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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 15 are drawn to “a computer program product…..comprising computer program code", per se, therefore, fail(s) to fall within a statutory category of invention. A claim directed to a computer program itself is non-statutory because it is not: A process, or A machine, or A manufacture, or A composition of matter.
Claim 15 is drawn to “a computer program product…..comprising computer program code” having stored there on a computer program, where the computer readable medium can be transitory, i.e., is not explicitly limited as disclosed as only being non-transitory computer readable media; therefore, fail(s) to fall within a statutory category of invention. Applicant should note that adding "non-transitory" to the claim to limit a claimed computer readable medium to being statutory would be acceptable.
A claim directed to a computer readable medium having stored there on a computer program is non-statutory, where the computer readable medium can be a signal, a carrier wave, or a data structure, per se, which are non-statutory as noted, infra.
A claim directed to a signal, a carrier wave, or a data structure, per se, is non-statutory because it is not: A process, or A machine, or A manufacture, or A composition of matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 11 and 13-15 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hanson et al. US 20140247335.
Regarding claim 1, Hanson et al. disclose A method for determining a label of a fall event, the method comprising the steps of: receiving signals from one or more sensors configured to measure signals indicative of characteristics of movement of a user; (Hanson et al. US 20140247335 abstract; paragraphs [0005]-[0014]; [0019]-[0022]; [0043]; [0047]-[0049]; [0059]; [0069]-0076]; [0086]-[0097] [0099]-[0107]; [0114]; figures 1-12;)
The central monitoring station 150 includes an electronic device (e.g., a server) configured to provide alarm monitoring service by exchanging communications with the remote monitoring server 130 over the network 145. For example, the central monitoring station 150 may be configured to monitor alarm events generated by a monitoring or alarm system that monitors the home or facility where the image sensing device 110 is located. In this example, the central monitoring station 150 may exchange communications with the remote monitoring server 130 to receive information regarding alarm events detected by the monitoring or alarm system. The central monitoring station 150 also may receive information regarding alarm events from the one or more user devices 140. The central monitoring station 150 may receive images captured by the image sensing device 110 to enable verification of potential fall events (Hanson et al. par. 36). Referring again to FIG. 5, the on-body sensor 510 comprises multi-modal sensing (e.g., triaxial inertial sensor, angular rate sensor, magnetometer, barometric pressure sensor, etc.), input/output, radio (e.g., via input/output), a processor, memory, battery, and user interface capabilities for human interaction (e.g., a button, LED/LCD, buzzer, etc.). The on-body sensor 510 may be used to measure gross human motion and activity, detect specific events or behaviors (e.g., falls, walking, running, sleeping, etc.), communicate to the user (e.g., reminders, notifications, etc.), or capture user input (e.g., panic button press, verification of event, etc.). Detecting falls with on-body sensing is described in further detail below (Hanson et al. par. 59). In general, the process 1100 enables fall detection and reporting based on human movement analysis. The system 400 performs a triggered or scheduled image capture (1110). For example, the system 400 may trigger a camera on an image sensing device to capture an image based on events detected by one or more of the image sensing device's sensors (e.g., perceived motion passive infrared motion sensor, triaxial inertial sensor). In this example, movement or impact detected proximal to the image sensing device may initiate the capture of an image. Furthermore, the system 400 may trigger the camera by one or more external sensors interfaced via a gateway device. For instance, the press of a panic button or the opening of a door sensor may trigger one or more image sensing devices to capture an image. Finally, image capture may be scheduled (e.g., capture an image every one minute during the hours of six in the morning through ten in the evening). In lower light conditions (e.g., characterized by the illumination sensor), the system 400 may employ infrared illumination to increase image detail and quality (Hanson et al. par. 100). After human segmentation, the system 400 performs human orientation and position estimation (1140). For example, the system 400 calculates orientation (e.g., human shape upright, angled, prone, etc.) and position (e.g., human shape above floor, near floor, etc.) by template or boundary shape proportion and rotation relative to a horizontal image plane. This estimation enables identification of postures and resting positions indicative of a fall. The floor proximal planar boundary can be specifically defined and moved to fit the unique geometries of different rooms (Hanson et al. par. 103).
