DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12 February 2026 has been entered.
Status
This First Action Final Office Action is in response to the communication filed on 12 February 2026. Claims 6 and 9-20 have been canceled currently or previously, claims 1-4 and 8 have been amended, and no new claims have been added. Therefore, claims 1-5 and 7-8 are pending and presented for examination.
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 .
Response to Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s Remarks allege that “Claim 1 is objected to because” a previously added phrase was incorrectly edit marked (Remarks at p. 5); however, the claim was not objected to – the error was noted, and how the markings impacted the amending was noted for clarity on the record. The Examiner notes appreciating Applicant’s candor regarding the earlier mistake.
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment does not overcome the prior art rejection(s) under 35 USC §§ 102 or 103; therefore, the Examiner maintains the rejection(s) as below.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Examiner’s Note
The Examiner notes that dependent claim 8 recites “receiving information from a first user of the plurality of users concerning at least one of the environmental conditions that adversely affects the users' sleep”, where “the users’ sleep” may be referring to a single or any one user in the user group indicated at parent claim 1, this may also refer to the entirety of the group of users indicated at parent claim 1 (all having their sleep disrupted or affected), or this may refer to the first user as introduced at claim 8 – as a designation of a single member of the group. This appears to be a breadth issue (and therefore no rejection under § 112 for indefiniteness) and is being interpreted as being any user that has sleep adversely affected.
The Examiner notes that claim 5 now recites “providing a notification to another in the group of users who do not share the identified common sleep pattern to enable proactive mitigation based on patterns observed in the group”, where “to enable proactive mitigation based on patterns observed in the group” is merely the desired or expected intent or result – no proactive mitigation is required other than the notification to others and any enabling that may be considered required is performed by the notification. See MPEP § 2103(I)(C) “Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation… [including] (A) statements of intended use or field of use, including statements of purpose or intended use in the preamble”. See also, e.g. In re Collier, 158 USPQ 266, 267 (CCPA 1968) (where the court interpreted the claimed phrase “a connector member for engaging shield means” and held that the shield means was not a positive element of the claim since “[t]here is no positive inclusion of ‘shield means’ in what is apparently intended to be a claim to structure consisting of a combination of elements” and where the court interpreted the claimed phrase “said ferrule-forming member being crimpable onto said shield means” and held that the shield means was not a positive element of the claim since “[t]here is no positive inclusion of ‘shield means’ in what is apparently intended to be a claim to structure consisting of a combination of elements.... “[t]he ferrule or connector member is crimpable but not required, structurally, to be crimped .... These cannot be regarded as structural limitations and therefore not as positive limitations in a claim directed to structure. They cannot therefore be relied on to distinguish from the prior art.”).
Therefore, the phrase “to enable proactive mitigation based on patterns observed in the group” may be given little if any patentable weight.
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 1-5 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to methods, which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a computer-implemented method for managing sleep for a group of users, comprising: monitoring, using at least one monitoring device that is configured to monitor sleep conditions and environmental conditions during the sleep of each user of the group of users to thereby automatically collect monitoring data, the monitoring device having a thermometer, a microphone, a light sensor, and a radar transducer for respectively collecting room temperature, sound information of each user's sleeping environment, light levels within each user's sleep environment, and movement of each user during the sleep in a non-contact manner, the sleep of each user being represented by the monitoring data that describes the sleep conditions and the environmental conditions; analyzing the monitoring data and identifying at least one sleep pattern based on the sleep conditions and the environmental conditions for each user; aggregating the monitoring data from the plurality of monitoring devices associated with the group of users over a communication network; identifying, via the communication network connected with the at least one monitoring device and using at least one computer processor configured to process the at least one sleep pattern for each user, at least one common sleep pattern shared by two or more users of the group by detecting shared sleep conditions or environmental factors from the aggregated data; and providing, via the communication network to an electronic device associated with at least one user of the group, a notification based on the at least one common sleep pattern to enable the electronic device to display a recommendation to improve sleep quality for the at least one user.
The dependent claims (claims 2-5 and 7-8) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the sleep pattern as a shared sleep disruption, a similar schedule or bedtime, or a common sleep disorder so as to provide a recommendation or notification to at least one of the users in the group (claims 2-5), the users are members of the same social network and connected via the network (claim 7), and/or receiving information from a first user relating to at least one environmental condition that adversely affects the user’s sleep so as to provide a recommendation to mitigate potential/possible effects (claim 8).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of monitoring sleep or predicted/potential sleep disruptors so as to, for example, identify common patterns or provide a recommendation or notification; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination).
This idea is within at least the mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) and/or certain methods of organizing human activity (e.g. … commercial or legal interactions such as … advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) groupings of subject matter. In the instant case, the “collecting” step is observation of a person and their environment, and the analyzing data and identifying a sleep pattern are within the evaluation, judgment, and/or opinion aspects of mental processes that (aside from the device recitation to perform the activities) can be performed in the human mind and/or by use of pen and paper or other such tools. And the now added indication of providing a recommendation is within the certain methods of organizing human activity since it is what people do to or for each other – provide recommendations of activities, environments, and/or products, for example, in order to help themselves or others sleep better.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are that the methods are computer-implemented, using a monitoring device to automatically collect monitoring data, the monitoring device having a thermometer, a microphone, a light sensor, and a radar transducer, and the identifying and providing being via a/the communication network connected with the at least one monitoring device and using at least one computer processor configured to process the data. These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
Applicant specification pages 5-7, at the roll-over paragraphs of pp.5-6 and 6-7 (¶¶ 0033-0034 as published), indicates that a “monitor device can have an architecture of a general purpose computer” (at p. 6, ll. 3-4; ¶ 0033 as published), that “any suitable” monitoring device may be used (at p. 6, l. 20; ¶ 0034 as published) and such a device “can broadly encompass all monitoring devices or systems” that can sense the data (at p. 6, ll. 22-23; ¶ 0034 as published), and the communication is accomplished via “communication with Bluetooth and WIFI devices, including a Home Area Network 22, which in turn is connected to the Internet 24. The Bluetooth/WIFI communication circuit 16 includes the use of all types of wireless communication devices and techniques, such as, but not limited to Bluetooth, WIFI, and Zigbee” (at p. 6, ll. 8-11; ¶ 0033 as published). The recitation of automatically collecting data, merely indicates implementation by the general purpose device, and the monitoring device having a thermometer, a microphone, a light sensor, and a radar transducer merely designates generic sensors as components of the device. As such, the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. As indicated above, the conveyed conception at Applicant pp. 5-7 (¶¶ 0033-0034 as published) indicates that the computers, monitor devices, and communication networks are envisioned as including general purpose machines and technology.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore, the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Shouldice et al. (U.S. Patent Application Publication No. 2020/0009349, hereinafter Shouldice) in view of Johnson et al. (U.S. Patent No. 9,993,166, hereinafter Johnson) and in further view of Brunner (U.S. Patent Application Publication No. 2017/0249434).
