DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,2,3,5-10,12-17 and 19-20 of U.S. Patent No.: US 12104815 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 1-17 are generic to all that are recited in claims 1,2,3,5-10,12-17 and 19-20 of U.S. Patent No.: US 12104815 B2.
Claim 1 is generic to species of invention covered by claim 10 of the above recited patent. The only difference is claim 1 of the current application does not recite the limitation about distinguishing between first and second audio signatures and filtering the first and second audio signatures since they are received from an electronic device. Claim 1 is broad in view of claim 1 of the US Patent No.: US 12104815 B2. Thus, the generic invention of the application is “anticipated” by the species of the patented invention of U.S. Patent No.: US 12104815 B2.
For claims 2-17, they are generic to species of invention covered by claims 2,3,5-10,12-17 and 19-20 of U.S. Patent No.US 12104815 B2. Thus, the generic inventions are “anticipated” by the species of the patented invention.
A comparison of the independent claim 1 of the instant application and the independent claim 1 of the US 12104815 B2 are shown in the table below:
Instant Application
U.S. Patent No.US 12104815.
An occupancy tracking device, comprising: a network interface operably coupled to a Heating, Ventilation, and Air Conditioning (HVAC) system, wherein the HV AC system is configured to control a temperature of a space; and
a processor operably coupled to the network interface, configured to: establish a network connection with an access point; receive a plurality of sound samples over a first predetermined time period; identify a plurality of voices within the plurality of sound samples; determine a first occupancy level based at least in part upon the plurality of voices, wherein the first occupancy level indicates a first number of people that are present within the space; identify user devices connected to the access point; determine a second occupancy level based at least in part upon the user devices that are connected to the access point, wherein the second occupancy level indicates a second number of people that are present within the space;
measure a signal strength of the network connection with the access point; capture wireless signal distortion information for the network connection over the first predetermined time period, wherein the wireless signal distortion information identifies a first plurality of signal strength measurements of the network connection with the access point; generate statistical metadata for the wireless signal distortion information; input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model, wherein: the machine learning model is configured to determine a third occupancy level based at least in part upon the wireless signal distortion information and the statistical metadata for the wireless signal distortion
information;
determine the third occupancy level based at least in part upon the
measured signal strength of the network connection with the access point from
the machine learning model, wherein the third occupancy level indicates a third
number of people that are present within the space;
determine a predicted occupancy level based at least in part upon a
consensus between the first occupancy level, the second occupancy level, and
the third occupancy level;
control the HVAC system over a second predetermined time period
based at least in part upon the predicted occupancy level, the first predetermined time period being different from the second predetermined time period;
capture training information for the network connection over the second
predetermined time period, the training information identifying a second
plurality of signal strength measurements of the network connection with the
access point; and
in conjunction with capturing the training information over the second
predetermined time period:
determine a number of people that are present within the space; and
train the machine learning model using the training information
and the number of people that are present within the space.
An occupancy tracking device, comprising:
a network interface operably coupled to a Heating, Ventilation, and Air Conditioning
(HV AC) system, wherein the HV AC system is configured to control a temperature of a space;
and
a processor operably coupled to the network interface, configured to:
establish a network connection with an access point;
receive a plurality of sound samples over a first predetermined time period;
determine a direction of arrival for each sound sample of the plurality of sound
samples;
identify a plurality of voices within the plurality of sound samples, each voice
of the plurality of voices being associated with a corresponding person speaking;
determine a plurality of audio signatures corresponding to the plurality of
voices, wherein each audio signature is associated with a corresponding voice;
determine that a first voice of the plurality of voices is associated with a first direction of arrival and a first audio signature;
determine that a second voice of the plurality of voices is associated with the
first direction of arrival and a second audio signature;
identify that the first voice and the second voice have a common sound source associated with a first location in the space based at least in part upon determining that the first voice and the second voice comprise a same direction of arrival;
identify the common sound source as an electronic device in the space based at
least in part upon determining that the first voice and the second voice comprise different audio signatures;
filter out the first voice and the second voice from the plurality of voices based
at least in part upon identifying that the first voice and the second voice are received from the electronic device;
determine a first occupancy level based at least in part upon the plurality of
voices that remain after the first voice and the second voice are filtered, wherein the first occupancy level indicates a first number of people that are present within the space;
identify user devices connected to the access point;
determine a second occupancy level based at least in part upon the user devices that are connected to the access point, wherein the second occupancy level indicates a second number of people that are present within the space;