According to the cited passages and figures, examiner interpret the system determining fall base the data and images receiving from sensing device like camera and/or using on-body sensor to detect human motion.
analyzing the received signals using a fall detection algorithm to determine a label indicative of a fall event by the user; initiating a first user interface interaction mode of a user interface, wherein in the first user interface interaction mode, the user interface is configured to receive a first input from the user indicative of a self-label of the fall event;
The system 400 performs fall verification (730). If a likely fall is detected, the detecting device, gateway, remote server, or monitoring center can initiate fall verification. The process can include an automated or human-prompted user response. For example, a user may be alerted (e.g., by audible tone, vibration, human operator, automated operator, or visual indicator) to verify their need for help (e.g., a button press or vocal response) or may be alerted to respond within a period of time to cancel a potential fall event. A human operator may also speak and listen to a user over two-way communication link (Hanson et al. par. 70). To tune sensitivity and specificity of fall detection (e.g., the on-body fall detection process 800), the process 1000 uses user feedback. The process 1000 may produce more granular fall reporting (e.g., true, false, minor, canceled falls) and may help to reduce and report incidence of false positives or false negatives (Hanson et al. par. 87). The system 400 prompts the user for cancellation of the potential fall event (1010). A user prompt may be initiated (e.g., audible or visual). The user can respond (e.g., by a button press or vocalization) to the user prompt at the device to cancel the detected potential fall event (Hanson et al. par. 89). The system 400 determines whether the user cancels the potential fall event within a defined period of time (1015). For instance, the system 400 monitors for input cancelling the potential fall event until the defined period of time has been reached and the system 400 determines whether the user cancelled the potential fall event within the defined period of time based on the monitoring. Based on a determination that the potential fall event was not cancelled within the defined period of time, the system 400 generates a fall signal (e.g., a fall signal from the body-worn device) (Hanson et al. par. 90).
According to the cite passages and figures, examiner interprets the user feedback like user prompt may be initiated as audible or visual as the first user interface interaction mode. Examiner interprets user can input their request or feedback through the user device over two-way communication to indicate their condition (e.g. fall triggered or not fall).
receiving the first input; determining a level of mismatch between the self-label and the determined label; if the level of mismatch is above a threshold, switching the user interface to a second user interface interaction mode,
Fall verification procedures may be staged sequentially or paired with fall detection mechanisms to create a hierarchical fall escalation process. For example, less accurate fall detection methods may trigger less invasive user verification (e.g., prompted user button press). If no user response is given within a threshold period of time, then more accurate fall detection methods may be employed alongside more invasive fall verification (e.g., two way communications with monitoring center) (Hanson et al. par. 72). Based on a determination that the subject did not recover from the suspected fall, the system 400 performs another user prompt for cancellation (1050) and determines whether the user cancels the potential fall event within a defined period of time from the additional prompt for cancellation (1055). (Hanson et al. par. 96).
wherein in the second user interface interaction mode, the user interface is configured to receive a second input from the user indicative of contextual information regarding the fall event, receiving the second input, and updating the self-label of the fall event based on the second input received.
Based on a determination that the potential fall event was cancelled within the defined period of time, the system 400 signals a cancelled fall (1060). For instance, the system 400 does not provide an alert for the potential fall event, but does classify the sensor data used to detect the potential fall event as being sensor data associated with a fall that was ultimately cancelled (Hanson et al. par. 96). In some examples, the system may tune sensitivity of one or more sensors/contexts used in fall detection and may determine a score as part of fall classification. In these examples, the system may determine the score based on a number of sensors that indicate a potential fall. For instance, the system may determine a relatively high score when the system detects a thud based on an accelerometer sensor, detects multiple motion sensors indicating motion consistent with a fall, and performs image analysis that suggests that a person has moved from a vertical orientation to a horizontal orientation below a plane near the floor (Hanson et al. par. 112).
According to the cited passages and figures, examiner interpret the user selected an input (second input) to cancelled fall triggered after period of time and update the fall event.
Regarding claim 11, Hanson et al. disclose The method according to claim 1, wherein the method further comprises receiving an input indicative of one or more characteristics of the user and determining a user interface input and/or output modality based on the one or more characteristics of the user.
