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 .
Status of Claims
The following is a non-final, first office action in response to the communication filed 02/27/2024. Claims 1-20 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claims 1-20 have support in PRO 63/448,354, and the instant application is entitled to the benefit of the provisional application, with an effective filing date of 02/27/2023.
The instant application is further a continuation-in-part of U.S. application Ser. No. 17/924,998 which was filed on November 13, 2022, as a U.S. National Phase Application under 35 U.S.C. 371 of International Application No. PCT/IB2021/054130, which has an international filing date of May 14, 2021, which claims the benefit of priority from U.S. Provisional Patent Application No. 63/024,520, filed May 14, 2020, U.S. Provisional Patent Application No. 63/042,023, filed June 22, 2020, and U.S. Provisional Patent Application No. 63/093,319, filed October 19, 2020. These earlier members of the patent family do not disclose “a beacon transmitter configured to transmit an activation beacon when a call event is detected”, and thus they do not provide support as required by 35 U.S.C. 112(a) for claims 1-20 of the instant application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/02/2022 and 05/17/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Specification
The disclosure is objected to because paragraph [0063] appears to have multiple inconsistencies regarding the numbers referencing Fig. 1B:
Line 4 states: “The telemedical monitoring device 104 is shown here connected to a weight measuring unit 214A and a blood pressure monitoring unit 214B”. No labels 214A or 214B appear in Fig. 1B or any other figure. The intended numbers appear to be 144A and 144B.
Lines 5-6 state: “The units 102A and 102B measure the weight and blood pressure…” However, 102A and 102B are used to refer to patients in Fig. 1A, not to monitoring units. It appears that again the intended numbers were 144A and 144B.
Page 17, line 4 states: “The weight measuring unit 136A…” It again appears that the intended numbering for the weight measuring unit is 144A.
Appropriate correction is required.
Claim Objections
Applicant is advised that should claims 5 and 6 be found allowable, claims 9 and 10 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). It appears that claims 9 and 10 were intended to depend on claim 8 rather than on claim 1 as currently written.
Claim 2 is objected to because of the following informalities:
The “said at least one communication device” in line 1 was previously in claim 1 referred to as “the at least one ancillary communication device”. The word “ancillary” should be likewise included in claim 2 for consistency and clarity.
Line 2 recites “a wearable devices”. The word “a” should be removed for grammatical correctness.
Claim 7 is objected to because of the following informalities:
In lines 1-2, in “a real-time locating system signals”, the word “signal” should be singular.
In line 2, “on” should be “one” in the phrase “at least on ancillary communication device”.
Claim 11 is objected to because of the following informalities: on page 3, lines 8-10, the punctuation makes it difficult to parse that the actions of “detecting” and “generating” are performed by the processor. Examiner suggests replacing the semi-colon at the end of lines 8 and 9 with a comma, adding the word “and” to the end of line 9, and adding a semi-colon to the end of line 10 “generating a fall alert”.
Claim 19 is objected to because of the following informalities:
In line 2, “comprises at least one of” is followed by a semi-colon and should be instead followed by a colon.
On page 5, line 3, the word “and” should be deleted in the phrase “determining and a signal to noise ratio”.
Claim 20 is objected to because of the following informalities: “the communication device” of line 4 should be referred to as the “the ancillary communication device” for consistency and clarity, as discussed regarding claim 2.
Appropriate correction is required.
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 1-20 are 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.
Claim 1 recites “raw data” in line 8. It is unclear whether this raw data is the same or different as the “raw data” recited in line 7. For purposes of examination, this phrase in line 8 will be read as “the raw data”.
Claim 8 recites “raw data” in line 6. It is unclear whether this raw data is the same or different as the “raw data” recited in line 5. For purposes of examination, this phrase in line 6 will be read as “the raw data”.
Claim 11 recites “at least one processor” in line 5 as part of the radar monitor, and also “at least one processor” on page 3, line 2, as part of the ancillary communication device. Lines 7 and 8 of page 3 recite “the processor”, and it is not clear to which of the previously-introduced processors these lines refer, rendering the claim indefinite. For purposes of examination, the processor will be read as belonging to the radar monitor.
Claim 11 recites the limitation "the monitoring software application" on page 3, lines 13-14. There is insufficient antecedent basis for this limitation in the claim, as a monitoring software application has not been previously introduced. For purposes of examination, “the monitoring software application” will be read as “a monitoring software application”.
Claims 2-7, 9-10 and 12-20 are also rejected since the claims are dependent on a previously rejected claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zack et al. (US-20160377705; hereinafter Zack) in view of Nhu (US-20160165387-A1; hereinafter Nhu).
Regarding claim 1, Zack discloses [note: what Zack fails to disclose is strike-through]
A radar-based telemedical monitoring system (see at Abs; “A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition.”) comprising at least one radar monitor (see at least Fig. 1C, UWB-RF interferometry unit 220) and at least one (see at least Fig. 1C, communication system 150) wherein:
at least one radar unit (see at least Fig. 1C, UWB-RF interferometry unit 220) comprising at least one transmitter antenna (see at least Fig. 1C, transmitting antennas 101) connected to an oscillator (see at least Fig. 1C, pulse generator 221) and configured to transmit electromagnetic waves towards a target region (see at least [0060]; “…UWB transmitting antennas 101 that deliver a UWB RF signal 91 to an environment 80, e.g., one including at least one human 90…”), and at least one receiver antenna (see at least Fig. 1C, receiver antennas 110) configured to receive electromagnetic waves reflected by a subject located within the target region and operable to generate raw data (see at least [0060]; “UWB receiver antennas 110 that receive echo signals 99 from the scene and UWB RF interferometer 120 that processes the received echo signals and provide signals for extraction of multiple features, as explained below.”);
at least one processor (see at least Fig. 1C, processing unit 225, features extractor 240, and cognitive situation analysis module 260) configured to receive raw data from the radar unit (see at least [0062]; “Environmental clutter cancelation 230 may be part of a processing unit 225 as illustrated and/or may be part of UWB-RF interferometry unit 220, e.g., clutter cancelation may be at least partially carried out by a Rx path pre-processing unit 222. The echo signals are pre-processed to reduce the environmental clutter (the unwanted reflected echo components that are arrived from the home walls, furniture, etc.). The output signal mostly contains only the echo components that reflected back from the monitored human body.”) and operable to detect fall events (see at least [0066]; “Cognitive Situation Analysis (CSA) module 260—This unit recognizes whether the monitored person is in an emergency or abnormal situation. This unit is based on a pattern recognition engine (e.g., Hidden Markov Model—HMM, based). The instantaneous states with their probabilities are streamed in and the CSA search for states patterns that are tagged as emergency or abnormal patterns, such as a fall. These predefined patterns are stored in a patterns codebook”); and
at least one communication manager (see at least Fig. 1C, communication system 150) configured and operable to communicate with third parties (see at least [0059]; “Communication system 150 may further include two-way communication system between the caregiver and the monitored person for real-time assistance.”); and
the at least one (see at least [0125]; “The communication unit provides the following functionalities: (i) This unit transmits any required ongoing situation of the monitored person and emergency alerts. (ii) It enables the two way voice/video communication with the monitored person when necessary. Such a communication is activated either automatically whenever the system recognizes an emergency situation or remotely by the caregiver.”).
