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 .
Status of Claims
This action is in reply to the present action filed on 3/15/2024.
Claims 1-7, 10, 12-13, and 15-20 have been amended.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/20/2025 was filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Claims 1-20 are within the four statutory categories. Claims 1-14 are drawn to a method (i.e. process). Claims 15-17 are drawn to a computing device (i.e. machine). Claims 18-20 are drawn to a non-transitory computer readable medium (i.e. product of manufacture).
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A computer-implemented method comprising:
receiving, by a computer, initial sensor data from a thermal-imaging sensor and at least one additional sensor of a sensor set in a physical environment for a time period,
wherein the sensor set includes at least one of a temperature sensor, a pressure sensor, a humidity sensor, a light sensor, a sound sensor, and a motion sensor;
detecting, based on the initial sensor data, a target subject, a person, and an object in the physical environment, wherein the object is separate from the target subject;
determining, based on the initial sensor data, a first distance between the target subject and the person and a second distance between the target subject and the object;
determining a first movement pattern of the target subject in the physical environment;
determining a second movement pattern of the person; and
determining a third movement pattern of the object;
determining behavior patterns of a set of sleep events of the target subject based on the first movement pattern of the target subject, the second movement pattern of the person, and the third movement pattern of the object;
generating, by the computer, a recommendation based on the behavior patterns to achieve a target outcome for the target subject in the physical environment, wherein the recommendation includes removing the object from the physical environment;
causing a user device to display a user interface that includes the recommendation;
determining that the target subject followed the recommendation; and
determining that following the recommendation resulted in the target outcome being achieved.
The series of steps as shown in underline above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teachings, and following rules or instructions – in this case, receiving sensor data, detecting a target subject, person, and object, determining behavior patterns, generating a recommendation to achieve a target outcome, providing the recommendation, determining that the recommendation was followed, and determining that following the recommendation resulted in the target outcome being achieved), e.g., see MPEP 2106.04(a)(2). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-6, 8-14, 16-17, and 19-20 include other limitations directed toward the abstract idea. For example, Claim 2 recites generating a second recommendation based on the behavior patterns to achieve a target outcome for the patient, Claims 3, 17, and 20 recites updating behavior patterns and target outcomes, Claim 4 recites determining that a recommendation was followed and notifying a third party that the recommendation was not followed, Claim 5 recites transmitting information about the behavior patterns and the recommendation, Claim 6 recites detecting a sound level and determining sound patterns, Claim 8 recites detecting biofeedback and determining the behavior patterns, Claim 9 recites determining attributes associated with a seizure event, Claim 10 recites the target outcome is defined based on the user preferences, Claim 11 recites receiving sensor data, determining behavior patterns, and providing a warning, Claim 12 recites determining behavior patterns of a set of sleep events, Claim 13 recites receiving data of length of sleep time and providing the time asleep compared to the target, Claim 14 recites providing sensor data and outputting a recommendation for achieving a target outcome, Claims 16 and 19 recite comparing sleeping times of the target subject and one or more sleeping times of the person. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in the dependent clams not addressed above are part of the abstract idea and will be further addressed below. Hence, dependent Claims 2-6, 8-14, 16-17, and 19-20 are nonetheless directed toward fundamentally the same abstract idea as independent Claims 1, 15, and 18.
Step 2A Analysis – Prong Two:
Claims 1, 15, and 18 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the computer, sensors of a sensor set, temperature sensor, a pressure sensor, a humidity sensor, a light sensor, a sound sensor, a thermal-imaging sensor, motion sensor, user device, and user interface of Claim 1, the processor, memory, sensors of a sensor set, temperature sensor, a pressure sensor, a humidity sensor, a light sensor, a sound sensor, a thermal-imaging sensor, motion sensor, user device, and user interface of Claim 15, and the non-transitory computer-readable medium, computer, sensors of a sensor set, temperature sensor, a pressure sensor, a humidity sensor, a light sensor, a sound sensor, a thermal-imaging sensor, motion sensor, user device, and user interface of Claim 18) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the user device 115 may include a desktop computer, a mobile device, a tablet computer, a mobile telephone, a wearable device, a head-mounted display, a mobile email device, a portable game player, a portable music player, a reader device, or another electronic device capable of accessing a network 105 [0046]. Such a computer program may be stored in a non-transitory computer-readable storage medium, including, but not limited to, any type of disk including optical disks, ROMs, CD-ROMs, magnetic disks, RAMs,…[00160]. Furthermore, the recited sensors of Claims 1, 15, and 18 were considered to be generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h). For example, Applicant’s specification explains that the sleep hub 120 may include an I/O interface 224, a sensor set with one or more of a temperature sensor 225, a pressure sensor 230, a humidity sensor 235, a light sensor 240, a sound sensor 245, a motion sensor 250, a thermal imaging sensor 255, a camera 260, and a speaker 265 [0052]. The sensor devices merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(1) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. Therefore Claims 1, 15, and 18 do not integrate the aforementioned abstract idea into a practical application.