Claim 1: Shouldice discloses a computer-implemented method for managing sleep for a group of users, comprising:
monitoring, using at least one monitoring device that is configured to monitor sleep conditions and environmental conditions during the sleep of each user of the group of users to thereby automatically collect monitoring data, the monitoring device having a thermometer, a microphone, a light sensor, … light levels within each user's sleep environment, and movement of each user during the sleep in a non-contact manner, the sleep of each user being represented by the monitoring data that describes the sleep conditions and the environmental conditions (see Shouldice at least at, e.g., ¶¶ 0071, “Some versions of the technology may optionally include a device with any one or more of the following features:” 0072, “It may sit by the user's bedside and unobtrusively record and analyze the user's sleep environment (light, sound and temperature, as well as humidity and/or air quality)”, 0073, “monitor and analyze the user's sleeping , breathing and heart rate patterns ( sleep and cardiorespiratory patterns)”, 0079, “The system and method measure environmental parameters of the bedroom using sensors such as light, sound, temperature, humidity and/or air quality. The proposed system and method also deliver customised personal advice to help improve the user's sleep based on personal sleep data, trended data, de-identified population data, bedroom environment data and external environmental data”, 0086, “the processor may be further configured to determine a measure of sleep or wake of the user, with the movement sensor”, 0097, “a processor adapted to access measured data representing user movement detected by a movement sensor” – the Examiner noting that the movement monitoring in Shouldice is indicated as being performed by the bedside device, and therefore non-contact, see also 0196-0198 as further describing the environmental monitoring; citation hereafter by number only);
analyzing the monitoring data and identifying at least one sleep pattern based on the sleep conditions and the environmental conditions for each user (0071-0073, as cited above, 0074, “It may actively assist the user to get to sleep and stay asleep, by way of generating calming sounds to help adjust the user's breathing and ease the user to sleep. It may intelligently detect sleep conditions and gently switch off the sounds, after the user falls asleep”, 0075, “It may chart the user's sleep patterns and send personalized recommendations via text or email to help improve the user's sleep. These customised advice “nuggets” are designed to help the person sleep better and may be based on clinical research”);
aggregating the monitoring data from the plurality of monitoring devices associated with the group of users over a communication network (0206, “Apart from monitoring the bedroom environment, the system may have knowledge of the time of year and the specific location of the user, and be able to link to geographic and season-adjusted weather conditions, ask the user targeted questions, receive user's answers by way of a keyboard, touch sensitive pad or speech recognition software, and cross correlate all the collected information to the sleep parameters and trends detected for the individual consumer. Statistical data from general population and/or other users may also be used”, 0445, “the audio and/or video cues can be provided to the user based not on a calculated, but on a predetermined rate, based on statistical data from this user, from other users of from statistical data obtained from the general population with no association to the device”, 0586, “the system may optionally aggregate data from other sources, such as environmental data (e.g., allergy alert, humidity, air quality and related parameters). These data can be obtained from physical wired or wireless sensors, or via ‘online’ services such as local, regional and trending sources of weather, air pollution, and allergy (e.g., pollen) conditions data”);
identifying, via the communication network connected with the at least one monitoring device and using at least one computer processor configured to process the at least one sleep pattern for each user of the group by detecting shared sleep conditions or environmental factors from the aggregated data (0071-0075, as cited above, 0076 “It may provide expert advice articles and access to moderated forums” , 0077, “It may communicate with the user's Smartphone to use its processing power for various levels of data processing, as well as to deliver the information to the user”, 0081, “versions of the present technology may be implemented as medical devices used in the diagnosis, amelioration, treatment, and/or prevention of sleep and/or respiratory disorders and may have one or more of improved comfort, cost, efficacy, ease of use and manufacturability”, 0205, “The technology provides customized rather than generic advice based on data from the user, local environment and other sources. A larger number of different types of parameters can be analysed, allowing for a much broader picture of the user's sleep health to be assembled—e.g., sleep interruptions could be linked to allergy based on seasonal factors/local weather forecast”, 0206, “Apart from monitoring the bedroom environment, the system may have knowledge of the time of year and the specific location of the user, and be able to link to geographic and season-adjusted weather conditions, ask the user targeted questions, receive user's answers by way of a keyboard, touch sensitive pad or speech recognition software, and cross correlate all the collected information to the sleep parameters and trends detected for the individual consumer. Statistical data from general population and/or other users may also be used”, 0445, “the audio and/or video cues can be provided to the user based not on a calculated, but on a predetermined rate, based on statistical data from this user, from other users of from statistical data obtained from the general population with no association to the device”, 0536, “These issues may be defined in a class implementation or list and may be mapped to a database so that the system and repository can share the same identification for each issue. Each issue may have particular detection methods for analysing the presence of the issue and evaluating relevance as well as content for messages to communicate the issues to users”, 0586, “the system may optionally aggregate data from other sources, such as environmental data (e.g., allergy alert, humidity, air quality and related parameters). These data can be obtained from physical wired or wireless sensors, or via ‘online’ services such as local, regional and trending sources of weather, air pollution, and allergy (e.g., pollen) conditions data”); and
providing, via the communication network to an electronic device associated with at least one user of the group, a notification based on the at least one common sleep pattern to enable the electronic device to display a recommendation to improve sleep quality for the at least one user (0076 “It may provide expert advice articles and access to moderated forums” , 0077, “It may communicate with the user's Smartphone … to deliver the information to the user”, 0115, “generation of an advice message may include triggering a push notification”, 0151, “The advice engine is able to draw from the user's history such as previous sleep histories, previous advice given to the user, pre-sleep questionnaire to tailor the advice and generate the most appropriate advice for the user. The advice is then relayed to the user. One such embodiment of this delivery method is a push notification service utilizing the smart devices operating system).