However, Zack does not teach a communication device separate from the communication manager, nor does Zach teach the use of beacons for communicating between the radar monitor and the communication device, or the sending of a beacon when a fall is detected. Furthermore, although Zack teaches that the communication unit can enable two-way voice communication, Zack does not explicitly teach that the communication unit includes a microphone, speaker, and processor.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Nhu is directed to a smart home system that may include a fall detection sensor. Nhu teaches:
A telemedical monitoring system (see at least Fig. 1, system 302 for a smart home platform with data analytics for home monitoring) comprising:
at least one processor configured to receive data and operable to detect fall events (see at least [0037]; “In case of a slip and fall as detected by the 3-axis accelerometer 208 and processed by a programmable firmware algorithm, described further below, the push notification or the call-center dialing can be activated automatically, which can be useful just before the user loses consciousness or when the user loses consciousness and the button is pushed by another”); and
at least one communication manager configured and operable to communicate with third parties (see again [0037], quoted above); and
at least one beacon transmitter configured to transmit an activation beacon when a fall event is detected (see at least [0035]; “Via a firmware algorithm running on a microcontroller in a BLE module 204 (FIG. 3A) that reads input data from accelerometer 208 and/or a button/switch 214, this fall detection apparatus 201 can activate the Beacon signal with different flags indicating different scenarios for a nearby BLE Mesh repeater to forward these events along with the repeater ID. The repeater ID can be linked to a location, such as Bedroom #1, and when this repeater ID is received by the Cloud via the gateway, the exact or precise location of the fall can be detected or determined.”); and
the at least one ancillary communication device comprises at least one microphone, at least one speaker (see at least [0010]; “Microphone and speaker in the wifi gateway unit can be include to interact with voice recognition services and/or devices.”), at least one processor (see at least [0021]; “A gateway B1 (FIG. 1) comprising a housing having a PCB with a Wifi module and a BLE module can be positioned or mounted to a bed at 316, for example to the bed frame. The Wifi gateway B1 can be connected to the home Wifi router and the BLE hardware module on the gateway B1 can run a mesh-networking firmware to communicate with surrounding BLE devices and nodes, as further discussed below.”), and at least one beacon receiver configured to receive the activation beacon from the at least one monitor (see at least [0026]; “The present system allows a BLE beacon signal from a B sensor 110 to be detected by a BLE Mesh repeater 108 (FIG. 2) or by a BLE node 106 in the gateway 104. If the information is first received by a BLE Mesh repeater node 108, it can then relay the information to one or more other repeater nodes 108, depending on the distance, then eventually to the gateway's BLE node 106, which then forwards the information to the Cloud server 308 via Wifi. If the information is received by the gateway's BLE node 106 directly, the gateway then forwards the information to the Cloud server via Wifi without going through a repeater node.”) and to open a communication channel to a third party (see at least [0042]; “At step 248, the microcontroller in the FD device's BLE module 204 determines the fall event by continuously reading the Y-axis values over a period of 4 seconds and checking if the values are all in the range between −409 to +409 counts…When the 7-second timeout is reached, at step 254, the Flag of the Beacon message is set to FALL. This Flag code can be received by the BLE repeaters 108 (FIG. 2) and forwarded to the cloud server via the gateway 104. At step 246, the cloud server decodes the Flag and sends a FALL alert via push notification, SMS, or phone dialing.”).
Both Zack and Nhu teach systems that use sensors to detect falls and subsequently open communication channels to caregivers or emergency responders. The system of Zack has an integrated communication manager able to call caregivers, while the system of Nhu transmits beacons through a BLE mesh network from the fall-sensing device to a separate device able to call a caregiver. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system used in Zack to include integration with a BLE mesh network that communicates using beacons, as taught by Nhu. Such a change would allow the flexibility to separate the device that detects the fall and the device that calls for help, and it would also enable integration of the fall detection device with other assistive devices, as taught by Nhu (see Nhu at least [0003] – [0007]). Modifying the system of Zack to communicate with a separated communication device through a BLE mesh network would represent a combination of prior art elements according to known methods to yield predictable results.
Regarding claim 2, Zack in view of Nhu teaches the system of claim 1. Nhu further teaches:
wherein said at least one communication device is selected from a group consisting of telephones (Nhu teaches that the functions of the gateway may alternatively be performed by a smartphone, see at least [0009], [0024], [0031], [0047] – [0049], and [0058]), a wearable devices, watches, bracelets, pendants and combinations thereof.
It would have been obvious to combine Zack and Nhu for the reasons given regarding claim 1.
Regarding claim 3, Zack in view of Nhu teaches the system of claim 1. Nhu further teaches:
wherein the at least one beacon transmitter comprises a low power signal (see at least [0001]; “The disclosure is directed to a BLE (Bluetooth Low Energy) Mesh/Beacon-based hardware/firmware system such as an embedded system platform with dedicated gateways running BLE Mesh networking firmware…”).