measure a signal strength of the network connection with the access point;
capture wireless signal distortion information for the network connection
over the first predetermined time period, wherein the wireless signal distortion
information identifies a first plurality of signal strength measurements of the
network connection with the access point;
generate statistical metadata for the wireless signal distortion information;
input the wireless signal distortion information and the statistical metadata
for the wireless signal distortion information into a machine learning model, wherein:
the machine learning model is configured to determine a third
occupancy level based at least in part upon the wireless signal distortion
information and the statistical metadata for the wireless signal distortion
information; determine [[a]] the third occupancy level based at least in part upon the measured signal strength of the network connection with the access point from the machine learning model, wherein the third occupancy level indicates a third number of people that are present within the space;
determine a predicted occupancy level based at least in part upon a consensus between the first occupancy level, the second occupancy level, and the third occupancy level;
control the HVAC system over a second predetermined time period based at least in part upon the predicted occupancy level, the first predetermined time period being different from the second predetermined time period;
capture training information for the network connection over the second
predetermined time period, the training information identifying a second plurality of signal strength measurements of the network connection with the access point;
and
in conjunction with capturing the training information over the second
predetermined time period:
determine a number of people that are present within the space; and
train the machine learning model using the training information
and the number of people that are present within the space.
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 1,2,6,7,8,12,13,14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bentz et al. (US 20210287311 A 1) in view of Bermudez (US 20180292520 A1) and White et al. (US 20190214019 A1) in further view of Bures et al. (US 20200322703 A1).
Regarding claim 1, Bentz et al. teaches, an occupancy tracking device (thermostat having sensors to track occupancy, [0084]), comprising: a network interface operably coupled to a Heating, Ventilation, and Air Conditioning (HVAC) system, wherein the HVAC system is configured to control a temperature of a space (HVAC system having thermostat connected over the communication network controlling temperature of a space, [0084] and [0085]); and
a processor operably coupled to the network interface (processor 702 connected to the network, [0086]), configured to:
establish a network connection with an access point (thermostat connected over the network through router 2402 (access point) to which all the devices are connected, [0151]);
receive a plurality of sound samples over a first predetermined time
period (audio data (sound samples) received from the microphones (plurality of sound samples) within the set amount of time, [0105] and [0104]);
identify a plurality of voices within the plurality of sound samples (" In some embodiments, the voice recognition module 716 identifies the one or more users1 based on voice biometrics of the audio received from sensors 602-606 .. . ",[0105]);
determine a first occupancy level based at least in part upon the plurality
of voices, wherein the first occupancy level indicates a first number of people
that are present within the space (based on voices identified, determine number of
users/occupancy at the space, [0105] and [0100]2);
identify user devices connected to the access point (occupancy identifier
identifying one or more users from user devices sending data through communications
interface 610, [0112], [0133] and [0151]);
determine a second occupancy level based at least in part upon the user
devices that are connected to the access point, wherein the second occupancy
level indicates a second number of people that are present within the space (thermostat determining occupancy level based on communication with user connected
devices to the access point router 2402, display settings or execute settings control based on the identified users, [0122], [0112],[0133],[0134] and [0151]);
control the HVAC system over a second predetermined time period
based at least in part upon the predicted occupancy level (based on the identified users, the thermostat adjust the temperature of the space based on preferred settings, [0100], [0105] and [0112]), the first predetermined time period being different from the second predetermined time period (the thermostat can control the settings based on currently detected occupancy like the user is home- first predetermined time or based on predicted occupancy times such as user will be arriving soon in the next window -second predetermined time based on preferred user settings determined when user was home (first predetermined time), [0116], [0117] and [0120]).
Bentz et al. does not teach the details of capturing the wireless signal distortion and use a machine learning model to determine the third occupancy level based on signal distortion, determine a predicted occupancy level based at least in part upon a
consensus between the first occupancy level, the second occupancy level, and
the third occupancy level; capture training information for the network connection over the second predetermined time period, the training information identifying a second
plurality of signal strength measurements of the network connection with the
access point; and in conjunction with capturing the training information over the second predetermined time period: determine a number of people that are present within the space; and train the machine learning model using the training information and the number of people that are present within the space. However Bentz et al. teaches to determine audio signatures and associate plurality of voices - audio signatures with corresponding person speaking via voice biometrics determine the number of occupants in a room in [0105] and also determined occupancy based on users equipment connected to the router-access point as taught in [0122] and [0112].