Shouldice, however, does not appear to explicitly disclose that the movement sensor is a radar transducer, and the at least one common sleep pattern [is] shared by two or more users. Johnson, though, teaches “a user monitoring device system with a user monitoring device that includes one or more microphones, a transmitter and sensors to determine air quality, sound level/quality, light quality, ambient temperature and humidity near the user. The transmitter serves as a communication system. A radar apparatus or system is configured to detect a user's movement information. The radar apparatus or system and the monitoring system configured assist to determine at least one of: user sleep information and sleep behavior information, or user respiration information” (Johnson at column:lines 3:60-4:2; citation hereafter by number only), where “In one embodiment system 10 monitors sleep in order to detect a variety of psychiatric disorders including but not limited to: (i) continuous recurrent nightmares in response to antidepressant medications in depressed individuals; (ii) nightmares are also observed in schizophrenic patients and acute schizophrenic episodes are often preceded of a period of frequent nightmares; (iii) individuals with a posttraumatic stress disorder may also experience recurrent nightmares about the traumatic event; (iv) eating abnormalities; (v) violent behaviors during sleep, and the like” (Johnson at 55:28-38). Therefore, the base system and/or methods of sleep monitoring as in Shouldice would be predictably improved or modified by the radar monitoring techniques indicated in Johnson so as to yield the predictable result of using radar detection in addition to the other monitoring in order to detect disorders such as nightmares, PTSD, eating abnormalities, violent sleep behaviors and the like. As such, the Examiner understands and finds that to combine radar movement detection with the other monitoring is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to detect nightmares and/or other possibly related sleep patterns.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the sleep monitoring of Shouldice with the radar detection of Johnson in order to combine radar movement detection with the other monitoring so as to detect nightmares and/or other possibly related sleep patterns.
The rationale for combining in this manner is that to combine radar movement detection with the other monitoring is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to detect nightmares and/or other possibly related sleep patterns as explained above.
Shouldice in view of Johnson, however, does not appear to explicitly disclose the at least one common sleep pattern [is] shared by two or more users. Brunner, though, teaches “the creation of individual health profiles or ‘avatars’ that capture a person's major health domains and that can be used as a surrogate for monitoring health and diagnosing disease , and as a tool to guide decisions and interventions” (Brunner at 0007), including by using simulated sleep studies and sleep logs (Figs. 10-12B) encompassing “sleep experimental data” (Brunner at 0039), where the data is correlated and analyzed as related to groups and their relationships (Brunner at 0049), where “using labels, patterns, symbols, color, size, or other markers according to a known diagnosis or label (e.g. ‘depressed’ versus ‘control’; FIG. 11) allows for exploration of the interpretation of the visual output…. As can be seen in FIGS. 10-12, such visual patterns can then be quantified to assess significance level of various parameters. In FIGS. 10-12, each point or node represents a cluster of patients. As can be seen the data can be segregated into common ‘related’ or ‘sister’ clusters which share one or more common features. Accumulation of multiple clusters may allow the formation of superclusters. In FIG. 10, two large super clusters are formed. Further imposing graphical information on these superclusters demonstrates the segregation between the three types of insomnia (here, each node is labeled with a 1, 2, 3, or 4 representing the three types of insomnia and control). FIG. 11 further qualifies the clusters allowing size to be proportional to the number of depressed subjects in each cluster, allowing a visualization of the depression x insomnia interaction. FIG. 12 explores the relationship with sex or mood.” (Brunner at 0221). Therefore, the base system and/or methods of sleep monitoring as in Shouldice in view of Johnson would be predictably improved or modified by the group analysis techniques indicated in Brunner so as to yield the predictable result of providing (and visualizing) user information as related to group information. As such, the Examiner understands and finds that to identify a sleep pattern common to two or more users is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide (and visualize) user information as related to group information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the sleep monitoring of Shouldice in view of Johnson with the group analysis of Brunner in order to identify a sleep pattern common to two or more users so as to provide (and visualize) user information as related to group information.
The rationale for combining in this manner is that to identify a sleep pattern common to two or more users is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide (and visualize) user information as related to group information as explained above.