Regarding claim 4, Zack in view of Nhu teaches the system of claim 1. Nhu further teaches:
wherein the at least one beacon transmitter comprises a Bluetooth Low Energy (BLE) beacon signal (see at least [0001]; “The disclosure is directed to a BLE (Bluetooth Low Energy) Mesh/Beacon-based hardware/firmware system such as an embedded system platform with dedicated gateways running BLE Mesh networking firmware…”).
Regarding claim 5, Zack in view of Nhu teaches the system of claim 1. Zack further teaches:
further comprising at least one fall identification module configured to identify a fall event if the raw data indicates a likelihood score above an alert-threshold value (see at least [0094]; “System 100 may further comprise human state classifier 250 configured to classify the motion and movement characteristics of the at least one human to indicate a state of the at least one human, and abnormality situation pattern recognition module 262, e.g., as part of cognitive situation analysis module 260 configured to generate an alert once the indicated state is related to at least one specified emergency. The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” Examiner maps the threshold to the probability of the other predefined state or states. See also Fig. 4 showing exemplary states including falling, and see [0120]; “The classifier output is the determined states and the set of the measured statistical distances (probabilities), i.e., the probability of State-i given the observation-O (the features vector).”).
Regarding claim 6, Zack in view of Nhu teaches the system of claim 1. Zack further teaches:
further comprising a fall alert mitigation manager (see at least Fig. 1C, Cognitive Situation Analysis Module 260) configured to prevent false positives by analyzing additional mitigation features selected to distinguish real falls from other fall-like detections (see at least [0123]; “FIG. 6 is a table 136 illustrating exemplary abnormal patterns in accordance with some embodiments of the present invention. It can be seen that in the first abnormal case (Critical fall), it appears that the person was sleeping in the leaving room (S25), then was standing (S45) and immediately fell down (S65). He stayed on floor (S15) and start being in stress due to high respiration rate (S75). The CSA may contain additional codebook (irrelevant codebook) to identify irrelevant patterns that might mislead the system decision.”).
Regarding claim 8, Zack discloses [note, what Zack fails to disclose is strike-through]:
A radar-based telemedical monitoring system (see at Abs; “A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition.”) comprising
at least one radar unit (see at least Fig. 1C, UWB-RF interferometry unit 220) comprising at least one transmitter antenna (see at least Fig. 1C, transmitting antennas 101) connected to an oscillator (see at least Fig. 1C, pulse generator 221) and configured to transmit electromagnetic waves towards a target region (see at least [0060]; “…UWB transmitting antennas 101 that deliver a UWB RF signal 91 to an environment 80, e.g., one including at least one human 90…”), and at least one receiver antenna (see at least Fig. 1C, receiver antennas 110) configured to receive electromagnetic waves reflected by a subject located within the target region and operable to generate raw data (see at least [0060]; “UWB receiver antennas 110 that receive echo signals 99 from the scene and UWB RF interferometer 120 that processes the received echo signals and provide signals for extraction of multiple features, as explained below.”);
at least one processor (see at least Fig. 1C, processing unit 225, features extractor 240, and cognitive situation analysis module 260) configured to receive raw data from the radar unit (see at least [0062]; “Environmental clutter cancelation 230 may be part of a processing unit 225 as illustrated and/or may be part of UWB-RF interferometry unit 220, e.g., clutter cancelation may be at least partially carried out by a Rx path pre-processing unit 222. The echo signals are pre-processed to reduce the environmental clutter (the unwanted reflected echo components that are arrived from the home walls, furniture, etc.). The output signal mostly contains only the echo components that reflected back from the monitored human body.”) and operable to detect fall events (see at least [0066]; “Cognitive Situation Analysis (CSA) module 260—This unit recognizes whether the monitored person is in an emergency or abnormal situation. This unit is based on a pattern recognition engine (e.g., Hidden Markov Model—HMM, based). The instantaneous states with their probabilities are streamed in and the CSA search for states patterns that are tagged as emergency or abnormal patterns, such as a fall. These predefined patterns are stored in a patterns codebook”); and
at least one communication manager (see at least Fig. 1C, communication system 150) configured and operable to communicate with third parties (see at least [0059]; “Communication system 150 may further include two-way communication system between the caregiver and the monitored person for real-time assistance.”); and
However, Zack does not teach at least one beacon transmitter configured to transmit an activation beacon when a fall event is detected.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Nhu is directed to a smart home system that may include a fall detection sensor. Nhu teaches:
A telemedical monitoring system (see at least Fig. 1, system 302 for a smart home platform with data analytics for home monitoring) comprising:
at least one processor configured to receive data and operable to detect fall events (see at least [0037]; “In case of a slip and fall as detected by the 3-axis accelerometer 208 and processed by a programmable firmware algorithm, described further below, the push notification or the call-center dialing can be activated automatically, which can be useful just before the user loses consciousness or when the user loses consciousness and the button is pushed by another.”); and
at least one communication manager configured and operable to communicate with third parties (see at least Fig. 3B, which provides a flowchart of the fall detection firmware algorithm and the ways beacons may be used to communicate with 3rd party cell and SMS); and
at least one beacon transmitter configured to transmit an activation beacon when a fall event is detected (see at least [0035]; “Via a firmware algorithm running on a microcontroller in a BLE module 204 (FIG. 3A) that reads input data from accelerometer 208 and/or a button/switch 214, this fall detection apparatus 201 can activate the Beacon signal with different flags indicating different scenarios for a nearby BLE Mesh repeater to forward these events along with the repeater ID. The repeater ID can be linked to a location, such as Bedroom #1, and when this repeater ID is received by the Cloud via the gateway, the exact or precise location of the fall can be detected or determined.”).
Both Zack and Nhu teach systems that use sensors to detect falls and subsequently open communication channels to caregivers or emergency responders. The system of Zack has an integrated communication manager able to call caregivers, while the system of Nhu transmits beacons through a BLE mesh network from the fall-sensing device to a separate device able to call a caregiver. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system used in Zack to include integration with a BLE mesh network that communicates using beacons, as taught by Nhu. Such a change would allow the flexibility to separate the device that detects the fall and the device that calls for help, and it would also enable integration of the fall detection device with other assistive devices, as taught by Nhu (see Nhu at least [0003] – [0007]). Modifying the system of Zack to communicate with a separated communication device through a BLE mesh network would represent a combination of prior art elements according to known methods to yield predictable results.