Referring again to FIG. 5, the on-body sensor 510 comprises multi-modal sensing (e.g., triaxial inertial sensor, angular rate sensor, magnetometer, barometric pressure sensor, etc.), input/output, radio (e.g., via input/output), a processor, memory, battery, and user interface capabilities for human interaction (e.g., a button, LED/LCD, buzzer, etc.). The on-body sensor 510 may be used to measure gross human motion and activity, detect specific events or behaviors (e.g., falls, walking, running, sleeping, etc.), communicate to the user (e.g., reminders, notifications, etc.), or capture user input (e.g., panic button press, verification of event, etc.). Detecting falls with on-body sensing is described in further detail below (Hanson et al. par. 59). Fall verification procedures may be staged sequentially or paired with fall detection mechanisms to create a hierarchical fall escalation process. For example, less accurate fall detection methods may trigger less invasive user verification (e.g., prompted user button press). If no user response is given within a threshold period of time, then more accurate fall detection methods may be employed alongside more invasive fall verification (e.g., two way communications with monitoring center) (Hanson et al. par. 72). Based on a determination that the subject did not recover from the suspected fall, the system 400 performs another user prompt for cancellation (1050) and determines whether the user cancels the potential fall event within a defined period of time from the additional prompt for cancellation (1055). Based on a determination that the potential fall event was cancelled within the defined period of time, the system 400 signals a cancelled fall (1060). For instance, the system 400 does not provide an alert for the potential fall event, but does classify the sensor data used to detect the potential fall event as being sensor data associated with a fall that was ultimately cancelled (Hanson et al. par. 96).
Regarding claim 13, Hanson et al. disclose A controller for determining a label of a fall event, the controller configured to: receive signals from one or more sensors configured to measure signals indicative of characteristics of movement of a user; (Hanson et al. US 20140247335 abstract; paragraphs [0005]-[0014]; [0019]-[0022]; [0043]; [0047]-[0049]; [0059]; [0069]-0076]; [0086]-[0097] [0099]-[0107]; [0114] figures 1-12;)
The central monitoring station 150 includes an electronic device (e.g., a server) configured to provide alarm monitoring service by exchanging communications with the remote monitoring server 130 over the network 145. For example, the central monitoring station 150 may be configured to monitor alarm events generated by a monitoring or alarm system that monitors the home or facility where the image sensing device 110 is located. In this example, the central monitoring station 150 may exchange communications with the remote monitoring server 130 to receive information regarding alarm events detected by the monitoring or alarm system. The central monitoring station 150 also may receive information regarding alarm events from the one or more user devices 140. The central monitoring station 150 may receive images captured by the image sensing device 110 to enable verification of potential fall events (Hanson et al. par. 36). Referring again to FIG. 5, the on-body sensor 510 comprises multi-modal sensing (e.g., triaxial inertial sensor, angular rate sensor, magnetometer, barometric pressure sensor, etc.), input/output, radio (e.g., via input/output), a processor, memory, battery, and user interface capabilities for human interaction (e.g., a button, LED/LCD, buzzer, etc.). The on-body sensor 510 may be used to measure gross human motion and activity, detect specific events or behaviors (e.g., falls, walking, running, sleeping, etc.), communicate to the user (e.g., reminders, notifications, etc.), or capture user input (e.g., panic button press, verification of event, etc.). Detecting falls with on-body sensing is described in further detail below (Hanson et al. par. 59). In general, the process 1100 enables fall detection and reporting based on human movement analysis. The system 400 performs a triggered or scheduled image capture (1110). For example, the system 400 may trigger a camera on an image sensing device to capture an image based on events detected by one or more of the image sensing device's sensors (e.g., perceived motion passive infrared motion sensor, triaxial inertial sensor). In this example, movement or impact detected proximal to the image sensing device may initiate the capture of an image. Furthermore, the system 400 may trigger the camera by one or more external sensors interfaced via a gateway device. For instance, the press of a panic button or the opening of a door sensor may trigger one or more image sensing devices to capture an image. Finally, image capture may be scheduled (e.g., capture an image every one minute during the hours of six in the morning through ten in the evening). In lower light conditions (e.g., characterized by the illumination sensor), the system 400 may employ infrared illumination to increase image detail and quality (Hanson et al. par. 100). After human segmentation, the system 400 performs human orientation and position estimation (1140). For example, the system 400 calculates orientation (e.g., human shape upright, angled, prone, etc.) and position (e.g., human shape above floor, near floor, etc.) by template or boundary shape proportion and rotation relative to a horizontal image plane. This estimation enables identification of postures and resting positions indicative of a fall. The floor proximal planar boundary can be specifically defined and moved to fit the unique geometries of different rooms (Hanson et al. par. 103).