Bermudez et al. teaches, measure a signal strength of the network connection with the access point (changes in signal strength of the RF signal
is used as a basis of determining occupancy of the room, [0023] and [0026]-[0027]);
capture wireless signal distortion information for the network connection over the first predetermined time period (detecting change in strength of RF signals over consecutive time slots to determine occupancy of the space, [0021], [0026], [0027] and [0031]), wherein the wireless signal distortion information identifies a first plurality of signal strength measurements of the network connection with the access point (strengths of RF signals are received as signal strength indicators over multiple points in time as received by the router (access point), [0026], [0027], [0041] and [0052]);
generate statistical metadata for the wireless signal distortion information (" ... Using the sampled signal strength, occupancy-detection method 100 may update one or more statistical measures of the received RF signal(s) at step 115. Examples of useful statistical measures include the mean and variance of the received RF signal(s) ... “, [0026]);
input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model (machine learning model receiving change in signal strength, and other inputs to determine occupancy of the space, [0036] and [0034]), wherein:
the machine learning model is configured to determine a third occupancy level based at least in part upon the wireless signal distortion information and the statistical metadata for the wireless signal distortion information (" ... Such
machine-learning algorithms may be a function of the statistical measure(s) used in the
probability assessment process and other input(s) that themselves are known or tend to
indicate that a detected change-point and corresponding value(s) of the statistical measure(s) corresponds to an actual chance in occupancy", [0036] that is using machine learning model to determine occupancy of a space, [0037] and [0039]);
determine the third occupancy level based at least in part upon the
measured signal strength of the network connection with the access point from
the machine learning model (machine learning model determining occupancy of the
space based on received change in signal strength information, [0036], [0034], [0041] and [0052]),
wherein the third occupancy level indicates a third number of people that are present within the space (the occupancy of the space is determined by the machine learning model receiving multiple inputs such as change in signal strengths of user devices, [0044] and [0047]).
Bentz et al. and Bermudez et al. are analogous art because they are from the same field of endeavor that is controlling the HVAC system based on occupancy.
Therefore, it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the occupancy tracking
device which determines number of occupants within a space based on human voices
identified within in the space and user devices connected to the network/access point as
taught by Bentz et al. by applying the known technique of determining occupancy
based on measured signal strengths of the network connection with the network/access
point as taught by Bermudez to yield predictable results of controlling the HVAC system based on occupancy determination as taught by Bermudez in [0046].
Neither in combination nor individually Bentz et al. and Bermudez et al. teach the details of determining a predicted occupancy level based at least in part upon a
consensus between the first occupancy level, the second occupancy level, and
the third occupancy level; capture training information for the network connection over the second predetermined time period, the training information identifying a second
plurality of signal strength measurements of the network connection with the
access point; and in conjunction with capturing the training information over the second
predetermined time period: determine a number of people that are present within the space; and train the machine learning model using the training information
and the number of people that are present within the space. However Bermudez et al. teaches about using machine learning model to determine occupancy but does not teach anything about training the machine learning model with data collected at different points in time. Also Bentz et al. teaches about identifying users and corresponding occupancy based on different received data such as voice samples and user devices connected to the network but does not teach the details of combining the received data from different sources to determine a number of occupancy for a space.
White et al, teaches, determine a predicted occupancy level based at least in part upon a consensus between the first occupancy level, the second occupancy level, and the third occupancy level (the occupancy is determined based on
consensus of results of occupancy observations of multiple sensors3, [0080] and [0081]).
Bentz et al., Burmudez, and White et al. are analogous art because they are from the same field of endeavor that is detecting occupancy using various means.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the occupancy tracking
device determining occupancy by receiving data from multiple sources as taught by combination of Bentz et al. and Bermudez by applying the known technique of determining occupancy based on consensus between data received from multiple sources determining occupancy as taught by White et al. to yield predictable results of determining occupancy with in a space.
