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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “one or more ambient sensoring means comprising one or more ambient sound sensoring means” and “one or more computing means” in claim 1, “one or more ambient sensoring means comprises one or more ambient humidity sensoring means” in the claim 2, “one or more ambient sensoring means comprises one or more ambient temperature sensoring means” in the claim 3, “one or more ambient sensoring means comprises one or more ambient CO2 sensoring means” in the claim 4, “one or more ambient sensoring means comprises one or more ambient VOC sensoring means” in the claim 5, “one or more ambient sensoring means comprises one or more ambient light sensoring means” in the claim 6 and “one or more ambient sensoring means comprises one or more ambient water flow sensoring means” in the claim 7. Examiner interprets those sensors and the computing device recited above as the sufficient hardware structure to perform functions. Those sensors can be found on page 2, 5 and figure 1 of the specification. Examiner interprets those sensors as the sufficient hardware structure because those sensors mounting within the device for continuously monitoring the target object (human activities) in real time. For example, the sound sensor continuously detecting sound, the temperature sensor continuously sensing the temperature and CO2 sensor continuously detecting any gas within the environment. The computing can be found on page 2 lines 18, page 7 lines 14 and page 14 lines 17. Examiner interprets the computing as the sufficient hardware structure like a processor equipped with a storage because the computing is mounting within the device (page 14 lines 17) and storing profiles data (page 7 lines 14-15).
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5 recites “one or move ambient VOC sensoring” is indefinite because the claim and the specification do not further define VOC sensoring. However, for examining purpose and examiner treat VOC sensoring as Voltatile organic compound sensor.
Regarding claim 8 recites “VOC pattern profiles” is indefinite because the claim and the specification do not further define VOC. However, for examining purpose and examiner treat VOC as Voltatile organic compound.
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.
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 1-4, 7, 9-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chiu US 20170172368 in view of Jakobsson US 20200106637.
Regarding claim 1, Chiu teaches An alerting device for warning of anomalies in the behavioral patterns of a person, the device comprising: one or more ambient sensoring means comprising one or more ambient sound sensoring means, the one or more ambient sensoring means being adapted to continuously monitor real-time data originating from behavioral patterns of a person; (Chiu US 20170172368 abstract; paragraphs [0003]; [0006]; [0009]-[0012]; [0020]-[0027]; figures 1-4)
In practice, before going to work, the user can turn on the cleaning robot 100 to clean floor and, in the meantime, the air sensor 310, the temperature sensor 320 and the sound sensor 330 integrated in the cleaning robot 100 are also started to continuously sense the set of the ambient data which is then compared with the default security data to determine the ambient state. For example, when the one of concentrations of carbon monoxide, fine particulate matter and gas becomes abnormal, or the ambient temperature is far higher than a normal temperature, or the sensed sound is abnormal, the ambient state is determined to be dangerous. Various abnormal concentrations may damage human body, the temperature far higher than the normal temperature may occur fire, and abnormal sound possibly indicates that there is an intruder, so the ambient state is set as dangerous, and the notice message is then transmitted to the user end 150 and the network is initialized to enable the user at the user end 150 to remotely control the charge-coupled device 340 and the sound collection element 350 (such as microphone) to obtain instant home environment audio/video (Chiu par. 27).
Chiu does not explicitly teaches one or more pre-defined pattern profiles stored in the device, including one or more pre- defined sound patterns transformed via a Fast Fourier Transform (FFT) algorithm into one or more pre-defined FFT Sound Pattern Profiles; and one or more computing means configured to: continuously transform real-time sound monitored by the one or more ambient sound sensoring means via a Fast Fourier Transform (FFT) algorithm into one or more real-time FFT Sound Pattern Profiles, thereby allowing a privacy-protecting detection of the ambient sound from the site of operation; continuously perform a pattern recognition, including a continuous comparison of the one or more real-time FFT Sound Pattern Profiles with the one or more pre-defined FFT Sound Pattern Profiles; store one or more event triggers on the device in case of a match between real-time data and the one or more pre-defined pattern profiles, including a match between the one or more real-time FFT Sound Pattern Profiles and the one or more pre-defined FFT Sound Pattern Profiles; and execute an alert based on the one or more stored event triggers if pre-set data sharing permissions allow it, thereby warning of anomalies in the behavioral patterns of a person.
