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
Claims 1, 11, and 19 has been amended.
Claims 4, 8-9, and 14 has been cancelled.
Response to Arguments
Applicant’s arguments, see pages 8-10, filed 12/18/2025, with respect to the rejection(s) of
claim(s) 1-19 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ulrich.
35 U.S.C. 103:
Regarding claim 1, applicant argue that Heneghan, alone or in combination with the prior art,
does not teach “accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein each template comprises time-matching data specifying a time of day; and determining that the time data from the clock is within the time of day of the time-matching data of the selected template.” After further search and consideration, the examiner will now rely on Ulrich to teach this limitation (col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that “a patient’s history 128 includes a list box that contains patient’s demographic information and all timeline entries for the selected patient, wherein the list box contains a specific date and time. It is further disclosed that a care plan is formulated with a group of reminders to remind the patient of a timeline event, which will occur at a particular data and time in the future based on the patient’s demographic template.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the template recording system of Heneghan in view of Sayadi with the specified date and time template timeline from Ulrich for the benefit of providing optimal treatment plans with corresponding reminders and notifications to the patient.
Regarding claim 11, applicant argue that Heneghan, alone or in combination with the prior art,
does not teach “classifying each of the plurality of templates as either one of a group of shown templates or one of a group of unshown templates, based on a determination of whether each of the plurality of templates has been used for the user before; selecting a selected template from the unshown templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template.” After further search and consideration, the examiner will now rely on Ulrich to teach this limitation (col. 3, lines 62-67; col. 4, lines 1-48; col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that medical records may be stored in a dictionary, which are groups of related entries (records). They may be viewed as reference material that does not change substantially over time and which may be selected for use during entry of data or for timeline entries. Therefore, a selected group of templates may be a relevant unshown group of templates based on related demographic entries that were used before.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the classification of templates of Heneghan in view of Sayadi with the group matching classification program of Ulrich for the benefit of recalling past data entries and includes template groups that were not used before to increase the processing accuracy based on patient demographics.
Regarding claim 19, applicant argue that Heneghan, alone or in combination with the prior art,
does not teach “accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein the plurality of templates are each configured to be completed with information specific to the user and with the at least one user parameter of demographic information to provide the behavior recommendation to the user.” After further search and consideration, the examiner will now rely on Ulrich to teach this limitation (col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that “a patient’s history 128 includes a list box that contains patient’s demographic information and all timeline entries for the selected patient, wherein the list box contains a specific date and time. It is further disclosed that a care plan (behavior recommendation) is formulated with a group of reminders to remind the patient of a timeline event, which will occur at a particular data and time in the future based on the patient’s demographic template.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the template recording system of Heneghan in view of Sayadi with the specified template timeline and recommendation system from Ulrich for the benefit of providing optimal treatment plans with corresponding reminders and notifications to the patient.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections
set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable by Heneghan et al.
US Pub.: US 2016/0270718 A1, hereinafter Heneghan in view of Sayadi et al. US Pub.: US 2020/0205580 A1, hereinafter Sayadi in view of Ulrich et al. US Pat.: US 7716069 B2, hereinafter Ulrich.
Regarding claim 1, Heneghan teaches a system comprising: a clock (fig. 1; paragraph 134); It is
disclosed that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
a pressure sensor in fluid communication with the air chamber to sense pressure in the air chamber, wherein the pressure sensor is configured to sense physical phenomenon of a user and generate objective sleep-quality data for a particular sleep- session of the user based on readings from the pressure sensor (fig. 1; paragraph 50, 83, and 86); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is further disclosed in [83] that ““Sleep sensors” that monitor a user's sleep and breathing may be utilized to provide objective sleep measures 120 such as daily and longitudinal trending of objective sleep quality and biomotion levels in the bedroom or other sleep location.” It is disclosed that “Where the sleep sensor has the capability to measure heart rate, e.g., RF Doppler movement sensors or mattress-based pressure sensors monitoring the ballistocardiogram, power spectral analysis may be performed on the signal to reveal variability of inter-cardiac intervals that may be predictive of fatigue.”