Claim 2: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, wherein the common pattern is a shared sleep disruption, the method further comprising: by using at least one computer processor, providing a recommendation on an electronic device user interface for resolving the shared sleep disruption based on the aggregated monitoring data (Shouldice at 0146, “FIG. 7 shows a logic outline … [where a] 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”, 0205-0206, as above, especially 0205 indicating “sleep interruptions could be linked to allergy based on seasonal factors/local weather forecast” and 0206 indicating, “be able to link to geographic and season-adjusted weather conditions, ask the user targeted questions, receive user's answers by way of a keyboard, touch sensitive pad or speech recognition software, and cross correlate all the collected information to the sleep parameters and trends detected for the individual consumer. Statistical data from general population and/or other users may also be used”, 0445, “the audio and/or video cues can be provided to the user based not on a calculated, but on a predetermined rate, based on statistical data from this user, from other users of from statistical data obtained from the general population with no association to the device”, 0245, “FIG. 7 shows a web server logic process … [where a] user interface allows a user to access various screens to manage their account, view their sleep and environmental data, view their personal goals and achievements, their progress against their peers, and sleep advice delivered from the advice engine”, 0535, “In some cases, an advice engine may employ … a graphic user interface (GUI)-based advice display mechanism running one or more Smart Devices”; Brunner at 0009-0019, 0220-0222, as combined above and using the rationale as at the combination above. The Examiner noting that “for resolving … “ explicitly indicates an intended result that may not garner patentable weight – see at least MPEP § 2111.04).
Claim 3: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, wherein the common pattern is a similar sleep schedule or similar bedtime, the method further comprising: by using at least one computer processor, providing a notification to at least one of the two or more users (Shouldice at 0097, “The processor may be configured to control the display of the sleep score and wherein the sleep factors from which the sleep score is based may be include two or more of total sleep time, deep sleep time, REM sleep time and light sleep time, wake after sleep onset time and sleep onset time. In some cases, the features may include time domain statistics and/or frequency domain statistics”, 0111, “one of the determined sleep factor may include a total sleep time for a sleep session”, 0115, “one or more advice messages may comprise a series of advice messages over time consecutively generated upon continued detection of the sleep issue. The measured environmental data may comprise one or more of detected light, detected sound and detected temperature. The sleep factors may comprise one or more of sleep latency, REM sleep time, deep sleep time and number of sleep interruptions. A detected sleep issue may comprise any one or more of a REM time too short condition, a REM time too long condition, a REM time fragmented condition, a Deep sleep time too short condition, a Deep sleep time too long condition and a Deep sleep time fragmented condition”).
Claim 4: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, wherein the common pattern is a common sleep disorder, the method further comprising:
by using at least one computer processor, providing a notification to at least one of the two or more users (Shouldice at 0625, “the advice engine may be configured to recognize “risky sleep” such as sleep that may be indicative of a sleep disorder and/or sleep disordered breathing (SDB) issues”, 0626, triage and sleep issue identification – issue identification indicating that the user is identified as having that particular sleep issue, 0627, “the advice engine can be initiated, such as by a Backend server or other cloud server, and can involve sending a notification (to the app of the SmD or an email) with a link to the user to download a report”, and “detect, for example, either ‘normal sleep’ or ‘risky sleep’ and generate output for user with the classification. The methodology of this process, which may also be referred to as a “risky sleep engine,” may include analysis of input from a set-up profile concerning user responses to risky sleep related questions of a questionnaire. The processing of the triage process may also evaluate any one or more of the following risky sleep indicators: Sleep duration (time asleep); Time in bed; Difference in time to bed; Deep sleep percentage and/or minutes; REM sleep percentage and/or minutes; Sleep efficiency; sleep disruptions, etc. The result of the analysis may be the output report”); and
generating the recommendation from the common sleep pattern identified in the aggregated data (Shouldice at 0205, “The technology provides customized rather than generic advice based on data from the user, local environment and other sources. A larger number of different types of parameters can be analysed, allowing for a much broader picture of the user's sleep health to be assembled—e.g., sleep interruptions could be linked to allergy based on seasonal factors/local weather forecast”, 0206, “Apart from monitoring the bedroom environment, the system may have knowledge of the time of year and the specific location of the user, and be able to link to geographic and season-adjusted weather conditions, ask the user targeted questions, receive user's answers by way of a keyboard, touch sensitive pad or speech recognition software, and cross correlate all the collected information to the sleep parameters and trends detected for the individual consumer. Statistical data from general population and/or other users may also be used”, 0445, “the audio and/or video cues can be provided to the user based not on a calculated, but on a predetermined rate, based on statistical data from this user, from other users of from statistical data obtained from the general population with no association to the device”, 0536, “These issues may be defined in a class implementation or list and may be mapped to a database so that the system and repository can share the same identification for each issue. Each issue may have particular detection methods for analysing the presence of the issue and evaluating relevance as well as content for messages to communicate the issues to users”, 0586, “the system may optionally aggregate data from other sources, such as environmental data (e.g., allergy alert, humidity, air quality and related parameters). These data can be obtained from physical wired or wireless sensors, or via ‘online’ services such as local, regional and trending sources of weather, air pollution, and allergy (e.g., pollen) conditions data”).