Jakobsson teaches one or more pre-defined pattern profiles stored in the device, including one or more pre- defined sound patterns transformed via a Fast Fourier Transform (FFT) algorithm into one or more pre-defined FFT Sound Pattern Profiles; and one or more computing means configured to: continuously transform real-time sound monitored by the one or more ambient sound sensoring means via a Fast Fourier Transform (FFT) algorithm into one or more real-time FFT Sound Pattern Profiles, thereby allowing a privacy-protecting detection of the ambient sound from the site of operation; continuously perform a pattern recognition, including a continuous comparison of the one or more real-time FFT Sound Pattern Profiles with the one or more pre-defined FFT Sound Pattern Profiles; (Jakobsson US 20200106637 abstract; paragraphs [0026] – [0030]; [0074]-[0079]; [0087]- [0093]; [0099]-[0107]; [0110]-[0123]; [0125]-[0128]; [0132]; [0150]-[0152]; figures 1-8)
Such information is very important to derive and act on, and accordingly, the system determines events that are likely to match such situations or needs, based on the sensor output profiles observed by the system. These profiles can be in the time range, for example, as in a typical reporting of sensor output values, or in the frequency range, for example, as in the reporting of a Fast Fourier Transform (FFT). The use of correlation between outputs is useful whether the values being processed are in the time or frequency range. Correlation between multiple types of sensor outputs is beneficial to obtain derived sensor profile data taking multiple dimensions of sensor data into consideration (Jakobsson par. 30). The system can determine music type by comparing the sound spectrum to spectrums of different genres of music, e.g., by comparing FFTs or performing image recognition of FFT plots. The system can also maintain identifying segments or FFTs of a set of common songs and match the sound associated with a space with these identifiers. This enables the automated generation of a music type predicate 122. This determination will preferably be done in the context of the determination of either pseudonym or identity, allowing the system to improve the automated selection of music, or of advertisements related to the musical preferences of the user, such as notifications or discount coupons associated with local performances, as well as other correlated preferences (Jakobsson par. 110). Similar techniques are also used to identify sports programs using sport program predicate 123. For example, a soccer match has a sound profile that is very distinct from that of a tennis match, enabling the use of FFTs to compare the sound associated with a space and that of a known sport or other generator of sound profiles. In addition, the system can compare the sound profiles observed with known broadcasting taking place at the time of the observation, e.g. using standard correlation methods between the broadcast stream (slightly delayed to account for the transmission) and the observed sound sequences associated with one or more sensors. Such arrangements can make use of not only sound sensors, but also, for example, inertial sensors (which detect low-frequency sounds), as well as motion sensors to find correlations between user actions and the broadcast sound profile. For example, the user may cheer after a goal is made, as will the on-site audience of the soccer game (Jakobsson par. 113).
According to the cited passages and figures, examiner interprets the system can apply a same technique to continuous tracking the environment with sound sensor and comparing to the prestored sound profiles.
store one or more event triggers on the device in case of a match between real-time data and the one or more pre-defined pattern profiles, including a match between the one or more real-time FFT Sound Pattern Profiles and the one or more pre-defined FFT Sound Pattern Profiles;
To the extent that the sound sensors are used for detection of voice commands, of course, the system will attempt to identify the presence of such voice commands—whether occurring in silence or in the presence of music—at the same time as it may also be attempting to determine the type of music being played. To the extent that the system has a baseline truth for any observation, e.g., the system was used as a remote control to select the music, this constitutes a tag that allows for the use of additional machine learning, and the determination of the success rate of the heuristics based simply on comparison with FFTs or time segments of sound inputs (Jakobsson par. 112). The system can detect that a room 130 has a washer, dryer and/or a water heater (“w/d/h”) based on sound profiles determined using one or more instances of sound sensor 101 being matched to stored appliance-specific sound profiles; by energy consumption profiles determined using one or more instances of energy sensor 112 being matched to stored appliance-specific energy consumption profiles; and by correlating the temperatures in the room as determined by one or more instances of temperature sensor 110 to stored appliance-specific temperature profiles; and by correlating the profiles of the different types to each other. For example, when the sound profile is correlated with the energy consumption profiles and the temperature profile with a correlation factor exceeding a threshold associated with a stored appliance-specific threshold, then this is indicative of the associated set of sensors with such outputs being in a space that is labeled as being a room with these appliances, such as a washer and dryer or a water heater. Similar profiles can be detected for other appliances, such as HVAC appliances (Jakobsson par. 121).
and execute an alert based on the one or more stored event triggers if pre-set data sharing permissions allow it, thereby warning of anomalies in the behavioral patterns of a person.