Neither in combination nor individually Bentz et al., Bermudez and White et al. teach the details of capture training information for the network connection over the second predetermined time period, the training information identifying a second
plurality of signal strength measurements of the network connection with the
access point; and in conjunction with capturing the training information over the second
predetermined time period: determine a number of people that are present within the space; and train the machine learning model using the training information and the number of people that are present within the space. However Bermudez teaches to determine occupancy using machine learning model and the occupancy detection is updated when new data is available as taught in [0026] and [0034] but does not teach anything about training the machine learning model.
Bures et al. teaches, capture training information for the network connection over the second predetermined time period (the gateway device measuring signal strengths of multiple sensor units over a multiple points in time that is training data captured over first, second, third and so on time periods. All the measured data are stored on the measured database and used to train the machine learning model as taught in [0162] and [0300]), the training information identifying a second plurality of signal strength measurements of the network connection with the access point (“…The signal strength of these signals broadcasted by one or more multi-sensor units 120; extracted timing data such as timestamps for measurements data included in these signals4 and/or a transmission time if timing between the one or more gateways devices and every multi-sensor unit 120 is synchronized…”, [0158], [0162] and [0300]); and
in conjunction with capturing the training information over the second
predetermined time period: determine a number of people that are present within the space (the occupancy sensors can determine occupancy for a space for consecutive time windows (first, second, third and so on time periods) and store all the information in measured database. The machine learning model is trained with the information from the measured database to predict multiple conditions based on received data such as heat maps, signal strength, occupancy and others from multiple time windows, [0162],[0163], [0333], [0334]);
and
train the machine learning model using the training information and the number of people that are present within the space (the machine learning model is trained using data from the measured database which has signal strength information collected over multiple points in time and heat maps showing current and predicted conditions5 within the space in multiple points in time. The machine learning model is retrained with updated data from the measured database collected over multiple consecutive time windows or over time, [0295], [0296], [0300] and [0333]).
Bentz et al., Burmudez, White et al. and Bures et al. are analogous art because they are from the same field of endeavor that is detecting occupancy using various means.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the occupancy tracking device determining occupancy using data from multiple sources and machine learning model as taught by combination of Bentz et al, Bermudez and White et al. by applying the known technique of training the machine learning model with training data related to signal strengths of devices connected over the network and occupancy data captured in multiple time windows as taught by Bures et al. to yield predictable results of train and retrain the machine learning model with recent received data to increase model prediction accuracy as taught by Bures et al. in [0295].
Bentz et al. teach:
[0133] Thermostat 600 may display different information based on the user detected. In some embodiments, thermostat 600 is able to distinguish between occupants based on information received from sensors 602-606. One of sensors 602-606 may be a camera, an IR sensor, a microphone, or any other conceivable sensor which could be used to detect occupancy. Thermostat 600 may only display the current temperature if a child or a pet is detected. In some embodiments, thermostat 600 may detect the user based on their identifiable personal device, and display a screen of her choice. For example, if a user prefers to see how long it will take to reach her settings, she can select that screen as the default screen when she is detected in the home. In another embodiment, thermostat 600 may display the most used screen. For example, if the temperature screen is used the most out of all screens available, thermostat 600 may display the temperature screen whenever occupancy is detected.
[0151] …Thermostat 600 may communicate directly with connected HVAC equipment 2420. Thermostat 600 may communicate with services such as weather service 2416, utility provider 2418, network 2422, or server 2424. In some embodiments, thermostat 600 communicates with devices through router 2402 to which the devices are connected…
[0122] Thermostat 600 may be able to determine what kind of activities are occurring in the home and change operation based on occupancy level. In some embodiments, thermostat 600 is able to detect separate occupants of the home. In other embodiments, thermostat 600 determines occupancy level based on communication with connected equipment. For example, thermostat 600 may be able to estimate occupancy based on assumed load seen by the AC unit. In another embodiment, thermostat600 may obtain activity information from a fitness tracker to determine the amount of activity related toa specific user. In yet another embodiment, thermostat 600 may use sensor 1102 to detect the amount of movement or activity occurring. For example, thermostat 600 may determine that a user is currently occupying a room, but that there is a low level of activity. Thermostat 600 may determine that the user is sleeping, and adjust conditioning accordingly. Thermostat 600 may determine that many people are in one room, and that there is a high level of activity, and increase conditioning accordingly.6
[0117] Referring now to FIG. 12A, thermostat 600 may determine occupancy based on a schedule or calendar. In some embodiments, a user is able to input a schedule directly to the thermostat. In other embodiments, thermostat 600 may support integration with existing calendar applications. In step1202, occupancy predictor 710 of memory 704 receives calendar data or a schedule from a user. Occupancy predictor 710 then determines when the user does not have any events scheduled in step1204. In some embodiments, thermostat 600 may allow a user to input a schedule of times when she expects to be home. The periods of time identified in step 1204 are then stored as predicted periods of occupancy (step 1206). In some embodiments, thermostat 600 may store the predicted occupancy periods in remote data storage 7187. In other embodiments, thermostat 600 may store the predicted occupancy periods locally in memory 704. In step 1208, operation commands are issued from thermostat 600 to the connected system based on the occupancy periods stored and the associated user's preferences.