Claim 5: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, wherein the common pattern is a common sleep disorder, the method further comprising: by using at least one computer processor, providing a notification to another in the group of users who do not share the identified common sleep pattern to enable proactive mitigation based on patterns observed in the group (Shouldice at 0625, “the advice engine may be configured to recognize ‘risky sleep’ such as sleep that may be indicative of a sleep disorder and/or sleep disordered breathing (SDB) issues” – the term “risky sleep” indicating it is not the same user as has been actually identified, but another user that is considered to be at risk based on other users data, 0626, triage and sleep issue identification, 0627, “the advice engine can be initiated, such as by a Backend server or other cloud server, and can involve sending a notification (to the app of the SmD or an email) with a link to the user to download a report”, and “detect, for example, either ‘normal sleep’ or ‘risky sleep’ and generate output for user with the classification. The methodology of this process, which may also be referred to as a “risky sleep engine,” may include analysis of input from a set-up profile concerning user responses to risky sleep related questions of a questionnaire. The processing of the triage process may also evaluate any one or more of the following risky sleep indicators: Sleep duration (time asleep); Time in bed; Difference in time to bed; Deep sleep percentage and/or minutes; REM sleep percentage and/or minutes; Sleep efficiency; sleep disruptions, etc. The result of the analysis may be the output report”; Brunner at 0020-0030, “monitoring a present … condition of a first subject” at 0020, and as related to groups of users at 0220-0222, as combined above and using the rationale as at the combination above)
Claim 7: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, wherein the group of users are members of a social network and the group of users are connected with the social network via the communication network (Johnson at 7:36-49, the Internet used for communication, 77:56-78:4, “activity-assistant user interface 3300 may be displayed via monitoring device 10, and may allow a user to interact with a voice activated digital assistant. While only one screen of the activity-assistant user interface 3300 is shown, it should be understood that the activity-assistant user interface may include other screens, which provide additional functionality, without departing from the scope of the invention. As shown, activity-assistant user interface 3300 includes a personalized activity panel 3302, an activity feed 3304 that displays activities that have been added, done, and/or recently updated by friends of the user (or members of the user's social graph and/or social network), a search/add bar 3306, and a context panel 3308. Further, context panel 3308 includes a number of input mechanisms 3310 A-C via which a user can input context signals”, Brunner at 0078, contextual data that “may refer to environmental, social, virtual, text, physical, auditory, visual or similar circumstances which define the setting of an event, statement, data or the like”, as combined above and using the rationale as at the combination above.
Claim 8: Shouldice in view of Johnson and in further view of Brunner discloses the method of claim 1, further comprising:
receiving information from a first user of the plurality of users concerning at least one of the environmental conditions that adversely affects the users' sleep (Shouldice at 0072, “record and analyze the user's sleep environment (light, sound and temperature, as well as humidity and/or air quality)”, 0078, “recording of sleep patterns and bedroom environment; offering personalized recommendations to help improve the user's sleep environment and habits” see also 0071-0078 in general as cited above); and
by using at least one computer processor, providing a recommendation to at least one of the remaining users of the plurality of users to mitigate adverse effects of the at least one environmental condition identified from the aggregated monitoring data and received information (Shouldice at 0111, “display a temporal correlation for a plurality of sleep sessions between one or more determined sleep factors and environmental data representing one or more ambient sleep conditions including ambient sound level, ambient light level, ambient temperature level, ambient air pollution level and weather conditions at a location of the user. The processor may be further configured to access weather data based on detecting a location of the apparatus. In some versions, the apparatus may be further configured to generate the temporal correlation for a plurality of sleep sessions between one or more determined sleep factors, one or more input user parameters and one or more ambient sleep conditions, including ambient sound level, ambient light level, ambient temperature level, ambient air pollution level and weather conditions at a location of the user”, 0205-0206, as above, especially 0205 indicating “sleep interruptions could be linked to allergy based on seasonal factors/local weather forecast” and 0206 indicating, “be able to link to geographic and season-adjusted weather conditions, ask the user targeted questions, receive user's answers by way of a keyboard, touch sensitive pad or speech recognition software, and cross correlate all the collected information to the sleep parameters and trends detected for the individual consumer. Statistical data from general population and/or other users may also be used”, also 0206, “the system may have knowledge of the time of year and the specific location of the user, and be able to link to geographic and season-adjusted weather conditions”, 0218, “evaluate other relevant factors, such as user's geographic location or altitude, time of the year etc.” – where location and geographic location indicate a same community; 0445, “the audio and/or video cues can be provided to the user based not on a calculated, but on a predetermined rate, based on statistical data from this user, from other users of from statistical data obtained from the general population with no association to the device”, 0536, “These issues may be defined in a class implementation or list and may be mapped to a database so that the system and repository can share the same identification for each issue. Each issue may have particular detection methods for analysing the presence of the issue and evaluating relevance as well as content for messages to communicate the issues to users”, 0586, “the system may optionally aggregate data from other sources, such as environmental data (e.g., allergy alert, humidity, air quality and related parameters). These data can be obtained from physical wired or wireless sensors, or via ‘online’ services such as local, regional and trending sources of weather, air pollution, and allergy (e.g., pollen) conditions data”).Brunner at 0020-0030, using a normative dataset to complete an incomplete dataset, 0078, contextual data that “may refer to environmental, social, virtual, text, physical, auditory, visual or similar circumstances which define the setting of an event, statement, data or the like”, as combined above and using the rationale as at the combination above).
Response to Arguments
Applicant's arguments filed 12 February 2026 have been fully considered but they are not persuasive.
Applicant first alleges that there was an objection to claim 1 (Remarks at 5); however, there was no actual objection, just an edit marking error that was noted for clarity on the record regarding how that was being handled for examination. Therefore, the argument regarding objection is considered moot and not persuasive.
Applicant then argues the eligibility rejections under § 101 (Remarks at 5-7), asserting that “the claims require specific hardware” and network communication such that the claims allegedly “cannot practically be performed in the human mind” (Id. at 5); however, these aspects of the claims are the elements additional to the abstract idea – they are merely the tools or hardware components used to collect and communicate the data and are analyzed as additional elements at Step 2A, Prong 2 and Step 2B.
Applicant then alleges in relation to Step 2A, Prong 2 that “the claims integrate …. into a specific practical application that improves the technical field of multi-user sleep monitoring systems. The invention solves a technical problem: individual sleep monitors provide isolated data, but users in groups (e.g., families or social networks, per claim 7) experience shared environmental or behavioral influences on sleep that are not detectable individually” (Id. at 6). However, sleep monitoring is not a technical field, and most of medical care is largely based on aggregation of data to suggest or diagnose (e.g., seasonal allergies, lactose intolerance, symptoms of sleep apnea, etc. etc.) answers related to a person. The aggregation, therefore, is part of the abstract idea – it is what people do, mentally recalling what they have seen or heard with others and/or the communications persons have in a relationships with others.