In step 806, a comparison is made whether there is a likely match to a registered user. Here, user registration may correspond to an explicit user action or may be based on repeated historical observation of identifiers associated with the user. If there is a match, then the intrusion detection unit initiates a verification in step 808. Such verification may comprise sending the matched user a notification and requesting a response, such as a confirmation of presence, the authentication using biometric techniques, etc. If there is no match, the intrusion detection unit initiates an alert in step 807. This may comprise sounding an alarm, sending messages informing registered users of the potential intrusion, or the collection of additional data, such as video data that may normally not be collected due to privacy concerns (Jakobsson par. 152).
According to the cited passages and figures, examiner interpret if there is no match as the anomalies condition, therefore the system will generating the alert based on the comparison of the result.
Therefore, it would have been obviously to one of ordinary skill in the art to substitute the known technique of using fast fourier transform to compare the sound input with the sound profiles as taught by Jakobsson reference into the system of Chiu reference. The result of the substitution would be predictable for the system to classify the sound and identify the location or event based on the matching result with the particular sound profile.
Regarding claim 2, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient humidity sensoring means being adapted to continuously monitor real-time humidity.
The collection of sensors 100 comprises one or more sensor units, where example sensor units include a sound sensor 101, a camera 102, a motion sensor 103, a radio unit 104 that may use WiFi, Bluetooth, Bluetooth low energy (BLE), near-field communication (NFC), ZigBee, and other types of radio; and which may comprise one or more of such units mounted on one or more separate boards associated with the collection of sensors 100. Additional sensors of the collection of sensors 100 comprise a humidity sensor 105, a pressure sensor 106, an inertial sensor 107, a carbon monoxide (CO) sensor 108, a tamper sensor 109 that detects that one or more of the sensors associated with the collection of sensors 100 is physically manipulated, or that the conveyance of the signals from the collection of sensors 100 is physically manipulated. The collection of sensors 100 further comprises a temperature sensor 110 and an energy sensor 112. The energy sensor 112 is configured to detect energy consumption or other types of energy use. Also included in the collection of sensors 100 is a user GPS sensor 111. Such a sensor is illustratively accessible via an API, and in some embodiments comprises a device that has GPS functionality and is associated with a user of the system. An example user GPS sensor 111 is the GPS unit of a handheld device such as a phone, which is accessed by the system via an API to access the location of the user. Although single instances of these and other sensors are shown in the collection of sensors 100, there can be multiple instances of one or more of the sensors, as well as multiple collections of different sensors, in other embodiments (Jakobsson par. 99).
Regarding claim 3, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient temperature sensoring means being adapted to continuously monitor real-time temperature.
The collection of sensors 100 comprises one or more sensor units, where example sensor units include a sound sensor 101, a camera 102, a motion sensor 103, a radio unit 104 that may use WiFi, Bluetooth, Bluetooth low energy (BLE), near-field communication (NFC), ZigBee, and other types of radio; and which may comprise one or more of such units mounted on one or more separate boards associated with the collection of sensors 100. Additional sensors of the collection of sensors 100 comprise a humidity sensor 105, a pressure sensor 106, an inertial sensor 107, a carbon monoxide (CO) sensor 108, a tamper sensor 109 that detects that one or more of the sensors associated with the collection of sensors 100 is physically manipulated, or that the conveyance of the signals from the collection of sensors 100 is physically manipulated. The collection of sensors 100 further comprises a temperature sensor 110 and an energy sensor 112. The energy sensor 112 is configured to detect energy consumption or other types of energy use. Also included in the collection of sensors 100 is a user GPS sensor 111. Such a sensor is illustratively accessible via an API, and in some embodiments comprises a device that has GPS functionality and is associated with a user of the system. An example user GPS sensor 111 is the GPS unit of a handheld device such as a phone, which is accessed by the system via an API to access the location of the user. Although single instances of these and other sensors are shown in the collection of sensors 100, there can be multiple instances of one or more of the sensors, as well as multiple collections of different sensors, in other embodiments (Jakobsson par. 99).
Regarding claim 4, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient CO2 sensoring means being adapted to continuously monitor real-time CO2.
The collection of sensors 100 comprises one or more sensor units, where example sensor units include a sound sensor 101, a camera 102, a motion sensor 103, a radio unit 104 that may use WiFi, Bluetooth, Bluetooth low energy (BLE), near-field communication (NFC), ZigBee, and other types of radio; and which may comprise one or more of such units mounted on one or more separate boards associated with the collection of sensors 100. Additional sensors of the collection of sensors 100 comprise a humidity sensor 105, a pressure sensor 106, an inertial sensor 107, a carbon monoxide (CO) sensor 108, a tamper sensor 109 that detects that one or more of the sensors associated with the collection of sensors 100 is physically manipulated, or that the conveyance of the signals from the collection of sensors 100 is physically manipulated. The collection of sensors 100 further comprises a temperature sensor 110 and an energy sensor 112. The energy sensor 112 is configured to detect energy consumption or other types of energy use. Also included in the collection of sensors 100 is a user GPS sensor 111. Such a sensor is illustratively accessible via an API, and in some embodiments comprises a device that has GPS functionality and is associated with a user of the system. An example user GPS sensor 111 is the GPS unit of a handheld device such as a phone, which is accessed by the system via an API to access the location of the user. Although single instances of these and other sensors are shown in the collection of sensors 100, there can be multiple instances of one or more of the sensors, as well as multiple collections of different sensors, in other embodiments (Jakobsson par. 99).