According to the cited passages and figures, examiner interpret the system determining fall base the data and images receiving from sensing device like camera and/or using on-body sensor to detect human motion.
analyze the received signals using a fall detection algorithm to determine a label indicative of a fall event by the user; initiate a first user interface interaction mode of a user interface wherein in the first user interface interaction mode, the user interface is configured to receive a first input from the user indicative of a self-label of the fall event;
The system 400 performs fall verification (730). If a likely fall is detected, the detecting device, gateway, remote server, or monitoring center can initiate fall verification. The process can include an automated or human-prompted user response. For example, a user may be alerted (e.g., by audible tone, vibration, human operator, automated operator, or visual indicator) to verify their need for help (e.g., a button press or vocal response) or may be alerted to respond within a period of time to cancel a potential fall event. A human operator may also speak and listen to a user over two-way communication link (Hanson et al. par. 70). To tune sensitivity and specificity of fall detection (e.g., the on-body fall detection process 800), the process 1000 uses user feedback. The process 1000 may produce more granular fall reporting (e.g., true, false, minor, canceled falls) and may help to reduce and report incidence of false positives or false negatives (Hanson et al. par. 87). The system 400 prompts the user for cancellation of the potential fall event (1010). A user prompt may be initiated (e.g., audible or visual). The user can respond (e.g., by a button press or vocalization) to the user prompt at the device to cancel the detected potential fall event (Hanson et al. par. 89). The system 400 determines whether the user cancels the potential fall event within a defined period of time (1015). For instance, the system 400 monitors for input cancelling the potential fall event until the defined period of time has been reached and the system 400 determines whether the user cancelled the potential fall event within the defined period of time based on the monitoring. Based on a determination that the potential fall event was not cancelled within the defined period of time, the system 400 generates a fall signal (e.g., a fall signal from the body-worn device) (Hanson et al. par. 90).
According to the cite passages and figures, examiner interprets the user feedback like user prompt may be initiated as audible or visual as the first user interface interaction mode. Examiner interprets user can input their request or feedback through the user device over two-way communication to indicate their condition (e.g. fall triggered or not fall).
receive the first input; determine a level of mismatch between the self-label and the determined label; if the level of mismatch is above a threshold, switch the user interface to a second user interface interaction mode,
Fall verification procedures may be staged sequentially or paired with fall detection mechanisms to create a hierarchical fall escalation process. For example, less accurate fall detection methods may trigger less invasive user verification (e.g., prompted user button press). If no user response is given within a threshold period of time, then more accurate fall detection methods may be employed alongside more invasive fall verification (e.g., two way communications with monitoring center) (Hanson et al. par. 72). Based on a determination that the subject did not recover from the suspected fall, the system 400 performs another user prompt for cancellation (1050) and determines whether the user cancels the potential fall event within a defined period of time from the additional prompt for cancellation (1055). (Hanson et al. par. 96).
wherein in the second user interface interaction mode, the user interface is configured to receive a second input from the user indicative of contextual information regarding the fall event, receive the second input, and update the self-label of the fall event based on the second input received.
Based on a determination that the potential fall event was cancelled within the defined period of time, the system 400 signals a cancelled fall (1060). For instance, the system 400 does not provide an alert for the potential fall event, but does classify the sensor data used to detect the potential fall event as being sensor data associated with a fall that was ultimately cancelled (Hanson et al. par. 96). In some examples, the system may tune sensitivity of one or more sensors/contexts used in fall detection and may determine a score as part of fall classification. In these examples, the system may determine the score based on a number of sensors that indicate a potential fall. For instance, the system may determine a relatively high score when the system detects a thud based on an accelerometer sensor, detects multiple motion sensors indicating motion consistent with a fall, and performs image analysis that suggests that a person has moved from a vertical orientation to a horizontal orientation below a plane near the floor (Hanson et al. par. 112).