Claims 2, 5-8, 10, and 13-14, and 16 recite additional elements. Claim 2 recites the previously recited additional element of the computer and user device and specifies the computer generates a second recommendation based on the behavior patterns, and the user device is associated with the person and includes a comparison of one or more sleeping times of the target subject and one or more sleeping times of the person. Claim 5 recites a new additional element of a medical server and an electronic medical record and specifies information is transmitted to a medical server for incorporation into an electronic medical record that is associated with the target subject. Claims 6 recites previously recited additional elements of a sensor set and a sound sensor and specifies the sensor set includes a sound sensor. Claim 7 recites the previously recited motion sensor and a new element of an activity tracker and specifies the sensor set includes the motion sensor and the motion sensor is part of an activity tracker worn by the target subject. Claims 8 recites the previously recited motion sensor and a new element of a radar and specifies the motion sensor is radar. Claim 10 recites the previously recited user interface and specifies prior to receiving the initial sensor data, providing the user interface with a request for user preferences about the target outcome. Claim 13 recites the previously recited user interface and specifies the user interface is a first interface and further providing a second user interface to the person which includes the length of time when the person is asleep as compared to the target subject. Claim 14 recites a new additional element of a machine learning model and specifies the initial sensor data is input into the model and the model outputs the recommendation for achieving the target outcome. Claims 16 and 19 recite the previously recited user interface and specify the interface includes a comparison of sleeping times of the target subject with seeping times of the person. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, when considered individually or in combination, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements of the computer, sensor set, listed sensors, user device, and user interface of Claim 1, the processor, memory, sensors of a sensor set, listed sensors, user device, and user interface of Claim 15, and the non-transitory computer-readable medium, computer, sensor set, listed sensors, user device, and user interface of Claim 18 amount to mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the sensors were considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the ‘significantly more’ analysis and has been found insufficient to provide significantly more. MPEP2106.05 (A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide an inventive concept (‘significantly more").
Accordingly, even in combination, these additional elements cannot provide significantly more. As such the independent Claims 1, 15, and 18 are not patent eligible.
Dependent Claims 3-4, 9, 11-12, 17, and 20 are similarly rejected because they further define the abstract idea and do not recite any additional elements. Claims 3, 17, and 20 recites updating behavior patterns and target outcomes. Claim 4 recites determining that a recommendation was followed and notifying a third party that the recommendation was not followed. Claim 9 recites determining attributes associated with a seizure event. Claim 11 recites receiving sensor data, determining behavior patterns, and providing a warning. Claim 12 recites determining behavior patterns of a set of sleep events.
Dependent Claims 2, 6, 10, 13, 16, and 19 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 2 recites the computer and user device and specifies the computer generates a second recommendation based on the behavior patterns, and the user device is associated with the person and includes a comparison of one or more sleeping times of the target subject and one or more sleeping times of the person. Claims 6 recites previously recited additional elements of a sensor set and a sound sensor and specifies the sensor set includes a sound sensor. Claim 10 recites the previously recited user interface and specifies providing the user interface with a request for user preferences about the target outcome. Claim 13 recites the previously recited user interface and specifies the user interface is a first interface and further providing a second user interface to the person which includes the length of time when the person is asleep as compared to the target subject. Claims 16 and 19 recite the user interface and specify the interface includes a comparison of sleeping times of the target subject with seeping times of the person.
Claims 5, 7-8, and 14 recite new additional elements with new functions. Claims 10 and 13 recite a user interface. Claim 5 recites a new additional element of a medical server and an electronic medical record and specifies information is transmitted to a medical server for incorporation into an electronic medical record that is associated with the target subject. Claim 7 recites the previously recited sensor set and motion sensor and a new element of an activity tracker and specifies the sensor set includes the motion sensor and the motion sensor is part of an activity tracker worn by the target subject. Claims 8 recites the recited motion sensor and a new element of a radar and specifies the motion sensor is radar. Claim 14 recites a new element of a machine learning model and specifies the initial sensor data is input into the model and the model outputs the recommendation for achieving the target outcome. Hence, Claims 2-14, 16-17, and 19-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6, 8, 14-15, and 18 are rejected under 35 USC § 103 as being unpatentable over Shouldice et al. (US 20160151603 A1) in view of Matsuoka et al. (US 20190200872 A1), Yamamoto et al. (US 20210106263 A1), and Orlovsky et al. (US 20220293276 A1).
Regarding Claim 1, Shouldice discloses the following:
A computer-implemented method comprising: receiving, by a computer, initial sensor data from a…sensor and at least one additional sensor of a sensor set in a physical environment for a time period, (Shouldice discloses the present technology may involve a method for an electronic system to promote sleep with one or more processors. The one or more processors may be in a server(s), a smart device(s) (e.g., mobile phone), a computer(s)…[0117]. The system can use environmental sensors to monitor the user's bedroom environment, such as light, sound, temperature, humidity, and/or air quality [0218]. The method may involve processing the measured data and determining sleep factors with features derived from the measured data [0109]. An alarm notification may be…if the user is taking too long to fall asleep (e.g., a time period commencing with the start of a sleep session [0212].)
wherein the sensor set includes at least one of a temperature sensor, a pressure sensor, a humidity sensor, a light sensor, a sound sensor, and a motion sensor; (Shouldice discloses the system and method measure environmental parameters of the bedroom using sensors such as light, sound, temperature, humidity and/or air quality [0079]. The system can record the user's sleep, breathing and heart rate patterns using a bio motion sensor, [0079].)
detecting, based on the initial sensor data, a target subject, (Shouldice discloses the present technology may include an apparatus to promote sleep of a user…may include a processor coupled with the microphone and configured to receive signals generated by a sensor and indicative of motion of a user. The processor may be further configured to analyze the received signals and detect sleep information from the signals…[0090].)