Bermudez teach:
[0023] As those skilled in the art will readily understand, each person or animal within room 208 can affect the intensity of the RF signal(s) as received by RF receiver 200. For example, each person or animal can absorb a portion of the RF signal(s), can occlude a portion of the RF signal(s) reaching receiver 200, and can cause additional reflections of the RF signal(s) within the room, and each of these can measurably affect the strength of the RF signal(s) that the RF receiver 200 receives8. As described below in detail, method 100 utilizes changes in signal strength of the RF signal(s) it receives as a basis of determining occupancy of room 208. It is noted that RF receiver and transmitter 200 and204, respectively, can be placed in fixed locations within room 208 that tend to cause a person or animal, when present in the room, to bring about the greatest change in the signal strength of the received RF signal(s) relative to the signal strength of the received RF signal(s) when the room is unoccupied.
[0027] At step 120, occupancy-detection method 100 performs a change-point detection analysis on the sampled signal strength to determine whether or not the received RF signal(s) exhibit(s) that a change-point has occurred. As will be readily appreciated, occupancy-detection method 100 performs the change-point detection analysis in a sequential, or online, manner such that the method is continually seeking to recognize change-points in the sampled received RF signal(s). Time-series sequential change-point detection algorithms are well-known in a variety of fields, and those skilled in the art will readily be able to adapt any suitable ones of those algorithms to the time-series nature of the continual sampling of the signal strength of the received RF signal(s).9 Readers unfamiliar with change-point detection algorithms may refer to any of a variety of publications for detailed discussions of such algorithms….
[0052] Occupancy-detection system 508 in occupancy-detection scenario 500 may implement a suitable occupancy-detection method, such as occupancy-detection method 100 of FIG. 1 to determine the relevant change-points, here change-points 520A and 520B, in RSSI values RSSI(1,1)of the received signal. Such occupancy-detection method may also, for example, determine the relevant statistical measure(s) of RSSI values RSSI(1,1) of the received signal, compare the statistical measure(s) to stored values known to correspond to occupancy events, such as a person entering room 504 and leaving the room 504, and/or use other analytics to determine whether or not the room504 remains occupied, among other things.
[0047] As described above in connection with FIG. 1, occupancy-detection method 100 can optionally include machine learning (see step 150) that takes as input information collected from one or more external sources. In the example noted above, the additional information may be a signal that light switch 224 has been actuated. Examples of other non-RF-transmitter external devices that can be used for machine learning and can communicate information to occupancy-detection system 404, 404′include motion sensors, door-activated switches, thermal sensors, CO.sub.2 sensors, VOC sensors, aural sensors, relative humidity sensors, differential pressure sensors, airflow sensors, capacitive sensors, and trip sensors, among others. To receive such signals and/or other signal(s) for machine learning, occupancy-detection system 404, 404′ may include one or more additional I/O devices 452,each of which may be any suitable wired or wireless port device. It is noted that occupancy-detection system 404′ can be deployed, for example, as each of occupancy-detection systems 508 (FIG. 5A)and 604 (FIGS. 6A, 7A, and 8A) described below in the context of some example occupancy-detection scenarios.
Bures et al. teach:
[0162] In some embodiments, at least one of the additional communication interfaces 1-M are utilized to as tracking sensors. For example, short range radio signals transmitted by client devices 160, additional data sources 170, and/or other computing devices can be received by at least one of the additional communication interfaces 1-M. This can be utilized by the multi-sensor units 120 to detect that this device is proximity to the multi-sensor units 120. This information can similarly be utilized by the monitoring data analysis system 140 to determine a location and/or zone within the facility that
various computing devices are located at particular times, and can be utilized to track the movement of people associated with these computing devices10. The multi-sensor unit can generate measurement data indicating an identifier of the computing device, and/or timestamp data indicating the time that a signal was received from the computing device and/or a timeframe that a pairing and/or bi-directional communications with the computing device was established and/or maintained.