Regarding claim 9, Zack in view of Nhu teaches the system of claim 1. Zack further teaches:
further comprising at least one fall identification module configured to identify a fall event if the raw data indicates a likelihood score above an alert-threshold value (see at least [0094]; “System 100 may further comprise human state classifier 250 configured to classify the motion and movement characteristics of the at least one human to indicate a state of the at least one human, and abnormality situation pattern recognition module 262, e.g., as part of cognitive situation analysis module 260 configured to generate an alert once the indicated state is related to at least one specified emergency. The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” Examiner maps the threshold to the probability of the other predefined state or states. See also Fig. 4 showing exemplary states and [0120]; “The classifier output is the determined states and the set of the measured statistical distances (probabilities), i.e., the probability of State-i given the observation-O (the features vector).”).
Regarding claim 10, Zack in view of Nhu teaches the system of claim 1. Zack further teaches:
further comprising a fall alert mitigation manager (see at least Fig. 1C, Cognitive Situation Analysis Module 260) configured to prevent false positives by analyzing additional mitigation features selected to distinguish real falls from other fall-like detections (see at least [0123]; “FIG. 6 is a table 136 illustrating exemplary abnormal patterns in accordance with some embodiments of the present invention. It can be seen that in the first abnormal case (Critical fall), it appears that the person was sleeping in the leaving room (S25), then was standing (S45) and immediately fell down (S65). He stayed on floor (S15) and start being in stress due to high respiration rate (S75). The CSA may contain additional codebook (irrelevant codebook) to identify irrelevant patterns that might mislead the system decision.”).
Regarding claim 11, Zack discloses [note: what Zack fails to disclose is strike-through]
A method for establishing a communication channel between at least one monitored subject and at least one third party (see at least [0125]; “The communication unit provides the following functionalities: (i) This unit transmits any required ongoing situation of the monitored person and emergency alerts. (ii) It enables the two way voice/video communication with the monitored person when necessary. Such a communication is activated either automatically whenever the system recognizes an emergency situation or remotely by the caregiver.”) when the at least one monitored subject falls (see at least [0122]; “The CSA's objective is to recognize the abnormal human patterns according to a trained model that contains the possible abnormal cases (e.g., fall).”), the method comprising:
providing a radar monitor (see at least Fig. 1C, system 200) comprising at least one radar unit (see at least Fig. 1C, UWB-RF interferometry unit 220) having at least one transmitter antenna (see at least Fig. 1C, transmitting antennas 101) connected to an oscillator (see at least Fig. 1C, pulse generator 221), at least one receiver antenna configured to receive electromagnetic waves (see at least [0060]; “UWB receiver antennas 110 that receive echo signals 99 from the scene and UWB RF interferometer 120 that processes the received echo signals and provide signals for extraction of multiple features, as explained below.”), at least one processor (see at least Fig. 1C, processing unit 225, features extractor 240, and cognitive situation analysis module 260); and at least one communication manager (see at least Fig. 1C, communication system 150);
providing at least one (see at least [0059]; “Communication system 150 may further include two-way communication system between the caregiver and the monitored person for real-time assistance.”)
the at least one transmitter antenna transmitting radio frequency signals into a monitored region (see at least [0060]; “…UWB transmitting antennas 101 that deliver a UWB RF signal 91 to an environment 80, e.g., one including at least one human 90…”);
the at least one receiving antenna receiving radio frequency signals reflected back from objects in the monitored region (see at least [0060]; “UWB receiver antennas 110 that receive echo signals 99 from the scene and UWB RF interferometer 120 that processes the received echo signals and provide signals for extraction of multiple features, as explained below.”);
the processor receiving raw data from the at least one receiving antenna (see at least Fig. 1C, path from receive antennas 110 to processing units 222, 225, 240, 250, 260);
the processor analyzing the raw data (see at least [0062]; “Environmental clutter cancelation 230 may be part of a processing unit 225 as illustrated and/or may be part of UWB-RF interferometry unit 220, e.g., clutter cancelation may be at least partially carried out by a Rx path pre-processing unit 222. The echo signals are pre-processed to reduce the environmental clutter (the unwanted reflected echo components that are arrived from the home walls, furniture, etc.). The output signal mostly contains only the echo components that reflected back from the monitored human body.”),
detecting a fall event in the raw data (see at least [0066]; “Cognitive Situation Analysis (CSA) module 260—This unit recognizes whether the monitored person is in an emergency or abnormal situation. This unit is based on a pattern recognition engine (e.g., Hidden Markov Model—HMM, based). The instantaneous states with their probabilities are streamed in and the CSA search for states patterns that are tagged as emergency or abnormal patterns, such as a fall. These predefined patterns are stored in a patterns codebook”), and
generating a fall alert (see at least [0066]; “In case that CSA recognizes such a pattern, it will send an alarm notification to the healthcare center or family care giver through the communication unit (e.g., Wi-Fi or cellular).”);
the at least one (see at least [0066]; “Two-way voice/video communication unit 150—this unit may be activated by the remote caregiver to communicate with the monitored person when necessary.” See also [0125]; “Such a communication is activated either automatically whenever the system recognizes an emergency situation or remotely by the caregiver.”);
the at least one (see at least [0125]; “The communication unit provides the following functionalities: (i) This unit transmits any required ongoing situation of the monitored person and emergency alerts. (ii) It enables the two way voice/video communication with the monitored person when necessary. Such a communication is activated either automatically whenever the system recognizes an emergency situation or remotely by the caregiver.”).