determining behavior patterns of a set of sleep events of the target subject based on the movement pattern of the…subject…; (Shouldice discloses the method may involve with a processor, analyzing signals from a motion sensor to detect sleep information from the signals [0092]. The technology may optionally include a device with any one or more of the following features:… analyze the user's sleep environment (light, sound and temperature, as well as humidity and/or air quality)… analyze the user’s sleeping, breathing and heart rate patterns… chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep [0071]. The system can record the user's sleep, breathing and heart rate patterns using a bio motion sensor,…[0079].)
generating, by the computer, a recommendation based on the behavior patterns to achieve a target outcome for the target subject in the physical environment; (Shouldice discloses the technology…may chart the user’s sleep patterns and send personalized recommendations via text or email to help improve the user's sleep. These customized advice “nuggets” are designed to help the person sleep better and may be based on clinical research [0071-75]. As shown in FIG. 1, in one view the system can be divided… providing sleep assistance (by guiding the user towards relaxation) in stage A, sleep data recording and analysis in Stage B, and providing sleep recommendations and coaching in Stage C. The interconnection between these stages may be understood with reference to FIG. 2 showing a progression of sleep data. Initially collected by the various sensors from the user in the “capture” stage, the data is processed, during the “crunch” stage. During this processing various characteristics and trends in the data, sleep characteristics and patterns are identified. On the basis of these features and trends, the proposed system and method provide recommendations and coaching to the user, in the “deliver” stage (e.g., FIG. 36) [0211].)
causing a user device to display a user interface that includes the recommendation (Shouldice discloses providing sleep recommendations and coaching in Stage C… the data is processed, during the “crunch” stage. During this processing various characteristics and trends in the data, sleep characteristics and patterns are identified. On the basis of these features and trends, the proposed system and method provide recommendations and coaching to the user, in the “deliver” stage (see, e.g., FIG. 36) [0231, see also Fig. 24 which shows a display of a recommendation]. FIG. 7 shows a logic outline of a web server/cloud and of its data links with the smart device app or PC/laptop and an application server;…The user interface allows a user to access various screens to manage their account, view their sleep and environmental data, and sleep advice delivered from the advice engine [0146].)
determining that the target subject followed the recommendation; (Shouldice discloses the advice engine can generate personalised advice for the user based on the user's sleep patterns, changes in sleep patterns, journal entries and a personal profile …if the user does not comply with the advice or the issue is no longer detected then the system will enter a probation period for a number of days and the advice may resume addressing this problem as before…if the user complies with advice then a reward policy may be implemented [0520]. The advice is interpreted as the recommendation.)
and determining that following the recommendation resulted in the target outcome being achieved. (Shouldice discloses once the sound/music is played to the user at the initial rate… the subject begins to match their breathing rate to the provided reference rate. The system then slowly decreases the targeting breathing rate cue to a target breathing rate of 6 breaths/min …The system is switched off if light sleep is detected. The reduction in volume ceases if the user is not detected as falling asleep…it may initiate a sound ramp down after 50 minutes to turn off the sound by 60 minutes. The system may keep checking every 5 minutes between the following times; from ten minutes after target breathing rate has been reached… Upon completion the feature is closed and the application returns to the sleep screen [0451-452]. The determination of the slowing of the breathing rate upon the user listening to sound/music is interpreted as determining if the target behavior is reached.)
Shouldice does not disclose the following limitations met by Matsuoka:
…sensor data from a thermal-imaging sensor… (Matsuoka teaches the camera 118 may include both a regular, visible-light camera function along with a thermal imager. The thermal imager may include a non-contact temperature measurement device that records thermal energy at pixel locations. The thermal imager can detect infrared energy that is emitted and/or reflected by any object or subject within its field-of-view [0119].)
detecting, based on the initial sensor data,…a person,… (Matsuoka teaches the camera 118 can distinguish between situations where a human other than the infant 602 is present in the sleep environment 604. For example, when a parent is cleaning the sleep environment 604, the smart-home system may distinguish between the parent and the infant 602 based on the size of the parent, the thermal signature of the parent, the facial recognition of the parent, the motion patterns of the parent [0133]. Motion sensors, such as the motion sensor in the hazard detector 104, can detect a parent moving across the sleep environment 604 and activate the nursery lighting setting [0173].)
determining a…movement pattern of the target subject…movement pattern of the person; (Matsuoka teaches the camera 118 can distinguish between situations where a human other than the infant 602 is present in the sleep environment 604. For example, when a parent is cleaning the sleep environment 604, the smart-home system may distinguish between the parent and the infant 602 based on the size of the parent, the thermal signature of the parent, … the motion patterns of the parent [0133]. Motion detectors in sleep environment 604 that are part of a security system can detect a motion pattern that indicates someone placing the infant 602 in the crib 606 [0124].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the detection of additional people with a thermal imaging sensor as taught by Matsuoka. This modification would create a system and method capable of creating an optimal sleep environment for a monitored subject (see Matsuoka, ¶ 0003).
Shouldice and Dothie do not teach detecting the movement of an object which is met by Yamamoto:
…removing the object from the physical environment; (Yamamoto teaches an accident prevention system including a unit which recognizes a risk factor present within a monitoring area, the system detecting an infant or a preschool child (a child up to 6 years old) approaching the risk factor and providing assist information to a caretaker of the infant or preschool child such that the caretaker thereof takes an action to direct the infant or preschool child away from the risk factor [0003]. The Examiner interprets removing risks as removing objects from the target subject’s room.)
detecting, based on…sensor data…and an object in the physical environment, …determining a third movement…of the object; wherein the object is separate from the target subject; (Yamamoto teaches in the case where the behavior detecting unit is a sensor, the behavior detecting unit may detect the movement of each toy or picture book which the preschool child is tidying up,… [0064].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the detecting the movement of an object and removing objects of risk that are detected as taught by Yamamoto. This modification would create a method capable of reducing the loads of childcare on women (see Yamamoto, ¶ 0002).