Regarding claim 7, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient water flow sensoring means being adapted to continuously monitor real-time water flow.
The motion and inertial sensor output associated with a fall is much more likely to correspond to a fall if it is observed in a room that is identified as the shower room, bathroom, or stairway, and following the sound of water being used (Jakobsson par. 90). A room is identified as a shower/bathroom 131 based on the system detecting increased humidity and/or sounds indicative, whether in time or frequency space, of showering, taking a bath, flushing, or other uses of water, as well as changes in temperature accompanying these other inputs, and of correlations between changes of all of these types of sensor output. It is beneficial to know that a room is a bathroom; for one thing, the presence of water in bathrooms increases the risk of accidents such as falls 134, and therefore, the system will be configured to identify, using instances of motion sensor 103 from nodes in a shower/bathroom 131 that a user is potentially falling. Whereas users can also fall in other rooms, the risk is greatest in rooms with water, and rooms associated with stairs, and therefore, the identification and labeling of such rooms is important. Stairs can be identified based on sound detected by sound sensor 101 and motion detected by motion sensor 103 for nearby nodes. Inertial sensor 107 is also beneficial to detect impact, which results from falls. Multiple instances of these and other sensors can also be used (Jakobsson par. 123).
According to the cited passages and figures, examiner interpret the sound sensor and motion sensor are use to tracking the water flow via sound and motion.
Regarding claim 9, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the pattern recognition includes a continuous comparison of real-time data monitored by the one or more ambient sensoring means, including ambient humidity sensoring means, with the one or more pre-defined pattern profiles.
A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type (Jakobsson par. 119). The system can detect that a room 130 has a washer, dryer and/or a water heater (“w/d/h”) based on sound profiles determined using one or more instances of sound sensor 101 being matched to stored appliance-specific sound profiles; by energy consumption profiles determined using one or more instances of energy sensor 112 being matched to stored appliance-specific energy consumption profiles; and by correlating the temperatures in the room as determined by one or more instances of temperature sensor 110 to stored appliance-specific temperature profiles; and by correlating the profiles of the different types to each other. For example, when the sound profile is correlated with the energy consumption profiles and the temperature profile with a correlation factor exceeding a threshold associated with a stored appliance-specific threshold, then this is indicative of the associated set of sensors with such outputs being in a space that is labeled as being a room with these appliances, such as a washer and dryer or a water heater. Similar profiles can be detected for other appliances, such as HVAC appliances (Jakobsson par. 121).
Regarding claim 10, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more pre-defined pattern profiles comprises one or more pre-defined sensor pattern profiles.
A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type (Jakobsson par. 119). The system can detect that a room 130 has a washer, dryer and/or a water heater (“w/d/h”) based on sound profiles determined using one or more instances of sound sensor 101 being matched to stored appliance-specific sound profiles; by energy consumption profiles determined using one or more instances of energy sensor 112 being matched to stored appliance-specific energy consumption profiles; and by correlating the temperatures in the room as determined by one or more instances of temperature sensor 110 to stored appliance-specific temperature profiles; and by correlating the profiles of the different types to each other. For example, when the sound profile is correlated with the energy consumption profiles and the temperature profile with a correlation factor exceeding a threshold associated with a stored appliance-specific threshold, then this is indicative of the associated set of sensors with such outputs being in a space that is labeled as being a room with these appliances, such as a washer and dryer or a water heater. Similar profiles can be detected for other appliances, such as HVAC appliances (Jakobsson par. 121).
Regarding claim 11, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more pre-defined sensor pattern profiles is correlated with the pre-defined FFT Sound Pattern Profiles, or wherein the pre-defined FFT Sound Pattern Profiles are correlated with the one or more pre-defined sensor pattern profiles.