[0163] One or more of the set of sensor devices 1-W can include occupancy sensors operable to detect whether or not a region within the facility being measured by the sensor includes a person, animal, and/or another particular feature of interest, and/or to detect how many people, animals, or particular features of interest are within the region at a given time or within a time window. This can include motion detection devices, passive infrared sensors, ultrasonic sensors, and/or microwave sensors. Alternatively or in addition, the occupancy sensors can utilize one or more of the other sensors discussed herein to determine this occupancy data, for example, based on changes in
vibration, other motion, noise, electricity, light, carbon dioxide levels, and/or other measures discussed herein that can indicate the presence of people or animals and/or changes in presence in people or animals. The measurement values generated by occupancy sensors can include a binary value indicating whether or not the region is occupied by a feature of interest at a particular time or within a time window, a count of features of interest at a particular time or within the time window, and/or identifiers of features of interest detected in the region within the time window. In some embodiments, an occupancy function is performed by the processing module 240 and/or the monitoring data analysis system 140 on measurement data generated by other sensor devices discussed herein to generate the measurement values corresponding to occupancy.
[0300] Some models can be trained to generate a prediction based on data captured over time in a longer timeframe, such as data captured in multiple consecutive time windows and/or data captured at multiple times within the time frame11. A single feature vector can correspond to data captured at multiple times, within the timeframe, by the same and/or different measurement source. The fields of the feature vector can be further designated by a timestamp, relative to the width of the timeframe or ordered number of timestamps within the timeframe, alternatively or in addition to its measurement source.
[0295] The model can be trained by tuning the weights and/or parameters by performing a determined training step by utilizing the training databased on the training data, input and/or output format, the initial set of weights and/or parameters, the step sizes, and/or the number of iterations. The trained model can be retrained over time, for example, based on additional input fields12 and/or based on new data received over time, for example to increase accuracy of the model based on additional types of data, more recent data, and/or a higher volume of training data. The model can also be retrained
example, to broaden the application of the model, for example, to detect and/or characterize a condition across the facility at any time and/or to narrow or fine-tune the application of the model, for example, to detect and/or characterize a condition at specific location, corresponding to a specific feature, and/or at a specific time of day, month, and/or year.
Regarding claim 2, combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the device of claim 1. In addition Bentz et al. teaches, wherein in determining the first occupancy level, the processor is further configured to:
compute an audio signature for each sound sample from the plurality of sound samples (voice biometrics of the users are identified from the samples of voices received from the microphones to associate the voice with a user, [0105] and [0106]).
In addition White et al. teaches, in conjunction with determining a direction
of arrival for each sound sample from the plurality of sound samples (microphones
detecting direction of sound, intensity of the sound received from the occupants in the space, [0067]);
populate entries in a voice data log for the plurality of sound samples (the microphones receive the sound samples and records them (populating entries) for further analysis , [0067], [0070] and [0072]),
wherein each entry comprises an audio signature and a direction of arrival
(analyzing the detected the sound for determining direction of sound, intensity of sound
and frequency of sound, [0067] and [0068]);
identify one or more clusters for the populated entries based at least in part
upon the direction of arrival that is associated with the populated entries (central
server processing the received information regarding sound direction, frequency and
intensity to identify the number of occupants in the space, [0068], [0072] and [0081]);
determining a number of clusters that are identified, wherein each cluster
corresponds with a person that is present within the space (central server timestamps the received information to determine a number of occupants on the space, [0068], [0072] and [0081]); and
determine the first occupancy level based at least in part upon the number
of clusters that are identified (central server processing the received information
regarding sound direction, frequency and intensity to identify the number of occupants
in the space,[0068], [0072] and [0081]).
Regarding claim 6, combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the device of claim 1. In addition Bentz et al. teaches, wherein controlling the HVAC system, the processor is further configured to adjust a set point temperature for the space (thermostat conditioning the space based on
occupancy detection and user defined setpoints, [0082], [0097] and [0116]).
Regarding claim 7 combination of Bentz et al., Bermudez, White et
al. and Bures et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking method implementing the functional steps of the occupancy tracking device as discussed in claim 1.
