaluations, judgments, and opinions group, are considered to recite an abstract idea. which is directed to the abstract idea.
This judicial exception is not integrated into a practical application. As Claims 2-6 and 12-16 recites no addition elements use the mathematical formulas and calculations that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As claims 18-19, only recites one additional element – using a processor to perform mathematical formulas and calculations. The processor in the steps recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of equations. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. As claims 9-10, recites no addition elements use to perform manually recorded or determined by performing video analytics. Accordingly, no additional element that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
As claim 20, only recites one additional element – using a processor to perform reevaluate step. The processor in the steps recited at a high-level of generality (i.e., as a generic processor performing a generic computer function). such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As claims 2-6, 9-10 and 12-16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As claims 18-19 discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the mathematical formulas and calculations no more than mere instructions to apply the exception using a generic computer component. As claim 20, discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform reevaluate amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims 2-6,9-10, 12-16 and 18-20 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
3. 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.
3.1 Claim(s) 1, 5, 7-11, 15, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Candanedo et al. (Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models) in view of Chowdhury (US 20180011463 A1).
Regarding claim 1 and 11, Candanedo discloses a method for controlling one or more components of a Building Management System (BMS) of a building in accordance with an estimated occupancy count of a space of the building (Page 28-29, section 1 and 1.2, Introduction, Fig. 3, occupancy detection in building, occupancy data was used as an input for HVAC control algorithms. Determination of occupancy to lower energy consumption by appropriate control of HVAC and lighting systems in buildings) the method comprising:
monitoring an occupancy count of the space of the building from each of a plurality of occupancy sensors (Abstract, page 28-29, A vision-based system for occupancy detection and activity analysis. It used a camera and automatic image analysis to find the humans in the field of vision. The system was able to count the number of occupants in the images. The accuracy of the prediction of occupancy in an office room using data from light, temperature, humidity and CO2 sensors);
identifying an error parameter for each of the plurality of occupancy sensors (Table 1, sensor /parameter for example, CO2, computer current, light, PIR, sound sensor with accuracy for occupancy, Ranging from 81% to 98.441% (only PIR), Only light: 81.01%, Only sound: 90.78%, Only CO2: 94.68%);
each error parameter (page 29, section 1.2, table 7, reported that an accuracy of 80% was obtained in the detection of the number of occupants) representative of a difference between the occupancy count of the respective occupancy sensor and a ground truth occupancy count of the space (Page 29, section 1.2, column 2, Table 2, The ground truth occupancy and the sensors recorded were compared and the error was 0% for the determination of the number of occupants),
determining an assigned weight for each of the plurality of occupancy sensors () based at least in part on the respective error parameter (Table 1-4, Table 7, Page 30, Table 1, column 1, Sensors with high resolution are employed and Poor sensor calibration and low resolution seem to lower the accuracy prediction and giving the priority or ranks base the accuracy percentage);
determining the estimated occupancy count of the space of the building (page 29, predicting occupancy) based at least in part on:
the occupancy count of each of the plurality of occupancy sensors (Abstract, Page 28-29, section 1.2, column 2, count the number of occupants in the images or the accuracy of the prediction of occupancy in an office room using data from light, temperature, humidity and CO2 sensors. Occupancy models were developed from data of a wireless sensor network that monitored occupancy);
the assigned weight of each of the plurality of occupancy sensors (Table 1-4, Table 7, Page 30, Table 1, column 1, Sensors with high resolution are employed and Poor sensor calibration and low resolution seem to lower the accuracy prediction and giving the priority or ranks base the accuracy percentage); and
controlling the BMS based at least in part on the estimated occupancy count ((page 28, section 1, column 1, occupancy data was used as an input for HVAC control algorithms. The determination of occupancy a very promising approach to lower energy consumption by appropriate control of HVAC and lighting systems in buildings).
However, Candanedo fails to disclose the controller operatively coupled to the plurality of occupancy sensors, and normalized over a period of time.
Chowdhury discloses the controller operatively coupled to the plurality of occupancy sensors (Fig. 1, The controller 110 is connected to the people counting sensors 102 via the network 10); and normalized over a period of time (Abstract, poll one or more people counting sensors associated with access points to a defined region of the building to obtain counts data from the one or more people counting sensors for a specified time period and historical calibration data for the one or more people counting sensors; and process the counts data and the historical calibration data to determine a normalized occupancy of the defined region during the specified time period).