According to the cited passages and figures, examiner interpret the user selected an input (second input) to cancelled fall triggered after period of time and update the fall event.
Regarding claim 14, Hanson et al. disclose A system for determining a label of a fall event, the system comprising: one or more sensors configured to measure signals indicative of characteristics of movement of a user; a controller according to claim 13.
In some implementations, the image sensing device 110 and the gateway 120 may be part of a home or facility monitoring system (e.g., a home security system). In these implementations, the home or facility monitoring system may sense many types of events or activities associated with the home or facility and the sensed events or activities may be leveraged in performing fall detection and reporting features. The home or facility monitoring system may include a controller that communicates with the gateway 120. The controller may be configured to control the home or facility monitoring system (e.g., a home alarm or security system). In some examples, the controller may include a processor or other control circuitry configured to execute instructions of a program that controls operation of an alarm system. In these examples, the controller may be configured to receive input from sensors, detectors, or other devices included in the home or facility monitoring system and control operations of devices included in the home or facility monitoring system or other household devices (e.g., a thermostat, an appliance, lights, etc.) (Hanson et al. par. 43).
Regarding claim 15, Hanson et al. disclose A computer program product for a computing device, the computer program product comprising computer program code to perform the method of claims 1 when the computer program product is run on a processing unit of the computing device.
The described systems, methods, and techniques may be implemented in digital electronic circuitry, computer hardware, firmware, software, or in combinations of these elements. Apparatus implementing these techniques may include appropriate input and output devices, a computer processor, and a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor. A process implementing these techniques may be performed by a programmable processor executing a program of instructions to perform desired functions by operating on input data and generating appropriate output. The techniques may be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language may be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors (Hanson et al. par. 114).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hanson et al. US 20140247335 in view of Spinelli et al. US 20230105173.
Regarding claim 5, Hanson et al. teach all the limitation in the claim 1.
Hanson et al. do not explicitly teach The method according to claim 1, wherein the step of receiving the second input indicative of contextual information regarding the fall event comprises receiving information regarding one of: actions of the user preceding the fall event, supporting evidence regarding the fall event, time data, location data, presence of a further person during the fall event, a light setting during and before the fall event.
Spinelli et al. teach The method according to claim 1, wherein the step of receiving the second input indicative of contextual information regarding the fall event comprises receiving information regarding one of: actions of the user preceding the fall event, supporting evidence regarding the fall event, time data, location data, presence of a further person during the fall event, a light setting during and before the fall event. (Spinelli et al. US 20230105173 abstract; paragraphs [0004]-[0018]; [0032]-[0037]; [0039]; [0056]; [0076]-[0078]; [0085]; figures 1-4)
Some embodiments of one or more systems and/or methods for performing dynamic optimized activity-assignment processing further include where, when the at least one processor executes the software instructions, the at least one processor is further programmed to: iteratively receive, during the plurality of time periods, the plurality of floor visual inputs from the plurality of cameras when the light intensity detected by each light sensor associated with each camera is of the sufficient amount to recognize a particular slippery condition on the floor (Spinelli et al. par. 17). FIG. 2 illustrates an exemplary computer-based system and platform automated machine learning-based slip risk conditions using image data in an enclosure including a warehouse for alerting one or more users in accordance with one or more embodiments of the present disclosure. In some embodiments, the warehouse may include multiple locations: a, b, c, d, e and f. The locations a, b, c, d, e and f may be within the view of multiple cameras 1, 2, 3 and 4 to provide imagery of each location a, b, c, d, e and f from different angles. Based on the detection of slippery conditions, e.g., at location f by camera 4, a user may be provided with a graphical user interface including the image with the detected slippery condition including a spill and a user selectable action to indicate the spill is “cleaned” and/or “not a spill” (Spinelli et al. par. 85).
According to the cited passages and figures, examiner interprets the camera is able to record the environmental condition that include time, location and light level at those particular location.