a computer system comprising: at least one input element configured to receive user input from a user of the computer system (fig. 1; paragraph 125); It is disclosed in [125] that “The subjective user data 145 may be captured via a user device such as a smartphone or tablet.” The smartphone or tablet equates to a computer system.
and at least one output element configured to render output to the user of the computer system (fig. 1; paragraph 173 and 177); It is disclosed in [177] that “the user information module 185 could provide specific personalized recommendations for the user.”
and wherein the computer system is configured to provide a behavior recommendation to the user through the output element (fig. 1; paragraph 173); It is disclosed in [173] that “The fatigue assessment 180 can be used to make recommendations to the user and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
the behavior recommendation presented based at least on: objective sleep-quality data (fig. 1; paragraph 50 and 173); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is disclosed that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
time data from the clock (fig. 1; paragraph 134 and 173); It is disclosed in [134] that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
a first input from the user through the input element, the first input specifying a subjective alertness rating reported by the user for the particular sleep-session after awakening from the particular sleep session (fig. 1; paragraph 71, 118 and 173); It is disclosed in [118] that “The subjective user data 145 represents user-entered data, for example in response to a questionnaire. One form of subjective user data 145 relates to the user's subjective or self-perceived sleepiness. It is disclosed in [71] Sleepiness, sleep health and cognitive performance questionnaires (as used to obtain subjective user data) may be completed by the user on the same processing device. Answering a questionnaire for sleep health may be performed after a sleep session if the user wishes to input information into the fatigue monitoring module 110 for accurate subjective user data.
However, Heneghan does not explicitly teach wherein providing the behavior recommendation comprises: accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein each template comprises time- matching data specifying a time of day; determining demographics for the user; and selecting a selected template from the plurality of templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template; and completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user; and determining that the time data from the clock is within the time of day of the time-matching data of the selected template.
Sayadi, in the same field of endeavor, teaches wherein providing the behavior recommendation comprises: determining demographics for the user; and selecting a selected template from the plurality of templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template; and completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user (paragraph 135, 146, 189-190, 217, 224, and 229). A plurality of demographics are disclosed to be age, sex, and weight, and are completed in a user identification module 1408. It is also disclosed that one or multiple demographics of the user is being compared to with the demographics of other sleepers. This equates to the matching of user demographic parameters to at least one selected template because the matching process is based on similar demographics of other sleepers. It is further disclosed that the system may “recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system of Heneghan with the template analysis step of Sayadi for the benefit providing more data to recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.
Ulrich, in the same field of endeavor, teaches accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein each template comprises time-matching data specifying a time of day; and determining that the time data from the clock is within the time of day of the time-matching data of the selected template (col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that “a patient’s history 128 includes a list box that contains patient’s demographic information and all timeline entries for the selected patient, wherein the list box contains a specific date and time. It is further disclosed that a care plan is formulated with a group of reminders to remind the patient of a timeline event, which will occur at a particular data and time in the future based on the patient’s demographic template.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the template recording system of Heneghan in view of Sayadi with the specified date and time template timeline from Ulrich for the benefit of providing optimal treatment plans with corresponding reminders and notifications to the patient.
Regarding claim 2 and 12, Heneghan in view of Sayadi in view of Ulrich teaches the claimed
invention and Heneghan further teaches wherein to provide the behavior recommendation to the user, the computer system is configured to: receive, from the pressure sensor, the readings (fig. 1; paragraph 50 and 173); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is disclosed that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
receive, from the clock, the time data (fig. 1; paragraph 134 and 173); It is disclosed in [134] that
“The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
and receive, through the input element, user selection of the subjective alertness rating (fig. 1; paragraph
118 and 173). It is disclosed in [118] that “The subjective user data 145 represents user-entered data, for example in response to a questionnaire. One form of subjective user data 145 relates to the user's subjective or self-perceived sleepiness.”
Regarding claim 3 and 13, Heneghan in view of Sayadi in view of Ulrich teaches the claimed
invention and Heneghan further teaches wherein the clock is one of the group consisting of
a component of the computer system (fig. 1; paragraph 134 and 173); It is disclosed in [134] that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
and physically separate from the computer system and connected to the computer system by a data network (fig. 1; paragraph 134 and 173). It is disclosed in [134] that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.” Time on the clock is separate from sensor and user input data.