Candanedo and Chowdhury are analogous art. They relate to methods and systems for determining occupancy of a building.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify determining normalized occupancy, taught by Chowdhury, incorporated with accuracy of the prediction of occupancy, taught by Candanedo, in order to accurate and reliable counts of people in defined regions of a building can facilitate making informed decisions regarding management of building utilities, space planning, staff scheduling, retail analytics, and space and energy usage optimization.
Regarding claims 5 and 15, Candanedo discloses the estimated occupancy count of the space of the building (Page 29-31, column 2, detect and predict occupancy the number of occupants) is a weighted average of the occupancy count from all of the plurality of occupancy sensors (Fig. 3, Fig. 9-10, Table Table1, Table 2, Table 7, page 29-31, section 3, Sensor data from CO2, sound, relative humidity, air temperature, computer temperature and motion to reported that have important contributions to the modeling results and assigned a rank for each sensor base the accuracy, as shown in Fig. 3, Fig. 9-10, Table 7).
Regarding claim 7, Candanedo discloses the period of time corresponds to a training period of time (Page 29, section 1.2, Sensor data from CO2, sound, relative humidity, air temperature, computer temperature and motion was used to train a neural-network model).
Regarding claim 8, Candanedo discloses repeatedly updating the assigned weights for each of the plurality of occupancy sensors from time to time to accommodate a change in accuracy of one or more of the plurality of occupancy sensors (Page 31, page 35-36, section 3, Fig. 3, Fig. 4, Table 3, 4, Table 7, recording data and data set of each sensor in a different time to improve the accuracy. High accuracies for all the model predictions are typically found when all the measured parameters are taken into account for model training).
Regarding claim 9, Candanedo discloses the ground truth occupancy count of the space is manually recorded by an operator (Page 29, section 1.2, The truth occupancy was found using a video camera and tagged by human observers. A vision-based system for occupancy detection and activity, analysis was presented. It used a camera and automatic image analysis to find the humans in the field of vision. The system was able to count the number of occupants in the images. The system was reported to have 97% accuracy in detection. The data was used to calibrate the belief network).
Regarding claim 10, Candanedo discloses the ground truth occupancy count of the space is determined by performing video analytics on one or more video streams from one or more video cameras (Page 29, section 1.2, par. 1, The truth occupancy was found using a video camera and tagged by human observers. The data was used to calibrate the belief network).
Regarding claim 17, Candanedo discloses the one or more processors to (Page 30, section 2, column 1, a microcontroller was employed to acquire the data):
access a trained model that is trained to predict an occupancy count of a space of a building using time stamped occupancy data from a number of different occupancy sensors (Fig. 1, light, CO2, temperature & humidity sensors) and corresponding time stamped ground truth occupancy data (page 29-31, Fig. 1, Fig. 2, section 1.2 and 2, column 1, A digital camera was used to determine if the room was occupied or not. The camera time stamped pictures every minute and these were studied manually to label the data. See Fig. 1 for photograph of the setup and the truth occupancy was found using a video camera and tagged by human observers. combinations of temperature and light, humidity and light, light and CO2and light and humidity sensors ratio for training the classification models)
predict an occupancy count of the space of the building (abstract, prediction of occupancy in an office room) by:
providing the trained model with time stamped occupancy data pertaining to the space of the building from each of the number of different occupancy sensors (page 31, section 3, Fig. 3, recording data and data sets of the plurality sensors, for example, light, humidity and light, light and CO2and light and humidity, indication the room occupied training the classification models, for example, statistical models CART, RF, GBM and LDA, using time and data, time stamp);
the trained model (Table 7, training Linear Discriminant Analysis (LDA)), outputting an estimated occupancy value that represents an estimated occupancy count in space of the building (Abstract, estimate/prediction the occupancy in an office room; Table 5, Table 7, Fig. 10, Page 35-37, the accuracy prediction information from the time stamp has been included in the training models, such as training Linear Discriminant Analysis (LDA). Different models have been trained considering different predictor and determination of the occupancy status combinations as summarized in Table 7); and
control a BMS of the building based at least in part on the estimated occupancy value (Abstract, Page 29, section 1, introduction, the prediction of occupancy in an office room using data from light, temperature, humidity and CO2 sensors has been evaluated with different statistical classification models and used as an input to lower energy consumption by appropriate control of HVAC and lighting systems in the buildings).