Therefore, it would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to substitute the image that show risky location that causing the fall like slippery condition and poor light intensity as taught by Spinelli et al. reference into the method of Hanson et al. reference. The result of the substitution would be predictable to enhance personal safety and avoid fall.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hanson et al. US 20140247335.
Regarding claim 7, Hanson et al. teach The method according to claim 1, wherein in the second user interface interaction mode, the user interface is configured to determine a question setting based on a natural language processing algorithm and output the determined question setting to the user.
The system 400 performs fall verification (730). If a likely fall is detected, the detecting device, gateway, remote server, or monitoring center can initiate fall verification. The process can include an automated or human-prompted user response. For example, a user may be alerted (e.g., by audible tone, vibration, human operator, automated operator, or visual indicator) to verify their need for help (e.g., a button press or vocal response) or may be alerted to respond within a period of time to cancel a potential fall event. A human operator may also speak and listen to a user over two-way communication link (Hanson et al. par. 70). Fall verification procedures may be staged sequentially or paired with fall detection mechanisms to create a hierarchical fall escalation process. For example, less accurate fall detection methods may trigger less invasive user verification (e.g., prompted user button press). If no user response is given within a threshold period of time, then more accurate fall detection methods may be employed alongside more invasive fall verification (e.g., two way communications with monitoring center) (Hanson et al. par. 72).
According to the cited passages and figures, examiner interprets the interaction questionary can be set or defined by user design desire via two-way communication.
However, Hanson et al. do not explicitly teach a question setting but it would be obviously to one of ordinary skill in the art to set or define the question as the user design desire for exchanging conversation during two-way communication when the fall is triggered.
Regarding claim 8, Hanson et al. teach The method according to claim 1, wherein in the second user interface interaction mode, the user interface is configured to determine a question setting based on the level of mismatch between the self-label and the determined label and output the determined question setting to the user.
The system 400 performs fall verification (730). If a likely fall is detected, the detecting device, gateway, remote server, or monitoring center can initiate fall verification. The process can include an automated or human-prompted user response. For example, a user may be alerted (e.g., by audible tone, vibration, human operator, automated operator, or visual indicator) to verify their need for help (e.g., a button press or vocal response) or may be alerted to respond within a period of time to cancel a potential fall event. A human operator may also speak and listen to a user over two-way communication link (Hanson et al. par. 70). Fall verification procedures may be staged sequentially or paired with fall detection mechanisms to create a hierarchical fall escalation process. For example, less accurate fall detection methods may trigger less invasive user verification (e.g., prompted user button press). If no user response is given within a threshold period of time, then more accurate fall detection methods may be employed alongside more invasive fall verification (e.g., two way communications with monitoring center) (Hanson et al. par. 72).
According to the cited passages and figures, examiner interprets the interaction questionary can be set of defined by user design desire via two-way communication.
However, Hanson et al. do not explicitly teach a question setting but it would be obviously to one of ordinary skill in the art to set or define the question as the user design desire for exchanging conversation during two-way communication when the fall is triggered.
Allowable Subject Matter
Claims 2-4, 9-10 and 12 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 2, Hanson et al. US 20140247335, Spinelli et al. US 20230105173, Sharma et al. US 20210005071, Heaton et al. US 20180000385, Haflinger et al. US 20160307427, Sweeney et al. US 20140313036, Ten Kate US 20140191863, Goodman US 20100052896, Ng et al. US 20200211154, Betka et al. US 20240037947, Kurfirst US 11055981 and Sayavong et al. US 20200286365 are the closest art. They are teaching every limitation of claim 2 except for this limitation cited “wherein the second input comprises verbal and/or non-verbal cues, and wherein the method further comprises: analyzing the verbal and/or non-verbal cues to determine a user intent score, said user intent score being indicative of the user's intent to deceive, and updating the self-label of the fall event based on the user intent score.”.
After update search, there are none of the prior arts of record singularly or combination, teaches or fairly suggest the features present in the claim 2 “wherein the second input comprises verbal and/or non-verbal cues, and wherein the method further comprises: analyzing the verbal and/or non-verbal cues to determine a user intent score, said user intent score being indicative of the user's intent to deceive, and updating the self-label of the fall event based on the user intent score.”.