Shouldice, Matsuko, and Yamamoto do not teach determining the distance between people which is met by Orlovsky:
determining, based on the initial sensor data, a…distance between the…subject and the person (Orlovsky teaches the social distancing monitoring system comprising at least one radar sensor array unit covering an area within the protected space and a processor operable to image objects in the covered area within the protected space, identify people in the covered area, count the number of people in the covered area and determine separation between the people in the covered area within the protected space [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the motion sensor detecting the distance between the target and the other people as taught by Orlovsky. This modification would create a method capable of remotely monitoring subjects to determine when intervention is needed (see Orlovsky, ¶ 0009-10).
Regarding Claim 15, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Shouldice further discloses:
a computing device comprising: one or more processors; and a memory coupled to the one or more processors, with instructions stored thereon that, when executed by the processor, cause the processor to (Shouldice discloses the system may include one or more processors. The one or more processors may be in a server(s), a smart device(s) (e.g., mobile phone), a computer(s) or any combination of such processors [0124]. The method may involve with the processor…recording by a microphone a voice sound message of the user and storing data of the voice sound message in a memory coupled to the processor [0092].)
Regarding Claim 18, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Shouldice further discloses:
A non-transitory computer-readable medium with instructions stored thereon that, when executed by one or more computers, cause the one or more computers to perform operations, the operations comprising: (Shouldice discloses such a processing apparatus may include integrated chips, a memory and/or other control instruction, data or information storage medium for carrying out such methodologies. For example, programmed instructions encompassing the methodologies may be coded on integrated chips in the memory of the device or apparatus to form an application specific integrated chip (ASIC). Such instructions may also…be loaded as software or firmware using an appropriate data storage medium [0659].)
Regarding Claim 6, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach limitations as seen in the rejection of Claim 1 above. Shouldice further discloses the following:
the sensor set includes the sound sensor and the method further comprises: detecting, based on the initial sensor data, a sound level in the physical environment; (Shouldice discloses the sound sensor, typically a microphone, could be implemented on the smart device rather than on the bedside unit. Additionally, audio processing can be performed by the app, including monitoring of background sounds (e.g., snoring of the user, traffic noise, other background noises such as the trash truck, car horns etc.—manifesting in the audio signal.) Sound data can be delivered via the app—either through the Smart device's internal speaker or via an external speaker (e.g., connected via Bluetooth, cable etc.) [0240-241].)
detecting, based on the initial sensor data, …sounds of [a person]and one or more other sounds in the physical environment; (Shouldice discloses the system and method measure environmental parameters of the bedroom using sensors such as light, sound,…[0079]. The system can optionally detect characteristic patterns of snoring, snuffling, coughing or breathing difficulties in the non-contact motion movement and respiration patterns. Optionally, sound can be detected by a microphone, and analyzed in conjunction with the non-contact sensor and/or the body temperature measurements [0390].)
filtering out specific sounds; (Shouldice discloses to monitor the user’s sleep environment, the system may utilize any one or more of;…Optional filter to separate the 5 loudest sounds during the night. Annotation of the environmental conditions on hypnogram [0198-0201].)
and determining sound patterns based on the sounds of the target subject, the sounds of the person, and the one or more other sounds in the physical environment; (Shouldice discloses the system can track the breathing pattern, and modulate the sound files such as only at the beginning of the feature and then adjust the sound according to a set pattern (such as described below). The idea is that the user naturally entrains their breathing to the sound pattern, [0408].)
wherein determining the behavior patterns is based on the sound patterns. (Shouldice discloses the method may involve with a processor, analyzing signals from a motion sensor to detect sleep information from the signals [0092].The technology may optionally include a device with any one or more of the following features:… analyze the user's sleep environment (light,… ) analyze the user's sleeping, breathing and heart rate patterns… intelligently detect sleep conditions and gently switch off the sounds, after the user falls asleep…chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep [0071]. This is interpreted as using the sound of the user's environment to determine the behavior pattern.)
Shouldice does not disclose the tracking being of two separate individuals which is met by Matsuoka:
…the target subject,…the person …(Matsuoka teaches the camera 118 can distinguish between situations where a human other than the infant 602 is present…when a parent is cleaning the sleep environment 604, the smart-home system may distinguish between the parent and the infant 602 based on the size of the parent, the thermal signature of the parent, the facial recognition of the parent, the motion patterns of the parent [0133]. The parent is interpreted as the person, and the infant is interpreted as the target subject.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the detection of an infant and a parent as taught by Matsuoka. This modification would create a system and method capable of creating an optimal sleep environment for a monitored subject (see Matsuoka, ¶ 0003).