Similar techniques are also used to identify sports programs using sport program predicate 123. For example, a soccer match has a sound profile that is very distinct from that of a tennis match, enabling the use of FFTs to compare the sound associated with a space and that of a known sport or other generator of sound profiles. In addition, the system can compare the sound profiles observed with known broadcasting taking place at the time of the observation, e.g. using standard correlation methods between the broadcast stream (slightly delayed to account for the transmission) and the observed sound sequences associated with one or more sensors. Such arrangements can make use of not only sound sensors, but also, for example, inertial sensors (which detect low-frequency sounds), as well as motion sensors to find correlations between user actions and the broadcast sound profile. For example, the user may cheer after a goal is made, as will the on-site audience of the soccer game (Jakobsson par. 113).
Regarding claim 12, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more computing means is configured to store one or more event triggers on the device in case of a match between real-time data and at least two pre-defined pattern profiles, such as at least three pre-defined pattern profiles.
A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type. In addition, by observing activity in the established kitchen 128, the system can determine the extent to which users eat at home vs. eat out; the extent to which users cook during weekdays; the type of appliances typically used for food preparation; and so on. This informs the risk profile as well as indicates demographic inferences and preference inferences for the users associated with the space, which is helpful for improving the system in terms of customizing configurations, services and content for the users (Jakobsson par. 119).
According to the cited passages and figures, examiner interprets sound profile (the humming of a dishwasher or the gurgling of a coffee maker) and heat profile (the indicate of the use of a stove or oven) are the real time data that match to determine the room is the kitchen according to multiple profiles like sound profiles, heat profile and energy consumption profile.
Regarding claim 13, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more event triggers includes a match between real-time data, including humidity data, and the one or more pre-defined pattern profiles.
A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type. In addition, by observing activity in the established kitchen 128, the system can determine the extent to which users eat at home vs. eat out; the extent to which users cook during weekdays; the type of appliances typically used for food preparation; and so on. This informs the risk profile as well as indicates demographic inferences and preference inferences for the users associated with the space, which is helpful for improving the system in terms of customizing configurations, services and content for the users (Jakobsson par. 119). A room is identified as a shower/bathroom 131 based on the system detecting increased humidity and/or sounds indicative, whether in time or frequency space, of showering, taking a bath, flushing, or other uses of water, as well as changes in temperature accompanying these other inputs, and of correlations between changes of all of these types of sensor output (Jakobsson par. 123).
Regarding claim 14, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the alert is executed in case of a match of at least two event triggers.
If a presence 121 is established in an entry room 132 while the alarm is turned on, and the identity 129 or the pseudonym 125 is not detected or recognized as a resident, then this is an indication that the alarm should sound or an alert be generated (Jakobsson par. 125).
According to the cited passages and figure above examiner interprets presence 121 (1st event) and the identity 129 or the pseudonym 125 is not detected or recognized as a resident (2nd event) and the alarm is triggered based on both the events is met.
Regarding claim 15, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the alert is executed in case of a match of at least three event triggers.
The system further detects increased risk of fire 135 based on output of temperature sensor 110 indicating increased temperature, energy consumption changes detected by energy sensor 112 indicative of melting of cables or overheating of appliances, failure detection of appliances as described above, sound profiles based on outputs of sound sensor 101 indicative of fire, changes in humidity or pressure from respective humidity sensor 105 and pressure sensor 106 indicative of fire, as well as combinations of these, and especially correlations of these types of sensor data, from one or more nodes in the network. As for many other disclosed types of detection, it is beneficial for the system to identify such risks in the context of the labeling of the room and the likely presence of gas or other flammable substances where the likely fire is detected (Jakobsson par. 127). As for other risks, risk of fire 135 is preferably reported to an automated backend system and also to a human operator, in addition to sounding local alarms and automatically generating notifications for emergency responders in proximity of the residence or business being observed. A pinpointing of the location of the fire is beneficial, as well as all location data of potential users in the space. This is also preferably reported, and can be used by emergency responders to prioritize and guide their efforts (Jakobsson par. 128).
According to the cited passages and figures, examiner interprets the alarm will generate according to the risk increase associated with multiple output events is triggered like temperature sensor detected the increased temperature (1st event), the energy sensor detected the melting of cables or overheating of appliance (2nd event) and the sound sensor detected the output of sound indicate of fire (3rd event).
Regarding claim 17, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more pre-defined pattern profiles stored in the device comprises one or more pre-defined sound patterns and one or more pre-defined humidity patterns.
A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type. In addition, by observing activity in the established kitchen 128, the system can determine the extent to which users eat at home vs. eat out; the extent to which users cook during weekdays; the type of appliances typically used for food preparation; and so on. This informs the risk profile as well as indicates demographic inferences and preference inferences for the users associated with the space, which is helpful for improving the system in terms of customizing configurations, services and content for the users (Jakobsson par. 119).