However, Zack does not teach a communication device separate from the communication manager, nor does Zach teach the use of beacons for communicating between the radar monitor and the communication device, or the sending of a beacon when a fall is detected. Furthermore, although Zack teaches, that the communication unit can enable two-way voice communication, Zack does not explicitly teach that the communication unit includes a microphone, speaker, and processor.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Nhu is directed to a smart home system that may include a fall detection sensor. Nhu teaches:
A method for establishing a communication channel between at least one monitored subject and at least one third party when the at least one monitored subject falls (see at least Fig. 3B, method 200 that includes detecting falls (204) and contacting a 3rd party (246)), the method comprising:
providing a monitor (see at least Fig. 3A, fall detection device 201 with 3-axis accelerometer 208), at least one processor (see at least [0035]; “Via a firmware algorithm running on a microcontroller in a BLE module 204 (FIG. 3A) that reads input data from accelerometer 208 and/or a button/switch 214, this fall detection apparatus 201 can activate the Beacon signal with different flags indicating different scenarios for a nearby BLE Mesh repeater to forward these events along with the repeater ID.”); and at least one communication manager (see at least Fig. 3B, flowchart of fall detection firmware algorithm);
providing at least one ancillary communication device comprising at least one microphone, at least one speaker (see at least [0010]; “Microphone and speaker in the wifi gateway unit can be include to interact with voice recognition services and/or devices.”), and at least one processor (see at least [0021]; “A gateway B1 (FIG. 1) comprising a housing having a PCB with a Wifi module and a BLE module can be positioned or mounted to a bed at 316, for example to the bed frame. The Wifi gateway B1 can be connected to the home Wifi router and the BLE hardware module on the gateway B1 can run a mesh-networking firmware to communicate with surrounding BLE devices and nodes, as further discussed below.”);
detecting a fall event in the raw data (see at least [0034]; “In a 13-bit accelerometer sensor, in the vertical position, the Y-axis value is near the max value of −4096 or +4095. In the horizontal position when the person falls face up or down, or sideway left or right, this Y value is near a zero 0 value. Different fall events can be tested and analyzed and the fall threshold can be empirically discover and programmed. In an example, the Y-axis value can fall within the range from −10% to +10% of the max value possible for a particular N-bit accelerometer chip. For the 13-bit accelerometer example, this range is between about −409 to +409 counts. When the Y value stays within this range for at least 4 seconds, a valid fall event can be assumed or flagged, otherwise any variation in values can be treated as a non-fall event. This range and the 4-second duration can be programmed to different values for different detection sensitivity and system responsiveness.”);
the monitor transmitting a beacon signal into the monitored region (see at least [0035]; “Via a firmware algorithm running on a microcontroller in a BLE module 204 (FIG. 3A) that reads input data from accelerometer 208 and/or a button/switch 214, this fall detection apparatus 201 can activate the Beacon signal with different flags indicating different scenarios for a nearby BLE Mesh repeater to forward these events along with the repeater ID. The repeater ID can be linked to a location, such as Bedroom #1, and when this repeater ID is received by the Cloud via the gateway, the exact or precise location of the fall can be detected or determined.”);
the at least one ancillary communication device receiving the beacon signal (see at least [0026]; “The present system allows a BLE beacon signal from a B sensor 110 to be detected by a BLE Mesh repeater 108 (FIG. 2) or by a BLE node 106 in the gateway 104. If the information is first received by a BLE Mesh repeater node 108, it can then relay the information to one or more other repeater nodes 108, depending on the distance, then eventually to the gateway's BLE node 106, which then forwards the information to the Cloud server 308 via Wifi. If the information is received by the gateway's BLE node 106 directly, the gateway then forwards the information to the Cloud server via Wifi without going through a repeater node.”);
the at least one ancillary communication device activating the monitoring software application (see at least [0022]; “This BLE range limitation can be enhanced or improved, such as to expand the range thereof, by using the low-cost embedded gateway B1 at block 316 and running BLE mesh networking software in or on the gateway board together with a network of BLE Mesh repeater nodes MN1 through MN6 at blocks 304 and 314 of FIG. 1.”);
the at least one ancillary communication device opening the at least one microphone and the at least one speaker (see at least [0058]; “When the voice-based feature is incorporated into the gateway B1 316, a user can perform voice-controlled home automation. For example, the user can issue a voice command that is picked up by the gateway's microphone, the captured audio is sent to the Alexa AVS Cloud A1 328 speech recognition to convert voice to text command which is then processed by the Cloud server 308 and its database. The Cloud server 308 then sends messages to the gateway 316 to relay to the particular node to carry out the instructions…In another example, upon the smart bottle dispenser's open event, BLE messages can be sent to the gateway 316 or a smartphone 306 to read the medicine information out loud, warn if the medicine is approaching its expiration date, or state which patient the medicine is for.”); and
the at least one ancillary communication device establishing a two way audio communication channel between the at least one monitored subject and the at least one third party (see at least [0042]; “At step 246, the cloud server decodes the Flag and sends a FALL alert via push notification, SMS, or phone dialing.”).
Both Zack and Nhu teach systems that use sensors to detect falls and subsequently open communication channels to caregivers or emergency responders. The system of Zack has an integrated communication manager able to call caregivers, while the system of Nhu transmits beacons through a BLE mesh network from the fall-sensing device to a separate device able to call a caregiver. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system used in Zack to include integration with a BLE mesh network that communicates using beacons, as taught by Nhu. Such a change would allow the flexibility to separate the device that detects the fall and the device that calls for help, and it would also enable integration of the fall detection device with other assistive devices, as taught by Nhu (see Nhu at least [0003] – [0007]). Modifying the system of Zack to communicate with a separated communication device through a BLE mesh network would represent a combination of prior art elements according to known methods to yield predictable results.
Regarding claim 13, Zack in view of Nhu teaches the method of claim 11. Zack further teaches:
collecting frame data from the radar (see at least Fig. 3A, “UWB Echo Signal” 401 and corresponding description in [0096]);
analyzing the frame data (see at least Fig. 3A, Echo signal preprocessing unit 405, Echo signals rearrangement 410, Motion Doppler Features 420A and Motion Range of Time Features 420B, which output to the Motion Features Vector 440);
detecting a fall event in at least one frame of the frame data (see at least [0120]; “he Human state classifier is a VQ (Vector Quantization) based classifier…Classifying phase—it's executed during the online operation while an unknown features vector is entered into the classifier and the classifier determines what the most probable state that it represents.” See also Fig. 4 showing exemplary states, including falling.);
verifying the fall event (see at least [0094]; “System 100 may further comprise human state classifier 250 configured to classify the motion and movement characteristics of the at least one human to indicate a state of the at least one human, and abnormality situation pattern recognition module 262, e.g., as part of cognitive situation analysis module 260 configured to generate an alert once the indicated state is related to at least one specified emergency. The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” See also [0123]; “The CSA may contain additional codebook (irrelevant codebook) to identify irrelevant patterns that might mislead the system decision.”); and
generating fall alert only if the fall event is verified (see at least [0066]; “In case that CSA recognizes such a pattern, it will send an alarm notification to the healthcare center or family care giver through the communication unit (e.g., Wi-Fi or cellular).”).