Applicant alleges analogy to “USPTO Example 42”, and then to “AI Examples 47-49”, but there is no apparent reasoning explained – there is merely the allegation of analogy. The Examiner further notes that shortly after Example 42 was introduced (7 January 2019), the Federal Circuit in University of Florida Research Foundation, Inc. v. General Electric Co. 916 F.3d 1363, slip op. 2018-1284 (Fed. Cir. 2019) (on 26 February 2019) indicated that very highly similar claims were ineligible as directed to an abstract idea. Therefore, analogy to Example 42 to indicate eligibility is not generally persuasive. Examples 47-49 relate to artificial intelligence (AI) and machine learning (ML) based claims, but Applicant’s claims do not appear to recite AI or ML – the monitoring, analyzing, and aggregating of claim 1 are not even recited as performed by a computer, much less via AI or ML. Only the identifying step of claim 1 even mentions the use of a computer processor but also does not indicate AI or ML, and the providing is “via a computer network” but also free of any indication of AI or ML. Therefore, analogy to Examples 47-49 does not appear reasonable nor persuasive.
Applicant then argues Step 2B, alleging that “The cited references (Shouldice, Johnson, Brunner) do not disclose or suggest this integrated system” (Id. at 6-7); however, prior art analysis is not applicable for eligibility analysis – the precedent is replete with patents that have overcome the prior art (i.e., they issued as a patent) and have nonetheless been found to be abstract. Applicant also asserts that “Generic computer implementation is not alleged” (Id. at 7); however, the rejection specifically “indicates that the computers, monitor devices, and communication networks are envisioned as including general purpose machines and technology” and supports that allegation by citation to Applicant’s specification. Therefore, this also is not persuasive.
Applicant then argues the prior art rejections under § 103 (current Remarks at 7-8), alleging that “the references fail to teach or suggest the claimed combination, particularly the network-based aggregation to identify common sleep patterns from the specific multi-sensor data and the resulting group-targeted recommendations” (Id. at 7, ¶ 0012) and that “Shouldice and Johnson both focus on individual user monitoring and personalized advice” (Id. at 7, ¶ 0013). Applicant previously admitted that “Brunner teaches group-level analysis to identify common sleep patterns (e.g., insomnia types) across multiple users” (10 June 2025 Remarks at 9, ¶ [0011]), but now alleges in what is apparently a completely contradictory manner, that “Brunner … does not involve … network aggregation of live environmental/sleep data from a group” (current Remarks at 8, ¶ 0014). It does not appear possible to resolve the contradiction of these statements – one or the other would appear to be, on its face, a lie to a tribunal.
Nevertheless, the Examiner notes that Shouldice, as cited above, does aggregate data for recommendations and Brunner (per the citations to Brunner), as combined above and as previously admitted, does in fact “teach[ ] group-level analysis to identify common sleep patterns (e.g., insomnia types) across multiple users” – i.e., a sleep pattern common shared by two or more users.
Applicant then alleges that “[t]here is no teaching, suggestion, or motivation to combine these references to arrive at the claimed invention without impermissible hindsight” (current Remarks at 8, ¶ 0015) since “[t]he Examiner's rationale (applying ‘known techniques’ for ‘predictable results’) is conclusory and does not identify a specific reason a POSITA would combine them in the claimed manner” (Id.). However, the combination merely identifies that data beyond that of the user (data that Shouldice itself already indicates it can use in aggregate per the citations above), and Brunner specifically indicates (per the citations to Brunner) that this allows for “interpretation of the visual output” (as in Brunner Fig. 11), and “In FIGS. 10-12, each point or node represents a cluster of patients. As can be seen the data can be segregated into common ‘related’ or ‘sister’ clusters which share one or more common features. Accumulation of multiple clusters may allow the formation of superclusters. In FIG. 10, two large super clusters are formed. Further imposing graphical information on these superclusters demonstrates the segregation between the three types of insomnia (here, each node is labeled with a 1, 2, 3, or 4 representing the three types of insomnia and control). FIG. 11 further qualifies the clusters allowing size to be proportional to the number of depressed subjects in each cluster, allowing a visualization of the depression x insomnia interaction. FIG. 12 explores the relationship with sex or mood.” (Brunner at 0221). This is NOT hindsight, it is what Shouldice and Brunner specifically disclose. The reason for combination is NOT conclusory – it is what Brunner indicates as the reason(s) for using the extra data: so as to provide (and visualize) user information as related to group information, as Brunner explains.
Applicant then alleges that “The arguments in the June 10, 2025 Remarks are maintained and supplemented by the amendments” (current Remarks at 8, ¶ 0015); however, the Examiner notes again that Applicant admits in the 10 June 2025 remarks that “Brunner teaches group-level analysis to identify common sleep patterns” and now apparently fully contradicts that admission in alleging that Brunner does not teach what Applicant already admitted Brunner does teach. This issue on its face appears to indicate that Applicant’s arguments are not, or should not be considered, believable or persuasive.
Therefore, the Examiner is not persuaded by Applicant’s argument(s).
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THI7-8S ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marten (U.S. Patent Application Publication No. 2016/0286327), indicating “Various options may be available to user in order to control and interact with the logged sounds. Reset log element 531 may allow a user to clear previously logged sounds from table 520. Send to mobile element 532 may permit a user to cause one or more logged sounds to have its information and/or recording of the sound transmitted to a mobile device, such as smart phone or tablet computer. Notify authorities element 533 may permit a user to notify the relevant authorities, such as the police department, about all or particular logged sounds. For instance, an email may be created that lists particular logged sounds corresponding to a particular or origination direction or position. For instance, such information may be useful if a user desires to file a notice complaint about a neighbor due to a recurring sound. As an example, the sound logged on May 1, 2015 at 3:31 AM has recurred seven times and is recorded as originating from a southwest direction. This high level of recurrence may prompt the user to want to notify the authorities in order to register a formal noise complaint against a neighbor who has his home in that direction.” (Marten at 0046).