According to the cited passages and figures, examiner interprets sound profile (the humming of a dishwasher or the gurgling of a coffee maker), heat profile (the indicate of the use of a stove or oven) and humidity sensor detect the changing in the temperature for determining the humidity condition.
Regarding claim 18, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more pre-defined pattern profiles is based on data selected from the group consisting of data originating from showering data, toileting data, running water, vacuuming data, presence data, absence data, and combinations thereof.
A room is identified as a shower/bathroom 131 based on the system detecting increased humidity and/or sounds indicative, whether in time or frequency space, of showering, taking a bath, flushing, or other uses of water, as well as changes in temperature accompanying these other inputs, and of correlations between changes of all of these types of sensor output (Jakobsson par. 123). If a presence 121 is established in an entry room 132 while the alarm is turned on, and the identity 129 or the pseudonym 125 is not detected or recognized as a resident, then this is an indication that the alarm should sound or an alert be generated (Jakobsson par. 125).
Regarding claim 19, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the one or more pre-defined pattern profiles stored in the device is adaptive by means of a learning algorithm.
Multiple identifiers can be assigned with one and the same user, as illustrated in FIG. 1. Multiple users may use one device as well. The pseudonyms generated in this way, which may be simple local identifiers associated with MAC addresses and other identifiers, correspond to tagged data. This tagged data can be used to train machine learning (ML) components that take other sensor data such as sound and motion data, and correlates the user pseudonyms with such data in order to generate a classifier that, given the sensor data such as sound and motion alone, and without any MAC address or other signal received by a radio unit, assigns a set of sensor observations to a pseudonym (Jakobsson par. 26).
Regarding claim 20, the combination of Chiu and Jakobsson disclose The alerting device according to claim 1, wherein the Fast Fourier Transform (FFT) algorithm is performed in the device.
Such information is very important to derive and act on, and accordingly, the system determines events that are likely to match such situations or needs, based on the sensor output profiles observed by the system. These profiles can be in the time range, for example, as in a typical reporting of sensor output values, or in the frequency range, for example, as in the reporting of a Fast Fourier Transform (FFT). The use of correlation between outputs is useful whether the values being processed are in the time or frequency range. Correlation between multiple types of sensor outputs is beneficial to obtain derived sensor profile data taking multiple dimensions of sensor data into consideration (Jakobsson par. 30).
Claims 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Chiu US 20170172368 in view of Jakobsson US 20200106637 and further in view of Liu US 20220369965.
Regarding claim 5, the combination of Chiu and Jakobsson teach all the limitation in the claim 1.
The combination of Chiu and Jakobsson do not explicitly teach The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient VOC sensoring means being adapted to continuously monitor real-time VOC.
Liu teaches The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient VOC sensoring means being adapted to continuously monitor real-time VOC. (Liu US 20220369965 abstract; paragraphs [0005]-[0011]; [0044]-[0046]; [0049]-[0051]; [0054]-[0056]; figures 1-9)
In certain embodiments, the inlet on the device is covered by a membrane that is waterproof and/or breathable. The sensor array contains multiple VOC (voltatile organic compound) sensors that react to VOCs and produce signals when exposed to gases, vapors, or odor containing VOCs released from a subject as well as one or more physiological sensors. The device can integrate with multiple commercially available physiological sensors for monitoring vital signals. Such vital signals may include heart rate, pulse rate, respiratory rate, blood oxygen saturation, blood pressure, hydration level, stress, position & balance, body strain, neurological function, brain activity, blood pressure, cranial pressure, auscultatory information, skin and body temperature, sleep, cholesterol, lipids, blood panel, body fat density, muscle density. Additional sensors may be installed that monitor environment conditions, such as temperature, humidity, and pressure. The collected data can be used for pattern recognition and machine learning algorithms, which can be used for detection of diseases and perform other functions (Liu par. 44). Some embodiments of the device are capable to measure skin and body temperature (−15° C. to 45° C.), heart rate, humidity (0-99%), and a variety of concentrations of VOCs. The VOC detection limit may range from 0.1 ppb to 5000 ppm, e.g., 0.1 ppb-1 ppb, 1 ppb-5 ppb, 5 ppb-10 ppb, 10 ppb-50 ppb, 50 ppb-100 ppb, 100 ppb-200 ppb, 200 ppb-300 ppb, 300 ppb-500 ppb, 500 ppb-1 ppm, 1 ppm-2 ppm, 2 ppm-5 ppm, 5 ppm-10 ppm, 10 ppm-100 ppm, 100 ppm-200 ppm, 200 ppm-500 ppm, 500 ppm-1000 ppm, 1000 ppm-2000 ppm, and 2000 ppm-5000 ppm. Other embodiments may have an audible alarm, an inaudible alarm, color alert, or other visualization when the skin emanated VOC patterns are detected (Liu par. 55).