Regarding claim 18, Zack in view of Nhu teaches the method of claim 13. Zack further teaches:
wherein the step of verifying the fall event in a frame comprises:
arraying height profiles into a height profile map (see at least [0088]; “The following is the used procedure to find the human position and posture: Dividing the surveillance space into voxels (small cubic) in cross range, down range and height; Estimating the reflected EM signal from a specific voxel by the back projection algorithm; Estimating the human position by averaging the coordinates of the human reflecting voxels for each baseline (Synthetic Aperture Antenna Array); Triangulating all baselines' position to generate the human position in the environment; Estimating the human posture by mapping the human related high-power voxels into the form-factor vector; and Tracking the human movements in the environment (bedroom, restroom, etc.)”);
extracting at least one input parameter from the height profile map (see at least [0099]; “Feature extraction 241 may be separated into two components—motion Doppler characteristics derivation 420A (motion Doppler features) and motion change over range bins and time characteristics derivation 420B (motion energy features). Motion features extraction 241 yields a motion features vector 440 which is then used for further processing and classification in classifiers 130 and/or 250.”);
inputting at least one input parameter into a neural network (see at least [0120]; “The Human state classifier is a VQ (Vector Quantization) based classifier… Classifying phase—it's executed during the online operation while an unknown features vector is entered into the classifier and the classifier determines what the most probable state that it represents.” Examiner notes that a VQ classifier is a type of neural network.);
the neural network generating a fall prediction value (PV) (see at least [0120]; “The classifier output is the determined states and the set of the measured statistical distances (probabilities), i.e., the probability of State-i given the observation-O (the features vector).” Fig. 4 shows that “Falling” is one of the VQ states.); and
determining if the prediction value (PV) is above a threshold value (PVth) (see at least [0094]; “System 100 may further comprise human state classifier 250 configured to classify the motion and movement characteristics of the at least one human to indicate a state of the at least one human, and abnormality situation pattern recognition module 262, e.g., as part of cognitive situation analysis module 260 configured to generate an alert once the indicated state is related to at least one specified emergency. The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” Examiner maps the threshold to the probability of the other predefined state or states.).
Regarding claim 19, Zack in view of Nhu teaches the method of claim 18. Zack further teaches:
wherein the step of extracting at least one input parameter from the height profile map comprises at least one of:
determining therefrom a low energy (LE) index;
determining a high energy (HE) index;
determining a height of low energy (HoLE) index;
determining a dynamic consistency (DynC) index;
determining and a signal to noise ratio (SNR) index;
tracing a target during the frame (see at least [0087]; “The “cleaned” echo signal vectors are used as the raw data for the features extraction unit. This unit extracts the features that mostly describe the instantaneous state of the monitored person… Posture—the person posture (sitting, standing, and laying) will be extracted by creating the person “image” by using, e.g., a back-projection algorithm. Both position and posture are extracted, for example, by operating, e.g., the Back-projection algorithm on received echo signals—as acquired from the multiple antennas array in SAR operational mode.”);
plotting height coordinates of the target during the frame; and
determining a maximum height (Z) jump index.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zack in view of Nhu, further in view of Soaz González (EP-3796282-A2; hereinafter Soaz González).
Regarding claim 7, Zack in view of Nhu teaches the system of claim 6. However, Zack does not teach:
further wherein at least one additional mitigation feature comprises a real-time locating system signals (RTLS signal) from the at least on ancillary communication device.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Soaz González is directed to a fall detection device. Soaz González teaches:
further wherein at least one additional mitigation feature comprises (see at least [0062] – [0063]; “Additionally or alternatively, determining the device status may be part of the fall detection algorithm…In some embodiments, the fall detection algorithm may comprise at least one or a plurality of one or more steps of comparing at least a subset of the collected sensor data to pre-defined thresholds, and one or more steps of classifying a feature set and/or raw data derived from at least a subset of the collected sensor data, to determine if a fall has happened or not. The step of classifying a feature set may be based on statistical or machine learning methods.”) a real-time locating system signal (RTLS signal) (see at least [0141]; “Generally, the position detection may be supported by the cellular network and/or WLAN, as well as other signals that may enable localisation. For example, cellular networks may enable for mobile phone tracking, e.g. through localization based on the global system for mobile communications (GSM). Further, if a WLAN is present IP localization or a Wi-Fi positioning system may provide means to localise the fall detection device. Such techniques may be combined with the position sensor to allow for a localization indoors and outdoors and additionally may enable reduction of the power consumption of the device.”) from the at least one ancillary communication device (see at least Fig. 2, device 100 includes position sensor 133 performing the detection referenced in [0141] and communication unit 160).
Zack teaches performing fall detection using a radar device. Soaz González performs fall detection using inputs from multiple sensors, including radar (see [0049]) and position detection using real-time locating system signals (see [0141], quoted above). It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to modify the system of Zack to use inputs from sensors located on the user and also external sensors, including signals to enable localization. Doing so would advantageously provide a broader array of data from which to make a fall determination, as taught by Soaz González (see at least [0051]; “In another embodiment the present invention relates to a fall detection system for identifying a fall and triggering an alarm, the fall detection system comprising a fall detection device as described above. The fall detection system may further comprise at least one external sensor, configured to provide additional data, e.g. on the vital signs or weight of a user. In other words, a fall detection system may comprise a fall detection device and further at least one external sensor, e.g. a scale, an external heart rate monitor or a blood sugar sensor. This may be advantageous, as it may provide the system with sensor data of sensors that may for example not easily be incorporated in the fall detection device itself.”)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zack in view of Nhu, further in view of Kurfirst (US-11055981-B1; hereinafter Kurfirst).