Jantunen (U.S. Patent Application Publication No. 2019/0224443) indicating “receive respective sleep profiles of a plurality of users, each sleep profile comprising recorded sleep phases of a sleep session of a respective user of the plurality of users, receive a target sleep outcome of the plurality of users; and based on the respective sleep profiles and the target sleep outcome, determine one or more respective sleep adjustments for provision to at least one of the respective plurality of users, the respective sleep adjustments comprising stimuli configured to attempt to adjust a respective user's sleep during one or more of the respective user's sleep session and a subsequent sleep session, in an attempt to achieve, at least in part, the target sleep outcome of the plurality of users.” (at Abstract, in part).
CNET, The 3 best ways to track your sleep, CNET.com [online], dated 17 December 2019, downloaded 20 September 2023 from https://www.cnet.com/health/sleep/how-to-track-your-sleep-schedule/, indicating devices and applications used to monitor sleep, compiling the data and causes of common sleep issues.
Acebo et al., Estimating Sleep Patterns with Activity Monitoring in Children and Adolescents: How Many Nights Are Necessary for Reliable Measures?, Sleep, Vol. 22, Iss. 1, January 1999, pp. 95–103, https://doi.org/10.1093/sleep/22.1.95, downloaded 20 September 2023 from https://academic.oup.com/sleep/article/22/1/95/2731704#google_vignette, describing measuring sleep patterns in children and adolescents (at least at 95-96).
Benson et al. (U.S. Patent Application Publication No. 2015/0164409, hereinafter Benson) describes “a sleep system [that] includes a mattress and one or more force sensors embedded within the mattress. The force sensors are positioned within the mattress to sense movement of an occupant of the mattress. The sleep system also includes one or more processors coupled with the one or more force sensors. At least one of the processors is configured to determine sleep state information for the occupant based on data obtained from one or more of the force sensors” (Benson at Abstract), where a “determination may be performed independently for each sensor set so that the sleep position of each occupant may be separately determined” (Benson at 0232) and “one or more of the processors associated with the sleep system 100 may be configured to monitor sleeping conditions. More particularly, in some embodiments, the one or more processors associated with the sleep system 100 may be configured to determine sleep environment information. Sleep environment information is information about the sleeping conditions for an occupant. The sleep environment information may, for example, identify and/or evaluate conditions in the room in which the sleep system 100 is located. The sleep environment information and/or the conditions that are identified and/or evaluated based on the sleep environment information may, in some embodiments, be referred to as sleep hygiene information” (Benson at 0359).
Apte et al. (U.S. Patent Application Publication No. 2018/0286520, hereinafter Apte) describes a “method 100 and/or system 200 can preferably determine and/or promote (e.g., provide; present; notify regarding; etc.) characterizations and/or therapies for one or more sleep-related conditions, and/or any suitable portions of the method 100 and/or system 200 can be performed in relation to sleep-related conditions” (Apte at 0022) and sleep-related conditions can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk (e.g., propensity scores, etc.), associated severity, behaviors (e.g., physical activity behavior; alcohol consumption; smoking behaviors; stress-related characteristics; other psychological characteristics; sickness; social behaviors; caffeine consumption; alcohol consumption; sleep habits such as sleep time, wake time, naps, length, quality, sleep phases, consistence, variance and/or other sleep behaviors; other habits; diet-related behaviors; meditation and/or other relaxation behaviors; lifestyle conditions associated with sleep-related conditions; lifestyle conditions affecting sleep quality; lifestyle conditions informative of, correlated with, indicative of, facilitative of, and/or otherwise associated with diagnosis and/or therapeutic intervention for sleep-related conditions; behaviors affecting and/or otherwise associated with sleep and/or sleep-related conditions; etc.), environmental factors (e.g., location of sleep; bed, mattress, pillow, blanket, and/or other bedding and/or sleeping environment factors; lighting; other visual factors; noise; other audio factors; touch factors; etc.), demographic-related characteristics (e.g., age, weight, race, gender, etc.), phenotypes (e.g., phenotypes measurable for a human, animal, plant, fungi body; phenotypes associated with sleep and/or other related aspects, etc.), and/or any other suitable aspects associated with sleep-related conditions. In examples, one or more sleep-related conditions can include a medical disorder affecting the sleep patterns of a human, animal, and/or other suitable entity. In an example, one or more sleep-related conditions can interfere with normal physical, mental, social and/or emotional function. In an example, one or more sleep-related conditions can be characterized by and/or diagnosed by medical interview, medical history, survey, sensor data, medical exams, data activities including and/or requiring monitoring individuals as they sleep, other supplementary data, and/or through any suitable techniques (e.g., techniques available for diagnosis for sleep-related conditions, etc.)” (Apte at 0023).
Zhenyu Chen et al., "Unobtrusive sleep monitoring using smartphones," 2013, 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, Venice, Italy, 2013, pp. 145-152, downloaded 8 April 2024 from https://ieeexplore.ieee.org/abstract/document/6563918, indicating that “In this paper, we present a radically different approach for measuring sleep duration based on a novel best effort sleep (BES) model. BES infers sleep using smartphones in a completely unobtrusive way - that is, the user is completely removed from the monitoring process and does not interact with the phone beyond normal user behavior” (Chen at Abstract), including monitoring sleep and environmental conditions (§ I at p. 145, § II at p. 146) and that “A diverse range of automated and semi-automated technologies for sleep monitoring have been developed for both medical and consumer usage scenarios. Many of these systems require purpose-built sensors that are used to instrument either the user or the sleep environment ([ 11] provides a survey of home-based sensor-oriented systems)” (§ VI at p. 151).