Prior arts of record fail to disclose “wherein the second input comprises verbal and/or non-verbal cues, and wherein the method further comprises: analyzing the verbal and/or non-verbal cues to determine a user intent score, said user intent score being indicative of the user's intent to deceive, and updating the self-label of the fall event based on the user intent score.”. However, upon consideration of the claim invention, there is no reasoning to combine the applied references to arrive in the context of the claim invention.
Claims 3-4 depend on and further limit of independent claim 2, therefore claims 3-4 are considered allowable for the same reason.
Regarding claim 9, Hanson et al. US 20140247335, Spinelli et al. US 20230105173, Sharma et al. US 20210005071, Heaton et al. US 20180000385, Haflinger et al. US 20160307427, Sweeney et al. US 20140313036, Ten Kate US 20140191863, Goodman US 20100052896, Ng et al. US 20200211154, Betka et al. US 20240037947, Kurfirst US 11055981 and Sayavong et al. US 20200286365 are the closest art. They are teaching every limitation of claim 9 except for this limitation cited “wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the first user interface interaction mode is initiated when the determined fall event is of a new type.”.
After update search, there are none of the prior arts of record singularly or combination, teaches or fairly suggest the features present in the claim 9 ““wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the first user interface interaction mode is initiated when the determined fall event is of a new type”.
Prior arts of record fail to disclose ““wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the first user interface interaction mode is initiated when the determined fall event is of a new type”. However, upon consideration of the claim invention, there is no reasoning to combine the applied references to arrive in the context of the claim invention.
Regarding claim 10, Hanson et al. US 20140247335, Spinelli et al. US 20230105173, Sharma et al. US 20210005071, Heaton et al. US 20180000385, Haflinger et al. US 20160307427, Sweeney et al. US 20140313036, Ten Kate US 20140191863, Goodman US 20100052896, Ng et al. US 20200211154, Betka et al. US 20240037947, Kurfirst US 11055981 and Sayavong et al. US 20200286365 are the closest art. They are teaching every limitation of claim 10 except for this limitation cited “wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the second user interface interaction mode is conditioned on whether the determined fall event is of a new type.”.
After update search, there are none of the prior arts of record singularly or combination, teaches or fairly suggest the features present in the claim 10 “wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the second user interface interaction mode is conditioned on whether the determined fall event is of a new type.”.
Prior arts of record fail to disclose “wherein the determining of the label indicative of the fall event comprises determining a type of the fall event, and wherein the second user interface interaction mode is conditioned on whether the determined fall event is of a new type.”. However, upon consideration of the claim invention, there is no reasoning to combine the applied references to arrive in the context of the claim invention.
Regarding claim 12, Hanson et al. US 20140247335, Spinelli et al. US 20230105173, Sharma et al. US 20210005071, Heaton et al. US 20180000385, Haflinger et al. US 20160307427, Sweeney et al. US 20140313036, Ten Kate US 20140191863, Goodman US 20100052896, Ng et al. US 20200211154, Betka et al. US 20240037947, Kurfirst US 11055981 and Sayavong et al. US 20200286365 are the closest art. They are teaching every limitation of claim 12 except for this limitation cited “receiving an input indicative of one or more characteristics of the user; determining a period of time for switching the user interface to the second user interface interaction mode, the period of time based on the one or more characteristics of the user and/or the user intent score of the self-label; switching the user interface to the second user interface interaction mode after the determined period of time.”.
After update search, there are none of the prior arts of record singularly or combination, teaches or fairly suggest the features present in the claim 12 “receiving an input indicative of one or more characteristics of the user; determining a period of time for switching the user interface to the second user interface interaction mode, the period of time based on the one or more characteristics of the user and/or the user intent score of the self-label; switching the user interface to the second user interface interaction mode after the determined period of time.”.
Prior arts of record fail to disclose “receiving an input indicative of one or more characteristics of the user; determining a period of time for switching the user interface to the second user interface interaction mode, the period of time based on the one or more characteristics of the user and/or the user intent score of the self-label; switching the user interface to the second user interface interaction mode after the determined period of time.”. However, upon consideration of the claim invention, there is no reasoning to combine the applied references to arrive in the context of the claim invention.
Conclusion
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/THANG D TRAN/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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