Regarding claim 12, Zack in view of Nhu teaches the method of claim 11. Nhu further teaches:
further comprising:
(see at least [0021]; “The Wifi gateway B1 can be connected to the home Wifi router and the BLE hardware module on the gateway B1 can run a mesh-networking firmware to communicate with surrounding BLE devices and nodes, as further discussed below.” See also [0047]; “This entire process flow and features of the smart dispenser device can also be supported via a BLE-enabled smartphone and an app instead of the gateway B1 316 and its embedded firmware.”);
It would be obvious to combine Zack and Nhu for the reasons given regarding claim 11. However, neither Zack nor Nhu explicitly teach installing a monitoring software application on the ancillary communication device and opening the monitoring software application on the ancillary communication device.
Zack discloses a Personal Emergency Response System architecture able to detect falls, Nhu is directed to a smart home system that may include a fall detection sensor, and Kurfirst is directed to a fall detection system comprising a fall detection device and a user device. Kurfirst teaches:
installing a monitoring software application on the ancillary communication device; and
opening the monitoring software application on the ancillary communication device (see at least col. 5, lines 49-64; “Individual 102 may operate, own, and/or otherwise be associated with a user device 104. For instance, the user device 104 may be a mobile phone such as a smartphone that is owned and/or operated by the individual 102. The individual 102 may provide information to the other entities of environment 100 such as the enterprise computing system 112 using the user device 104. For example, the user device 104 may receive user input from the individual 102 such as indications to download, operate, and/or manage a software application associated with an enterprise organization. The enterprise organization may be any type of corporation, company, organization, and/or other institution. The software application may be an application that is used by the user device 104 to communicate with the first and/or second fall detection devices 108, 110 as well as the enterprise computing system 112.”).
Zack, Nhu and Kurfirst teach fall detection devices. Nhu teaches an ancillary communication device that runs computer code and can be implemented as a BLE gateway node or as a smartphone. Kurfirst teaches installing and operating an application related to fall-monitoring on a smartphone. It would have been obvious to one or ordinary skill in the art at the time of the claimed invention that the computer code taught by Nhu could be installed and opened, as taught by Kurfirst.
Claims 14-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Nhu, further in view of Sundholm et al. (US-20240065570-A1; hereinafter Sundholm).
Regarding claim 14, Zack in view of Nhu teaches the method of claim 13. However, Zack does not teach:
wherein the step of verifying the fall event comprises:
the at least one ancillary communication device transmitting characteristic real-time locating system signals (RTLS signal);
the radar monitor receiving at least one RTLS signal; and
the radar monitor identifying the at least one RTLS signal as characteristic of the at least one ancillary communication device.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Sundholm is directed to a fall detection device. Sundholm teaches:
wherein the step of verifying the fall event (see at least [0070]; “The system can send a notification of a fall, if a person is interpreted as having fallen and/or if the vital functions of the monitored person, such as tracked heartbeat and/or the monitored person's breathing, is not within the predefined limits.”) comprises:
the at least one ancillary communication device transmitting characteristic real-time locating system signals (RTLS signal) (see at least [0098]; “In one embodiment of the invention, the sensor and/or the system can comprise a radio-based identification means for identifying a person. The radio-based identification means can be, for example, Bluetooth, Bluetooth low energy (BLE) or Zigbee based means. In this embodiment, the system can recognize the person and a radio-based device carried by the person, such as a bracelet, a watch, a mobile device, a tag, and the measurement results can be linked to the specific recognized person. In this way the system is able to know, who is present in the monitored area and to whom the monitored results relate.”);
the radar monitor receiving at least one RTLS signal (see at least [0104]; “In one embodiment of the invention, in which the radio-based identification means are used, the necessary electronics and antennas can be integrated with the sensor. An example of embodiment is presented in FIG. 3, in which a Bluetooth antenna array is integrated with the sensor 301…The antenna or antenna array of the radar 303 arranged in this embodiment in the centre of the sensors and inside the area formed by the four Bluetooth antennas 302.”); and
the radar monitor identifying the at least one RTLS signal as characteristic of the at least one ancillary communication device (see at least [0102]; “In one embodiment of the invention, if the radar of the sensor detects movement but the radio-based identification means do not detect a remotely readable tag or device, such as a Bluetooth, BLE or Zigbee tag or device, then the person detected by the radar can be considered a visitor. If, on the other hand, the radar detects a remotely readable tag or device, such as a Bluetooth, BLE or Zigbee tag or device, then the detected person can be identified, and actions can be taken based on the identified person.”).
Both Zack and Sundholm teach radar devices that detect a fall event. It would have been obvious to one of ordinary skill at the time of the claimed invention to modify the device of Zack to include with the radar apparatus a radio-based identification means, as taught by Sundholm. One of ordinary skill would be motivated to do so in order to identify the detected person and take appropriate actions based on the identified person, as taught by Sundholm (see at least [0102]).
Regarding claim 15, Zack in view of Nhu teaches the method of claim 13. Zack further teaches:
wherein the step of verifying the fall event value (see at least [0094]; “System 100 may further comprise human state classifier 250 configured to classify the motion and movement characteristics of the at least one human to indicate a state of the at least one human, and abnormality situation pattern recognition module 262, e.g., as part of cognitive situation analysis module 260 configured to generate an alert once the indicated state is related to at least one specified emergency.”) comprises:
the radar monitor
determining an alert threshold (see at least [0094]; because identification if based on the most probably state, Examiner interprets determining an alert threshold as determining the probability of the second-most-probable state.);
determining a fall likelihood score (see at least [0120]; “The classifier output is the determined states and the set of the measured statistical distances (probabilities), i.e., the probability of State-i given the observation-O (the features vector). The aforementioned probability scheme may be formulated by: P (Si|O).” Note that Fig. 4 lists “Falling” as one of the states whose probability is assessed by the classifier.)
comparing the fall likelihood with the alert threshold (see at least [0094]; “The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.”).