Therefore, it would have been obviously to one of ordinary skill in the art to substitute the VOC sensor as taught by Liu reference into the modified sensor system of Chiu and Jakobsson reference. The result of the substitution would be predictable for the system to classify VOC data to detect the skin emanated VOC patterns.
Regarding claim 8, the combination of Chiu, Jakobsson and Liu disclose The alerting device according to claim 1, wherein the one or more pre-defined pattern profiles includes one or more pre-defined pattern profiles selected from the group consisting of humidity pattern profiles, temperature pattern profiles, CO2 pattern profiles, light pattern profiles, water flow pattern profiles, and combinations thereof.
Such information is very important to derive and act on, and accordingly, the system determines events that are likely to match such situations or needs, based on the sensor output profiles observed by the system. These profiles can be in the time range, for example, as in a typical reporting of sensor output values, or in the frequency range, for example, as in the reporting of a Fast Fourier Transform (FFT). The use of correlation between outputs is useful whether the values being processed are in the time or frequency range. Correlation between multiple types of sensor outputs is beneficial to obtain derived sensor profile data taking multiple dimensions of sensor data into consideration (Jakobsson par. 30). The collection of sensors 100 comprises one or more sensor units, where example sensor units include a sound sensor 101, a camera 102, a motion sensor 103, a radio unit 104 that may use WiFi, Bluetooth, Bluetooth low energy (BLE), near-field communication (NFC), ZigBee, and other types of radio; and which may comprise one or more of such units mounted on one or more separate boards associated with the collection of sensors 100. Additional sensors of the collection of sensors 100 comprise a humidity sensor 105, a pressure sensor 106, an inertial sensor 107, a carbon monoxide (CO) sensor 108, a tamper sensor 109 that detects that one or more of the sensors associated with the collection of sensors 100 is physically manipulated, or that the conveyance of the signals from the collection of sensors 100 is physically manipulated. The collection of sensors 100 further comprises a temperature sensor 110 and an energy sensor 112. The energy sensor 112 is configured to detect energy consumption or other types of energy use. Also included in the collection of sensors 100 is a user GPS sensor 111. Such a sensor is illustratively accessible via an API, and in some embodiments comprises a device that has GPS functionality and is associated with a user of the system. An example user GPS sensor 111 is the GPS unit of a handheld device such as a phone, which is accessed by the system via an API to access the location of the user. Although single instances of these and other sensors are shown in the collection of sensors 100, there can be multiple instances of one or more of the sensors, as well as multiple collections of different sensors, in other embodiments (Jakobsson par. 99). A room can also be determined to be a kitchen 128 by being used in a manner consistent with kitchen use. This includes being used soon after a user leaves the bedroom in the morning (for breakfast); for having sound profiles from sound sensor 101 indicative of a kitchen (the humming of a dishwasher or the gurgling of a coffee maker); heat profiles from temperature sensor 110 indicative of the use of a stove or oven; the energy consumption profile from an energy sensor 112 matching the operation of a microwave; a refrigerator; a dishwasher, etc.; the correlation between such inputs (e.g., sound and energy consumption, as described above); and possibly others. Additional sensors such as humidity sensor 105 are also used to determine cooking, and is correlated with heat detected by temperature sensor 110. A room that is identified as a likely kitchen 128 faces a different use and risk profile than other rooms, and the system adapts its responses to observed events in the context of knowing the likely room type (Jakobsson par. 119).