Regarding Claim 8, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice further discloses:
…and the method further comprises: detecting, based on the initial sensor data, biofeedback that includes a respiratory rate and a heart rate of the target subject; (Shouldice discloses the data from the one or more sensors 130 can be analyzed to determine one or more physiological parameters, which can include a respiration signal, a respiration rate, a respiration pattern or morphology, respiration rate variability,… a heart rate, heart rate variability, movement of the user 210,… [0104].)
wherein determining the behavior patterns is further based on the biofeedback. (Shouldice discloses the technology may optionally include a device with any one or more of the following features:… analyze the user's sleep environment (light, sound, and temperature, as well as humidity and/or air quality)… analyze the user's sleeping, breathing and heart rate patterns (sleep and cardiorespiratory patterns)…intelligently detect sleep conditions and gently switch off the sounds, after the user falls asleep… chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep [0071].)
Shouldice, Matsuoka, Yamamoto do not teach the motion sensor being a radar which is met by Orlovsky:
wherein the motion sensor is radar (Orlovsky teaches a radar system configured and operable to scan the subjects remotely in an anonymous manner and to analyze electromagnetic radiation reflected from the subjects so as to obtain required parameters [0051].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the motion sensor being a radar as taught by Orlovsky. This modification would create a method capable of remotely monitoring subjects to determine when intervention is needed (see Orlovsky, ¶ 0009-10).
Regarding Claim 14, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach limitations as seen in the rejection of Claim 1 above. Shouldice further discloses the following:
providing the initial sensor data as input to a trained machine-learning model; and outputting, using the trained machine-learning model, the recommendation for achieving the target outcome. (Shouldice discloses that the advice, which is generated by one or more processors of the system, can be designed to inform the users of the benefits of good sleep habits, best environmental conditions for sleep, and daily activities that help sleep. It delivers credible and insightful information so as to assist the user's sleep and keep the user engaged with the overall system. The system may implement a learning classifier, such as using Bayesian methods and/or a decision tree, in order to tailor advice to the individual patterns of the user, a local population, or a global population of system users [0503].)
Claims 2, 13, 16, and 19 are rejected under 35 USC § 103 as being unpatentable over Shouldice, Matsuoka, Yamamoto, and Orlovsky in view of Jeon et al. (Jeon et al. Correspondence between Parents' and Adolescents' Sleep Duration. Int J Environ Res Public Health. 2022 Jan 18).
Regarding Claim 2, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach limitations as seen in the rejection of Claim 1 above. Shouldice further discloses the following:
wherein the recommendation is a first recommendation (Shouldice discloses the technology… may chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep [0071].)
and the method further comprises generating, by the computer, a second recommendation based on the behavior patterns to achieve a target outcome for the person; (Shouldice discloses the technology…may intelligently detect sleep conditions …chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep. These customized advice “nuggets” are designed to help the person sleep better and may be based on clinical research [0071-75]. As shown in FIG. 1, in one view the system can be divided conceptually in three categories or stages—e.g., providing sleep assistance (by guiding the user towards relaxation) in stage A, sleep data recording and analysis in Stage B, and providing sleep recommendations and coaching in Stage C. The interconnection between these stages may be understood with reference to FIG. 2 showing a progression of sleep data. Initially collected by the various sensors from the user in the “capture” stage, the data is processed, during the “crunch” stage. During this processing various characteristics and trends in the data, sleep characteristics and patterns are identified. On the basis of these features and trends, the proposed system and method provide recommendations and coaching to the user, in the “deliver” stage (see, e.g., FIG. 36) [0211].)
the user device is associated with the person and further includes: (Shouldice discloses FIG. 7 shows a logic outline of a web server/cloud and of its data links with the smart device app or PC/laptop and an application server;…The user interface allows a user to access various screens to manage their account,…[0146].)
Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach the determination of how long the person is asleep as compared to the target which is met by Jeon:
a comparison of one or more sleeping times of the target subject and one or more sleeping times of the person; and the second recommendation (Jeon teaches the correspondence or agreement of the adolescents’ sleep duration with the father’s sleep duration was a slight agreement (Kappa = 0.064, p = 0.031), while the correspondence with the mother’s sleep duration was a fair agreement (Kappa = 0.213, p < 0.001) (p. 7, ¶ 0007). This is interpretted as comparing the parents' sleep duration to the child's sleep duration.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate comparing the length of time the user is asleep to the target subject as taught by Jeon. This modification would identify the factors affecting the sleep of both parties to improve seep duration and quality (see Jeon, p. 2, ¶ 0003-5).
Regarding Claim 13, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice further discloses:
the user interface is a first user interface and the method comprises: receiving the initial sensor data associated with the person that identifies a length of time when the person is asleep; (Shouldice discloses “Sleep Efficiency” provides a metric of how well a person has slept. This may be understood as working out the percentage of time spent in bed asleep each night. If a person spends 8 hours in bed, but only 4 of those hours are spent asleep, then the sleep efficiency may be very low at 50% [0035]. Fig 20 displays data received from sensors of a user's sleep status.)
and providing a second user interface to the person that includes the length of time when the person is asleep… (Shouldice discloses displaying the time awake and sleeping on the user interface in Fig. 24.)
Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach the determination of how long the person is asleep as compared to the target which is met by Jeon:
length of time when the person is asleep as compared to when the target subject is asleep. (Jeon teaches the correspondence or agreement of the adolescents’ sleep duration with the father’s sleep duration was a slight agreement (Kappa = 0.064, p = 0.031), while the correspondence with the mother’s sleep duration was a fair agreement (Kappa = 0.213, p < 0.001) (p. 7, ¶ 0007). This is interpreted as comparing the parents' sleep duration to the child's sleep duration.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate comparing the length of time the user is asleep to the target subject as taught by Jeon. This modification would identify the factors affecting the sleep of both parties to improve seep duration and quality (see Jeon, p. 2, ¶ 0003-5).