Regarding claims 8 and 12 combination of Bentz et al., Bermudez, White et
al. and Bures et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking method implementing the functional steps of the occupancy tracking device as discussed in claims 2 and 6.
Regarding claim 13 combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking system implementing the functional steps of the occupancy tracking device as discussed in claim 1.
Regarding claims 14 and 17 combination of Bentz et al., Bermudez, White et
al. and Bures et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking system implementing the functional steps of the occupancy tracking device as discussed in claims 2 and 6.
Claims 3,9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bentz et al. (US 20210287311 A1) in view of Bermudez (US 20210287311 A1) and White et al. (US 20190214019 A1) in further view of Bures et al. (US 20200322703 A1) and Alpert et al. (US 10,257,295 B1).
Regarding claim 3 combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the device of claim 1.
Neither in combination nor individually of Bentz et al., Bermudez, White et al. and Bures et al. explicitly teach the detail of identifying plurality of devices connected to
the access point over a predetermined time period, populating entries and identifying
and determining clusters for the entries for corresponding each cluster with a person
within the space. However Bentz et al. explicitly teaches to identify user devices
connected to the network and identify the user occupying the space based on user
device identification as taught in [0122], [0132] and [0133] but data collection for user devices over periods of time is not clear.
Alpert et al. teaches, identify a plurality of devices that are connected to the access point over a predetermined time period (monitoring user devices network traffic at predefined intervals such as et every 10 minutes, Col.11 lines 30-45);
populate entries in a device log for the plurality of devices, wherein each
entry comprises a timestamp and a device identifier (if determined the known user
device in not detected in the network for over 10 minutes at three consecutive time
intervals, state to the central server the user device is no longer on the network, Col.11
lines 30-45);
identify one or more clusters for the entries of the device log, wherein each
cluster identifies one or more devices that are present within the space at
the same time identifier (based on information from the known host list, the known
user devices can be identified from the devices connected to the network and the
presence of the specific user can be detected at every 10 minutes interval (first, second and so on predetermined time periods), Col.12 lines 50-60 and Col.11 lines 30-
45); and
determine a number of clusters that are identified, wherein each cluster
corresponds with a person that is present within the space (based on known user
devices connected to the network over a period of time, occupancy of a specific user in
a space is determined, Col.11 lines 30 -45 and Bentz et al. [0122]).
Bentz et al., Burmudez, White et al., Bures et al. and Alpert et al. are analogous art because they are from the same field of endeavor that is detecting occupancy using various means.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art modify the occupancy tracking
device identifying user and occupancy level based on user devices connected to the network as taught by combination of Bentz et al., Bermudez, White et al. and Bures et al. by applying the known technique identifying the devices are connected to the network for over a period of time based of which entries are populated including timestamp and user identifier to determine occupancy as taught by Alpert et al. to yield predictable result of determining occupancy level in a space.
Regarding claim 9 combination of Bentz et al., Bermudez, White et al., Bures et al. and Alpert et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking method implementing the functional steps of the occupancy tracking device as discussed in claim 3.
Regarding claim 15 combination of Bentz et al., Bermudez, White et al., Bures et al. and Alpert et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking system implementing the functional steps of the occupancy tracking device as discussed in claim 3.
Claims 4,5,10,11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bentz et al. (US 20210287311 A 1) in view of Bermudez (US 20180292520 A1) and White et al. (US 20190214019 A1) in further view of Bures et al. (US 20200322703 A1) and Lydecker et al. (US 20150134136 A1).
Regarding claim 4 combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the device of claim 1.
Neither in combination nor individually of Bentz et al., Bermudez, White et al. and Bures et al. explicitly teach the detail of transitioning the HVAC system out of a low power mode when number of people present in the space is greater than zero. However
Bentz et al. explicitly teaches that based on anticipating the occupancy of the home within a certain time, adjust the operation of the HVAC system to condition the space per user preferred setting by the time user will be home as taught in [0097]. The details of transitioning in/out of low power mode is not taught but can be inferred since the HVAC operates in a manner to conserve energy based on occupancy detection.