Rohman et al., "Toward a Compact Infant Monitoring System Using UWB Radar and Environmental Sensors," 2019 IEEE 1st Global Conf. on Life Sci. and Tech. (LifeTech), Osaka, Japan, 2019, pp. 4-5, DOI: 10.1109/LifeTech.2019.8883982. Downloaded from https://ieeexplore.ieee.org/abstract/document/8883982 on 5 December 2024, indicating that “In this system, we employ three different sensors: UWB radar, environmental sensors packaged as SensorTile, and visual camera. This system is separated into two parts: main module consisting of radar, camera, single board computer and extendable tripod; and a portable module which is a SensorTile” (Rohman at 2), where “As the environmental sensor, we use SensorTile module (STEVAL-STLCS01V1)from ST-Microelectronics™. It is a low-powered electronic module whose many sensors including accelerometer, magnetometer, gyroscope, thermometer, barometer, humidity sensor, and micro-electro-mechanical (MEMS)-based microphone” (Rohman at 1).
Sadwick (U.S. Patent Application Publication No. 2017/0231058) indicates that “The present invention can also be … including receiving signals from one or more sensors and detectors including, but not limited to wired and wireless signals, feedback, information, etc. from one or more devices including with wearable devices and other sensors that can detect, for example, but not limited to, heart rate, blood pressure, phase of the circadian rhythm cycle, other information about circadian rhythm, ambient light, pressure, movement, electroencephalogram/electroencephalography (EEG), electrocardiography/electrocardiogram (EKG or ECG), brain waves, oxygen level, brain waves, muscle movement, body temperature, pulse rate, actimetry, sleep actigraphs, temperature, polysomnography (PSG), mood, emotional state, etc. Wearable devices can include, but are not limited to, wrist devices, or watch-shaped devices worn on the wrist of the non-dominant arm, detectors and sensors, sleep management and monitoring sensors, systems, etc. including for awake, REM, deep sleep, various other states of sleep and wake, etc., delayed sleep phase disorder, perspiration, orientation, location, vertical or horizontal sensing, etc., speech, speech patterns, voice, weather, etc., combinations of these, etc. Such signals, input, feedback, information, etc. can be used to, for example, to set the level, spectrum and intensity, emulated sunlight spectrum, white temperature, color temperature, duration and intensity of treatment, etc. In addition, sensors can include light sensors, photosensors, spectrum analyzers including optical spectrum analyzers, light sensors with or without one or more notch filters, motion sensors, proximity sensors, radio frequency identification (RFID), sonar, radar, ultrasonic, ultrasound, voice, noise, microphones, vibrations, mechanical, acoustic, cell phones, smart phones, tablets, etc. Smart phones, tablets, laptops, computers, dedicated control and/or interface units, etc. may be used to, for example, but not limited to, transmit and/or process the information via applications or apps or can use apps to display, store, log, analyze, etc. data, results, performance, control, provide feedback, etc.” Applicant first alleges there is still a specification objection (Remarks at 5), as noted above; however, that objection was indicated as overcome and therefore withdrawn. Therefore, the allegation is considered moot and not persuasive.
Burton (U.S. Patent Application Publication No. 2021/0169417) indicates “This document describes technology comprising of one or more wearable devices (i.e. attached or applied to limbs, body, head or other body extremities but also applicable to implanted or physiologically attachable systems). These systems have a means of enabling diagnostic or prognostic monitoring applicable to monitoring relevant parameters and corresponding analysis determination and characterisation applicable to the onset or detection of events or health conditions of interest. One application relates to sleep monitoring and associate EEG sensors” (At Abstract).
Saad et al., Development of sleep monitoring system for observing the effect of the room ambient toward the quality of sleep, 2017 IOP Conf. Ser.: Mater. Sci. Eng. 210 012050. DOI 10.1088/1757-899X/210/1/012050. Downloaded 8 August 2025 from https://iopscience.iop.org/article/10.1088/1757-899X/210/1/012050/meta, indicating “Getting enough sleep at the right times can help in improving quality of life and protect mental and physical health. This study proposes a portable sleep monitoring device to determine the relationship between the room ambient and quality of sleep. Body condition parameter such as heart rate, body temperature and body movement was used to determine quality of sleep and Audio/video-based monitoring system. The functionality test on all sensors is carried out to make sure that all sensors is working properly. The functionality of the overall system is designed for a better experience with a very minimal intervention to the user. The simple test on the body condition (body temperature and heart rate) while asleep with several different ambient parameters (humidity, brightness and temperature) are varied and the result shows that someone has a better sleep in a dark and colder ambient. This can prove by lower body temperature and lower heart rate” (at Abstract).
Drake et al., Vulnerability to insomnia: the role of familial aggregation. Sleep Med. 2008 Mar;9(3):297-302. doi: 10.1016/j.sleep.2007.04.012. Epub 2007 Sep 6. PMID: 17825612; PMCID: PMC2358982. Downloaded 30 March 2026 via https://pmc.ncbi.nlm.nih.gov/articles/PMC2358982/, indicating that “Our data support the notion that vulnerability to stress-related sleep disturbance has a strong familial aggregation“ (at Abstract, Conclusions).
Ong et al., Large-scale data from wearables reveal regional disparities in sleep patterns that persist across age and sex. Sci Rep 9, 3415 (2019). https://doi.org/10.1038/s41598-019-40156-x, downloaded 30 March 2026 from https://www.nature.com/articles/s41598-019-40156-x#citeas, indicating “Using sleep data from a total of 553,559 nights from 23,680 Fitbit users (aged 15–80y), we found objective evidence for regional disparities in sleep duration of 32–43 min between Oceanian and East Asian users on weekdays. This was primarily driven by later bedtimes in East Asians. Although users in all countries extended sleep on weekends, East Asians continued to sleep less than their Oceanian counterparts. Women generally slept more than men, and older users slept less than younger users” (at Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685