Regarding Claim 16, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 15 above. Shouldice further discloses the following:
the user interface further includes… (Shouldice discloses FIG. 7 shows a logic outline of a web server/cloud and of its data links with the smart device app or PC/laptop and an application server;…The user interface allows a user to access various screens to manage their account, view their sleep and environmental data, and sleep advice delivered from the advice engine [0146].)
Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach the determination of how long the person is asleep as compared to the target which is met by Jeon:
a comparison of one or more sleeping times of the target subject and one or more sleeping times of the person (Jeon teaches the correspondence or agreement of the adolescents’ sleep duration with the father’s sleep duration was a slight agreement (Kappa = 0.064, p = 0.031), while the correspondence with the mother’s sleep duration was a fair agreement (Kappa = 0.213, p < 0.001) (p. 7, ¶ 0007). This is interpreted as comparing the parents' sleep duration to the child's sleep duration.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate comparing the length of time the user is asleep to the target subject as taught by Jeon. This modification would identify the factors affecting the sleep of both parties to improve seep duration and quality (see Jeon, p. 2, ¶ 0003-5).
Regarding Claim 19, this claim recites limitations that are substantially similar to those recited in Claim 16 above; thus, the same rejection applies.
Claims 3, 17, and 20 are rejected under 35 USC § 103 as being unpatentable over Shouldice, Matsuoka, Yamamoto, and Orlovsky in view of Jeon, further in view of Mettler May et al. (US 20190009133 A1).
Regarding Claim 3, Shouldice, Matsuoka, Yamamoto, Orlovsky, and Jeon teach the limitations as seen in the rejection of Claim 2 above. Shouldice further discloses the following:
responsive to determining that the target behavior was achieved, updating the behavior patterns based on subsequent sensor data; (Shouldice discloses (5) Probation: If during an awareness or advice phase the user replies with a negative feedback for a number of times or the issue is not detected anymore, the user moves to a probation phase, where it can stay for a small number of days. From here, the issue may arise again, therefore the system will get back to where it left, or the issue may disappear completely, and have the system back to regular phase. This stage allows the advice engine to make sure that newly established environmental conditions and behaviors are maintained and successfully implemented as the user's new habit [0551-556]. The changed environmental conditions and behaviors are interpreted as the new behavior patterns.)
Shouldice, Matsuoka, Yamamoto, Jeon, and Orlovsky do not teach updating the target outcome which is met by Mettler May:
and updating the target outcome based on the behavior pattern... (Mettler May teaches when the training goal is completed…new baseline data is generated and the training goal is updated. The figure also illustrates the acceleration of the learning curve provided by an update to models and augmentations, etc. As the model parameters are tracked and incrementally updated, the training goals and associated augmentations drive the learning process to achieve best efficiency [0488].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate updating the target in response to the updated data as taught by Mettler May. This modification would create a method for automatically determining and updating personalized goals for optimal performance (see Mettler May, ¶ 0016).
Regarding Claims 17 and 20, these claims recite limitations that are substantially similar to those recited in Claim 3 above; thus, the same rejection applies.
Claims 4 and 10-12 are rejected under 35 USC § 103 as being unpatentable over Shouldice, Matsuoka, Yamamoto, and Orlovsky in view of Dothie et al. (US 20110015467 A1).
Regarding Claim 4, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach limitations as seen in the rejection of Claim 1 above. Shouldice further discloses the following:
…notifying … that the recommendation was not followed. (Shouldice discloses if an issue is identified, the user moves to a precaution advice stage 4006 for a period of time (e.g., maximum of two days)… If the issue goes away, they move back to the sleep assessment phase. If the issue remains, the Sleep Advice phase becomes more active. This may last for a subsequent period of time (e.g., around 3-5 days, depending on the detected condition and the content available). If a positive (getting better) or negative (getting worse) trend is seen, the user may also receive trend feedback in a trend stage 4009. In some cases, the process may advance from the advice stage 4008 to the probation phase 4010 if the device detects that the previously detected issue is fixed or no longer detected. Otherwise, the will continue or move back to advice phase where further or secondary advice suggestions may be generated [0517-518].)
Shouldice does not disclose determining if the recommendation was followed and the notification going to a third party which is met by Dothie:
determining if the recommendation was followed; and responsive to determining that the recommendation was not followed (Dothie teaches the record of these interactions and the sensor data metrics are checked to ensure that the carer is carrying out any behavioural program correctly. It may alternatively or additionally provide one or more updated recommendations for behavioural programs and/or actions if lack of compliance… is detected [0061]. When the carer is carrying out a chosen behavioural program, the metrics are checked to ensure that the carer is carrying out the program correctly, ensuring compliance [0060].)
…notifying a third party… (Dothie teaches this record can be used by the carer or given to a sleep specialist or clinician to examine [0063]. The system is additionally able to provide a printed or electronic record of the child's sleep data and the actions taken by the carer. The format of the data is such as to provide a clear picture of the child's sleep problems. Such a record can be taken to a sleep specialist or doctor who can use the data to understand the child's sleep problems and treat the child as appropriate [0062].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome, and determining if the recommendation was followed as disclosed by Shouldice to incorporate notifying a third party as taught by Dothie. This modification would create a method capable of addressing identified sleep problems to provide benefits for a child and their family (see Dothie, ¶ 0005).