VOC pattern profiles,
In certain embodiments, the inlet on the device is covered by a membrane that is waterproof and/or breathable. The sensor array contains multiple VOC (voltatile organic compound) sensors that react to VOCs and produce signals when exposed to gases, vapors, or odor containing VOCs released from a subject as well as one or more physiological sensors. The device can integrate with multiple commercially available physiological sensors for monitoring vital signals. Such vital signals may include heart rate, pulse rate, respiratory rate, blood oxygen saturation, blood pressure, hydration level, stress, position & balance, body strain, neurological function, brain activity, blood pressure, cranial pressure, auscultatory information, skin and body temperature, sleep, cholesterol, lipids, blood panel, body fat density, muscle density. Additional sensors may be installed that monitor environment conditions, such as temperature, humidity, and pressure. The collected data can be used for pattern recognition and machine learning algorithms, which can be used for detection of diseases and perform other functions (Liu par. 44). Some embodiments of the device are capable to measure skin and body temperature (−15° C. to 45° C.), heart rate, humidity (0-99%), and a variety of concentrations of VOCs. The VOC detection limit may range from 0.1 ppb to 5000 ppm, e.g., 0.1 ppb-1 ppb, 1 ppb-5 ppb, 5 ppb-10 ppb, 10 ppb-50 ppb, 50 ppb-100 ppb, 100 ppb-200 ppb, 200 ppb-300 ppb, 300 ppb-500 ppb, 500 ppb-1 ppm, 1 ppm-2 ppm, 2 ppm-5 ppm, 5 ppm-10 ppm, 10 ppm-100 ppm, 100 ppm-200 ppm, 200 ppm-500 ppm, 500 ppm-1000 ppm, 1000 ppm-2000 ppm, and 2000 ppm-5000 ppm. Other embodiments may have an audible alarm, an inaudible alarm, color alert, or other visualization when the skin emanated VOC patterns are detected (Liu par. 55).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chiu US 20170172368 in view of Jakobsson US 20200106637 and further in view of Galburt US 20160240057.
Regarding claim 6, the combination of Chiu and Jakobsson teach all the limitation in the claim 1.
The combination of Chiu and Jakobsson do not explicitly teach The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient light sensoring means being adapted to continuously monitor real-time light.
Galburt teaches The alerting device according to claim 1, wherein the one or more ambient sensoring means comprises one or more ambient light sensoring means being adapted to continuously monitor real-time light. (Galburt US 20160240057 abstract; paragraphs [0019]-[0029]; [0038]-[0041]; [0050]-[0054]; [0083]; figures 1-12)
In addition to primary sensors, the present application can employ secondary sensors and output devices to take advantage of the presence of a complete networked controller, and provide useful additional functionality beyond the occupancy and behavior detection data received from the primary sensors. For example, some of this functionality can be related to environmental quality and some to emergency alerts for the occupants. The secondary sensors and output devices can include environment sensors and alerting components and systems. Example environmental sensors can detect, for example, air temperature, humidity and light levels. Various alerting components and systems can include strobes 108 (e.g., bright white), loudspeakers and an audio amplifier, such as for siren and speech alerts (not shown) (Galburt par. 23).
According to the cited passages and figures, examiner interpret the environment sensor as the light sensor for detecting light levels.
Therefore, it would have been obviously to one of ordinary skill in the art to substitute the environmental sensor including the light sensor as taught by Galburt reference into the modified sensor system of Chiu and Jakobsson reference. The result of the substitution would be predictable for user design desire. For example the light sensor help to improve safety and security like intrusion detection system (light turn on when detecting a presence of a subject).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Chiu US 20170172368 in view of Jakobsson US 20200106637 and further in view of Eckert et al. US 20220384047.
Regarding claim 16, the combination of Chiu and Jakobsson teach all the limitation in the claim 1.
The combination of Chiu and Jakobsson do not explicitly teach The alerting device according to claim 1, wherein the behavioral pattern of a person includes Activities of Daily Living (ADL).
Eckert et al. teach The alerting device according to claim 1, wherein the behavioral pattern of a person includes Activities of Daily Living (ADL). (Eckert et al. US 20220384047 abstract; paragraphs [0002]-[0008]; [0022]-[0028]; [0059]; [0069]; figures 1-12)
According to the present invention, techniques, including a method, and system, for processing signals using artificial intelligence techniques to monitor, detect, and act on activities are provided. In an example, the signals can be from both active and passive sensors, among others. Merely by way of examples, various applications can include daily life, and others (Eckert et al. par. 22). In an example, multiple aspects of antenna design can improve the performance of the activities of daily life (“ADL”) system. For example, in scanning mode the present technique continuously looks for moving human targets (or user) to extract ADL or fall. Since these can happen anywhere in the spatial region of a home, the present system has antennas that have wide field of view. Once the human target is identified, the technique focuses signals coming only from that particular target and attenuate returns from all other targets (Eckert et al. par. 25).
Therefore, it would have been obviously to one of ordinary skill in the art to substitute the technique continuously looks for moving human targets (or uer) to extract ADL (activities of daily life) as taught by Eckert et al. reference into the modified sensor system of Chiu and Jakobsson reference. The result of the substitution would be predictable for improve the safety of the user. For example, the system can send a notification to care taker or medical response when detecting something is abnormal (fall, shortness of breathing or other).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG D TRAN whose telephone number is (408)918-7546. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm (pacific time).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian A Zimmerman can be reached at 571-272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THANG D TRAN/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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