However, Zack does not teach the at least one ancillary communication device transmitting characteristic real-time locating system signals (RTLS signal); the radar monitor receiving at least one RTLS signal; and determining a fall likelihood score based at least in part upon the at least one RTLS signal.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Sundholm is directed to a fall detection device. Sundholm teaches:
wherein the step of verifying the fall event (see at least [0070]; “The system can send a notification of a fall, if a person is interpreted as having fallen and/or if the vital functions of the monitored person, such as tracked heartbeat and/or the monitored person's breathing, is not within the predefined limits.”) comprises:
the at least one ancillary communication device transmitting characteristic real-time locating system signals (RTLS signal) (see at least [0098]; “In one embodiment of the invention, the sensor and/or the system can comprise a radio-based identification means for identifying a person. The radio-based identification means can be, for example, Bluetooth, Bluetooth low energy (BLE) or Zigbee based means. In this embodiment, the system can recognize the person and a radio-based device carried by the person, such as a bracelet, a watch, a mobile device, a tag, and the measurement results can be linked to the specific recognized person. In this way the system is able to know, who is present in the monitored area and to whom the monitored results relate.”);
the radar monitor receiving at least one RTLS signal (see at least [0098]; “In one embodiment of the invention, the sensor and/or the system can comprise a radio-based identification means for identifying a person. The radio-based identification means can be, for example, Bluetooth, Bluetooth low energy (BLE) or Zigbee based means. In this embodiment, the system can recognize the person and a radio-based device carried by the person, such as a bracelet, a watch, a mobile device, a tag, and the measurement results can be linked to the specific recognized person. In this way the system is able to know, who is present in the monitored area and to whom the monitored results relate.”); and
determining a fall alert based at least in part upon the at least one RTLS signal (see at least [0100]; “In one embodiment of the invention, the alarms can be automatically disabled, if the identification means detect a certain person such as a nurse in the monitored area.”).
Both Zack and Sundholm teach radar devices that detect a fall event. It would have been obvious to one of ordinary skill at the time of the claimed invention to modify the device of Zack to include with the radar device a radio-based identification means, as taught by Sundholm. One of ordinary skill would be motivated to do so in order to recognize when a nurse is present to help a resident, as taught by Sundholm (see at least [0100]).
Regarding claim 16, Zack in view of Nhu and Sundholm teaches the method of claim 15. Zack further teaches:
generating a fall alert only if the fall likelihood score exceeds the alert threshold (see at least [0094]; “The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” See also [0056]; “The system may automatically detect, identify and alert concerning emergency situations (particularly falls) that might be encountered by elders while being at home and identifies the emergency situations.”).
Regarding claim 17, Zack in view of Nhu and Sundholm teaches the method of claim 15. Zack further teaches:
further comprising inhibiting fall alert generation if the fall likelihood score is below the alert threshold (see at least [0094]; “The classification may carried out by identification of a most probable fit of one of a plurality of predefined states to the motion and movement characteristics and wherein the alert generation is based on pattern recognition with respect to previously indicated states.” See also [0056]; “The system may automatically detect, identify and alert concerning emergency situations (particularly falls) that might be encountered by elders while being at home and identifies the emergency situations.”).
Regarding claim 20, Zack in view of Nhu teaches the method of claim 11 and the at least one ancillary communication device establishing a two way audio communication channel between the at least one monitored subject and the third party. However, Zack does not teach:
wherein the step of the at least one ancillary communication device establishing a two way audio communication channel between the at least one monitored subject and the at least one third party comprises:
the communication device transmitting a communication signal to the radar monitor; and
the radar monitor relaying the communication signal to the at least one third party.
Zack discloses a Personal Emergency Response System architecture able to detect falls, and Sundholm is directed to a fall detection device. Sundholm teaches:
the communication device (see at least [0098]; “In one embodiment of the invention, the sensor and/or the system can comprise a radio-based identification means for identifying a person. The radio-based identification means can be, for example, Bluetooth, Bluetooth low energy (BLE) or Zigbee based means. In this embodiment, the system can recognize the person and a radio-based device carried by the person, such as a bracelet, a watch, a mobile device, a tag, and the measurement results can be linked to the specific recognized person. In this way the system is able to know, who is present in the monitored area and to whom the monitored results relate.”) transmitting a communication signal to the radar monitor (see at least [0102]; “In one embodiment of the invention, the radio-based identification means, for example Bluetooth, Bluetooth low energy (BLE) or Zigbee based means, can be used in locating a person or assist in locating the person. The sensor can include several antennas for radio-based identification means, e.g. Bluetooth, BLE or Zigbee antennas to enable direction finding techniques, for example Zigbee, Bluetooth or Bluetooth low energy (BLE) direction finding techniques, e.g. according to Bluetooth 5.1 specification. In one embodiment of the invention, if the radar of the sensor detects movement but the radio-based identification means do not detect a remotely readable tag or device, such as a Bluetooth, BLE or Zigbee tag or device, then the person detected by the radar can be considered a visitor. If, on the other hand, the radar detects a remotely readable tag or device, such as a Bluetooth, BLE or Zigbee tag or device, then the detected person can be identified, and actions can be taken based on the identified person. In one example embodiment, when a resident is in a room and there is also an assisting person, the status of the person or the room can be set in the system to “an assisting person present in the room”.”); and
the radar monitor relaying the communication signal to the at least one third party (see at least [0076]; “The sensors 101 can be connected wirelessly or by wireline to the gateway 104, which collects measured values obtained from the sensors 101 or status information formed by the sensors 101, e.g. the objects detected, the state of health of the objects, such as persons, and/or the movement and attitudes of the objects. The gateway 104 sends the information onwards e.g. to a control centre or to another body that supervises the area and/or the objects, such as persons, therein. The transfer of information between the system and some recipient can be performed e.g. using a phone connection, a wireline broadband connection or wireless connections”).
Both Zack and Sundholm teach radar devices that detect a fall event. It would have been obvious to one of ordinary skill at the time of the claimed invention to modify the device of Zack to include with the radar device a radio-based identification means, as taught by Sundholm. One of ordinary skill would be motivated to do so in order to update a relevant status shared with a third party (see at least [0102] and [0076]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley B. Raynal whose telephone number is (703)756-4546. The examiner can normally be reached Monday - Friday, 8 AM - 4 PM.
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648