However, Candanedo fails to disclose a non-transitory computer-readable storage medium having stored thereon instructions that when executed by one or more processors.
Chowdhury discloses a non-transitory computer-readable storage medium having stored thereon instructions that when executed by one or more processors (Abstract, [0010], [0025], The device, server or computer includes a memory and a processor coupled to the memory. The processor is configured to: poll one or more people counting sensors).
Candanedo and Chowdhury are analogous art. They relate to methods and systems for determining occupancy of a building.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify determining normalized occupancy, taught by Chowdhury, incorporated with accuracy of the prediction of occupancy, taught by Candanedo, in order to accurate and reliable counts of people in defined regions of a building can facilitate making informed decisions regarding management of building utilities, space planning, staff scheduling, retail analytics, and space and energy usage optimization.
Regarding claim20, Candanedo discloses the one or more processors are caused to periodically reevaluate the weights assigned to each of the different occupancy sensors. (Page 31, page 35-36, section 3, Fig. 3, Fig. 4, Table 3, 4, Table 7, recording data and data set of each sensor in a different time to improve the accuracy. High accuracies for all the model predictions are typically found when all the measured parameters are taken into account for model training).
3.2 Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Candanedo et al. (Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models) in view of Chowdhury (US 20180011463 A1) further in view of Thokala et al. (US 20220327263 A1).
Regarding claims 2 and 12, the combination of Candanedo and Chowdhury discloses the limitations of claim 1 and claim 11, but fails to discloses the limitations of claims 2 and 12. However, Thokala discloses the error parameter for each of the plurality of occupancy sensors represents a normalized root mean square error (NRMSE) for the respective occupancy sensor ([0032], [0047]-[0048], the power consumption data with other sensory information such as occupancy and temperature. Usually, the occupancy and temperature are available at one-hour granularity. the performance of the system is demonstrated using the actual power consumption of the one or more buildings. For performance evaluation Symmetric Mean Absolute Percentage Error (SMAPE) and a Normalized Root Mean Squared Error (NRMSE) are considered as the error terms as these are scale independent making them applicable for comparing model performance across one or more buildings of different capacities).
Thokala and Candanedo and Chowdhury are analogous art. They relate to methods and systems for building automation.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify building energy management system, taught by Thokala, incorporated with teaching of Chowdhury and Candanedo, as state above, in order to estimate the functional parameters for every new slot in predefined forecast horizon making it more efficient in forecasting accurately even as the horizon increases.
Allowable Subject Matter
4. Claims 3-4, 6, 13-14, 16 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Citation Pertinent prior art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Luca et al. (US20150286948A1) discloses generally to the field of occupancy detection, and more particularly to an occupancy detection system and a corresponding method suitable for a presence -controlled system.
Gribble (US 20040030417 A1) discloses a tracking system is provided for detecting abnormal drift errors in the outputs of sensors (3) monitoring a plurality of parameters of a gas turbine engine (1). To this end the tracking system comprises a tracking simulator (5) providing a real-time computer model of the engine having control inputs for receiving control signals.
Wootton et al. (US 10382893 B1) discloses systems and methods that can utilize the detection of human occupancy without fiducial elements to control an environmental, security, or other system within a structure. The systems and method can initiate communication to a human user directly, and can alter their operation based on human presence.
Wouhaybi (US 20180183661 A1) discloses sensor normalization including receiving a first value of a characteristic measured by a first sensor, receiving a second value of the characteristic measured by a second sensor more accurate than the first sensor; determining a normalization for the first sensor based on the first value and the second value, wherein the normalization to alter a response equation of the first sensor; and providing the normalization to be available for another sensor of a same type as the first sensor.
Ekwevugbe discloses an infrared camera was mounted in the test area to capture occupants' traffic. Video capture and recording used an ordinary laptop, with images captured at a five-minute interval. This was also found to be a more efficient approach than, capturing live streaming video, with no significant loss of resolution. Validation of occupancy counts was carried out by analysis of infrared camera images.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119