On the other hand, Lydecker et al. teaches, determine a number of people that are present within the space is greater than zero (if occupancy is detected within the time period, the load device13 remain in high power mode but no occupancy is detected within the time period, the load device transitions to low power mode, [0089]); and
transition the HVAC system out of a low power mode (if occupancy is
detected within the time period, the load device remain in high power mode but if no
occupancy is detected within the time period, the load device transitions to low power
mode, [0089]).
Bentz et al., Burmudez, White et al., Bures et al. and Lydecker et al. are analogous art because they are from the same field of endeavor that is detecting occupancy using various means.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the occupancy tracking
device of HVAC system as taught by combination of Bentz et al., Bermudez, White et al. and Bures et al. by applying the known technique of transitioning the HVAC system
out of low power mode when occupancy is detected as taught by Lydecker et al. to yield predictable results of operating the HVAC system efficiently by transitioning modes of operation based on occupancy detection.
Regarding claim 5 combination of Bentz et al., Bermudez, White et al. and Bures et al. teach the device of claim 1.
Neither in combination nor individually of Bentz et al., Bermudez, White et al. and Bures et al. explicitly teach the detail of determine a number of people that are present within the space is zero; and transition the HVAC system to a low power mode. However Bentz et al. explicitly teaches that based on anticipating the occupancy of the home within a certain time, adjust the operation of the HVAC system to condition the space per user preferred setting by the time user will be home as taught in [0097]. The details of transitioning in/out of low power mode is not taught but can be inferred since the HVAC operates in a manner to conserve energy based on occupancy detection.
On the other hand Lydecker et al. teaches, determine a number of people that are present within the space is zero (during the sensing time period, if no occupancy is detected that is number of people is zero, the load device is transitioned to low power mode, [0089]); and
transition the HVAC system to a low power mode (during the sensing time period, if no occupancy is detected, the load device is transitioned to low power mode, [0089]).
Bentz et al., Burmudez, White et al., Bures et al. and Lydecker et al. are analogous art because they are from the same field of endeavor that is detecting occupancy using various means.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the occupancy tracking
device of HVAC system as taught by combination of Bentz et al., Bermudez, White et al. and Bures et al. by applying the known technique of transitioning the HVAC system
to low power mode when occupancy is not detected as taught by Lydecker et al. to yield predictable results of operating the HVAC system efficiently by transitioning modes of operation based on occupancy detection.
Regarding claims 10 and 11 combination of Bentz et al., Bermudez, White et
al., Bures et al. and Lydecker et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking method implementing the functional steps of the occupancy tracking device as discussed in claims 4 and 5.
Regarding claim 16 combination of Bentz et al., Bermudez, White et al., Bures et al. and Lydecker et al. teach the claimed occupancy tracking device. Therefore, together they teach the occupancy tracking system implementing the functional steps of the occupancy tracking device as discussed in claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jiang et al. (US 20210112097 A1) teaches an occupancy detection system where a machine learning model is trained based on occupancy data collected on various periods and other relevant information as meta data and update machine learning model training data continually for improved accuracy for occupancy detection as taught in [0031]-[0043].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMINI SHAH can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANZUMAN SHARMIN/ Examiner, Art Unit 2115
/KAMINI S SHAH/ Supervisory Patent Examiner, Art Unit 2115
1 Plurality of voices.
2 See also [0116].
3 Multiple sensors indicate voice recognition and connected devices to the network in view of Bentz
et al. and change in measured RF signal strength in view of Bermudez.
4 Signal strengths collected or received at multiple consecutive time windows that is over first, second, third and so on predetermined time windows as taught in [0300].
5 The generated heat maps at multiple points in time illustrate current and predicted conditions of the space for those points in time which can also include occupancy in view of [0222] and [0230].
6 Based on current occupancy, the thermostat is controlling the HVAC system that is controlling in first predetermined time.
7 Occupancy data collected at the first period of time when user performing setup and providing information. Based on data collected on first period of time, controlling the HVAC system over the second predetermined period of time that is when user is predicted to arrive based on schedule information provided by the user in the first predetermined time.
8 Signal strengths received by the receiver (access point) used to determine occupancy.
9 Signal strengths measured at multiple points in time.
10 Signal strengths of user devices tracked by the system.
11 Occupancy data and signal strength data captured at multiple timeframes which are fed to the machine learning model as training data.
12 Multiple data from the database such as occupancy, signal strength and others are used to train and retrain the machine learning model.
13 HVAC system in view of Bentz et al.
Prosecution Insights