Regarding Claim 10, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach the following limitations met by Dothie:
prior to receiving the initial sensor data, providing a user interface with a request for user preferences about the target outcome, wherein the target outcome is defined based on the user preferences. (Dothie teaches at the first stage, the carer is requested to set a target for the child's sleep. In a program of reset bedtimes, the carer chooses (S101) a desired bedtime for the child that is suitable for the child's age and fits with the carer's lifestyle. The system suggests suitable times based on typical data for children of similar ages, for example from that given in Table 8. The target for the behavioural program is then set by the processing unit to be that the child goes to bed at the desired time and sleeps within 10 minutes. Once a suitable target has been set, the system designs a personal behavioural program for the particular child [0129-130].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome, and determining if the recommendation was followed as disclosed by Shouldice to incorporate the parent setting goals for their child before receiving sensor data as taught by Dothie. This modification would create a method capable of addressing identified sleep problems to provide benefits for a child and their family (see Dothie, ¶ 0005).
Regarding Claim 11, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice further discloses:
receiving subsequent sensor data for a subsequent time period; (Shouldice discloses the method may include accessing measured data representing user movement detected by a movement sensor. The method may include processing the measured data to determine sleep factors with features derived from the measured data. The method may include accessing measured environmental data representing ambient sleep conditions [0117].)
Shouldice does not disclose determining actions that will lead to arousal which is met by Dothie:
determining, based on a comparison of the subsequent sensor data to the behavior patterns, that an action is likely to precipitate a nighttime arousal or a naptime arousal; and providing a warning that the action is likely to precipitate the nighttime arousal or the naptime arousal. (Dothie teaches if the temperature in the room is found to drop below recommended levels for significant parts of the night, the system will recommend increasing the temperature by using a heater. Other recommendations are based on correlations between different sensor data. For example, if the child is typically found to wake at a certain time from the movement data and that time corresponds to the onset of dawn as calculated from the light data, the system will alert the carer to the fact that the child may be being woken by bright light. In this case the system may recommend heavier curtains for the child's room [0122].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate determining actions that will result in an arousal and warning of the action as taught by Dothie. This modification would create a method capable of addressing identified sleep problems to provide benefits for a child and their family (see Dothie, ¶ 0005).
Regarding Claim 12, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice further discloses:
determining behavior patterns of a set of sleep events of the [user] based on the initial sensor data. (Shouldice discloses the method may involve with a processor, analyzing signals from a motion sensor to detect sleep information from the signals [0092]. The technology may optionally include a device with any one or more of the following features:… analyze the user's sleep environment (light, sound and temperature, as well as humidity and/or air quality)… analyze the user's sleeping, breathing and heart rate patterns (sleep and cardiorespiratory patterns)… intelligently detect sleep conditions and gently switch off the sounds, after the user falls asleep…chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep [0071].)
Shouldice does not disclose providing a different recommendation to the user than the target, which is met by Dothie:
wherein the recommendation is provided to the target subject and the person and the method further comprises: (Dothie teaches the method comprises sending S1440 said one or more recommendations from said processing means to a portable user interaction device, for example a portable user interaction device as described with reference to FIG. 6, for presenting the recommendation(s) to the carer via a display [0086]. Any negative changes in sleep behaviour must be sustained over a long period of time of typically at least two weeks before the system will alert the parents and recommend a behavioural therapy [0153]. The Examiner interprets the target subject as being the child, and the person is interpreted as the parent. Furthermore, the recommendation is provided to the child through the parent carrying out the recommendation.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate determining the recommendations to provide the user as taught by Dothie. This modification would create a method capable of addressing identified sleep problems to provide benefits for a child and their family (see Dothie, ¶ 0005).
Claims 5 and 7 are rejected under 35 USC § 103 as being unpatentable over Shouldice, Matsuoka, Yamamoto, and Orlovsky in view of Youngblood et al. (US 20200077942 A1).
Regarding Claim 5, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice further discloses:
transmitting information about the behavior patterns and the recommendation to a…server (Shouldice discloses initially collected by the various sensors from the user in the “capture” stage, the data is processed, during the “crunch” stage. During this processing various characteristics and trends in the data, sleep characteristics and patterns are identified [0231]. The data is captured by the sensors of the BeD device (bedside unit 3000). The data is transmitted to the processor of the SmD (smart device 3002) [0253].)
Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach sending data to an electronic medical record which is met by Youngblood:
…incorporation into an electronic medical record that is associated with the target subject (Youngblood teaches the user profile 736 stores stress reduction and sleep promotion system preferences and information about the user, including…age, weight, height, gender, medical history (e.g., sleep conditions, medications, diseases), fitness…, sleep goals, stress level, and/or occupational information (e.g., occupation, shift information). The medical history includes caffeine consumption, alcohol consumption, tobacco consumption, use of prescription sleep aids and/or other medications, blood pressure, restless leg syndrome, narcolepsy, headaches, heart disease, sleep apnea, depression,… [0136].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate updating transmitting data into an electronic medical record as taught by Youngblood. This modification would create a system capable of utilizing user-specific data to improve sleep (see Youngblood, ¶ 0003-5).
Regarding Claim 7, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice Yamamoto, and Orlovsky do not teach the following limitations met by Matsuoka:
…the target subject… (Matsouka teaches the monitoring system may use the monitoring of an infant or child as an example [0118].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the detection of an infant as taught by Matsuoka. This modification would create a system and method capable of creating an optimal sleep environment for a monitored subject (see Matsuoka, ¶ 0003).
Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach sending data to an electronic medical record which is met by Youngblood:
the sensor set includes the motion sensor and the motion sensor is part of an activity tracker that is worn… (Youngblood teaches the movement sensor 716 is an accelerometer and/or a gyroscope. In one embodiment, the accelerometer and/or the gyroscope are incorporated into a wearable device (e.g., FITBIT, APPLE WATCH, SAMSUNG GALAXY WATCH, actigraph) [0124].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate the use of an activity tracker to detect motion as taught by Youngblood. This modification would create a system capable of utilizing user-specific data to improve sleep (see Youngblood, ¶ 0003-5).
Claim 9 is rejected under 35 USC § 103 as being unpatentable over Shouldice, Matsuoka, Yamamoto, and Orlovsky in view of Arends et al. (Arends, Johan B A M. “Movement-based seizure detection.” Epilepsia vol. 59 Suppl 1 (2018): 30-35).)
Regarding Claim 9, Shouldice, Matsuoka, Yamamoto, and Orlovsky teach the limitations as seen in the rejection of Claim 1 above. Shouldice, Matsuoka, Yamamoto, and Orlovsky do not teach the analysis of sleep behaviors for the determination of seizures which is met by Arends:
wherein determining the behavior patterns includes determining attributes associated with at least one seizure event selected from the set of a time between multiple seizures, verbalizations that occur during the seizure event, movements of the target subject during the seizure event, sounds of the target subject during the seizure event, and combinations thereof. (Arends teaches a critical review and comment on the use of movement detection in epileptic seizures. The detection of rhythmic movement components, such as the clonic part of tonic–clonic seizures, is essential in all seizure detection based on movement sensors. Of the many available movement sensor types, accelerometric sensors are used most often (p. 1, ¶ 0001). [M]ore experimental, analysis of the semiology of the various types of motor seizures. This requires a special setup but may reveal unusual seizure patterns, not only limited to elementary movement patterns—myo(clonic), tonic—but also including more complex hyperkinetic and other stereotyped movements (p. 2, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing sensor data, determining behavior patterns of sleep events, and providing a generated recommendation to achieve a target outcome as disclosed by Shouldice to incorporate determining attributes associated with a seizure as taught by Arends. This modification would create a method capable of detecting motor seizures and providing great insight into seizure patterns to identify drug therapies (see Arends, p. 5, ¶ 0010).
Response to Arguments
Regarding the rejections under 35 USC 112(b) to Claims 1-4, Applicant’s amendments have been considered, and the rejection has been withdrawn.
Regarding the rejections under 35 USC 101 to Claims 1-20, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues Claim 1 advantageously addresses the problem of trying to improve target outcomes for a target subject. For example, both a baby and a caregiver's sleep suffer when the baby has frequent nighttime wakeups. Claim 1 recites a situation where an object is determined to be causing the problem: "determining behavior patterns of a set of sleep events" and "generating, by the computer, a recommendation...wherein the recommendation includes removing the object from the physical environment." After providing the recommendation to remove the object from the physical environment, claim 1 recites "determining that the target subject followed the recommendation." This is a practical application of the technology that results in "determining that following the recommendation resulted in the target outcome being achieved." The examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement." Kim, C. (2025, August 4). "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101." United States Patent and Trademark Office (see Remarks, page 3).
Regarding (a), Examiner respectfully disagrees. The instant claims to not provide an improvement to technology, but rather use additional elements in an “apply it” manner to carry out an abstract idea. The problem at hand is sleep management and the solution to the problem is rooted in an improvement to the abstract idea itself and not a technical failure of a computer system. The additional elements can best be characterized as tools to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples., v.”).
Further, simply providing a recommendation does not result in the object being removed. The practical application would only be realized if the object causing the issue with the behavior/sleep patterns is removed. The subsequent steps following the generation of the recommendation is to determine that the target subject followed the recommendation, and if so, that it resulted in the target outcome being achieved. If the target subject did not follow the recommendation, then the target outcome would not be achieved due to the recommendation.
There are no details claimed in how the recommendation is derived, only that it is “based on the behavior patterns”. Because of this, it does not appear there is an improvement in how to generate the recommendation, but instead the recommendation is simply the result of applying the behavior patterns to a computer. There are also no details regarding the determination that the recommendation was followed or that the target outcome was achieved as a result. After the recommendation, there are no subsequent steps of monitoring or detecting the behavior and movement pattern to see the effectiveness of removing the object (the recommendation).
Applicant argues the statement under MPEP 2106.04(d)(2) and that Claim 1 recites a method that is used to treat a disease or medical condition and therefore integrates the exception into a practical application. This is further supported by paragraph [0012] of the specification, which states that the system "is useful for target subjects with a variety of health conditions including special needs, mental health struggles, athletes, the elderly, people with jobs that require a well-rested state (e.g., pilots, air-traffic controllers, etc.), and people with medical issues." (Emphasis added) (p. 4).
Regarding (b), Examiner respectfully disagrees. As there is no positively recited administration step of the treatment, but only “recommending a treatment”, the claim does not qualify as a prophylaxis step under Step 2A Prong 2.
In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.
Regarding the rejections under 35 USC 102/103 to Claims 1-20, Applicant’s arguments have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made, rejecting the independent claims over Shouldice in view of Matsuoka, Yamamoto, and Orlovsky as per the rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681