Regarding claim 5 and 15, Heneghan in view of Sayadi in view of Ulrich teaches the claimed
invention and Heneghan further teaches wherein the computer system comprises at least one of the group consisting of a controller device of a bed on which the user sleeps in the sleep session; a phone device of the user; a home-automation hub; and a server physically separate from the pressure sensor and connected to the pressure sensor by a data network (fig. 1; paragraph 55 and 71-72). It is disclosed that “The fatigue monitoring module may be implemented at a remote server connected to the one or more data sources over a network.”
Regarding claim 6 and 16, Heneghan in view of Sayadi in view of Ulrich teaches the claimed
invention and Heneghan further teaches wherein the subjective alertness rating is a rating wakefulness and/or alertness selected from a plurality of possible ratings to be selected by the user at least a threshold time after the end of the sleep session (fig. 1; paragraph 118-126). It is disclosed in [118] that “One form of subjective user data 145 relates to the user's subjective or self-perceived sleepiness.” An example of the sleepiness scale is the Karolinska Sleepiness Scale (KSS). “It is further disclosed in [126] that “a VAS may be administered four times a day (e.g., automatically triggered by a processor) (e.g. two hours after getting up, one hour after lunch, thirty minutes after dinner, and one hour before bed).”
Regarding claim 7 and 17, Heneghan in view of Sayadi in view of Ulrich teaches the claimed
invention and Heneghan further teaches wherein the threshold time is two hours (fig. 1; paragraph 118-126). It is disclosed in [126] that a VAS may be administered four times a day (e.g., automatically triggered by a processor) (e.g. two hours after getting up, one hour after lunch, thirty minutes after dinner, and one hour before bed).”
Regarding claim 10, Heneghan in view of Sayadi in view of Ulrich teaches the claimed invention
and Heneghan further teaches the system further comprising means for controlling pressure of a bed that includes the sensor (fig. 1; paragraph 86 and 137). It is disclosed that “Where the sleep sensor has the capability to measure heart rate, e.g., RF Doppler movement sensors or mattress-based pressure sensors monitoring the ballistocardiogram, power spectral analysis may be performed on the signal to reveal variability of inter-cardiac intervals that may be predictive of fatigue.” Comforters and sheets disclosed in [137] may change the pressure from the mattress because of added layers between the user and the mattress.
Regarding claim 11, Heneghan teaches a computer system comprising: at least one input
element configured to receive user input from a user of the computer system (fig. 1; paragraph 125); It is disclosed in [125] that “The subjective user data 145 may be captured via a user device such as a smartphone or tablet.” The smartphone or tablet equates to a computer system.
a pressure sensor in fluid communication with the air chamber to sense pressure in the air chamber, wherein the pressure sensor is configured to sense physical phenomenon of a user (fig. 1; paragraph 50, 83, and 86); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is further disclosed in [83] that ““Sleep sensors” that monitor a user's sleep and breathing may be utilized to provide objective sleep measures 120 such as daily and longitudinal trending of objective sleep quality and biomotion levels in the bedroom or other sleep location.” It is disclosed that “Where the sleep sensor has the capability to measure heart rate, e.g., RF Doppler movement sensors or mattress-based pressure sensors monitoring the ballistocardiogram, power spectral analysis may be performed on the signal to reveal variability of inter-cardiac intervals that may be predictive of fatigue.”
and at least one output element configured to render output to the user of the computer system (fig. 1; paragraph 173 and 177); It is disclosed in [177] that “the user information module 185 could provide specific personalized recommendations for the user.”
and wherein the computer system is configured to provide a behavior recommendation to the user through the output element (fig. 1; paragraph 173); It is disclosed in [173] that “The fatigue assessment 180 can be used to make recommendations to the user and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
the behavior recommendation presented based at least on: objective sleep-quality for a particular sleep-session of the user based on readings from the pressure sensor configured to sense physical phenomenon of the user (fig. 1; paragraph 50 and 173); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is disclosed that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
time data from the clock (fig. 1; paragraph 134 and 173); It is disclosed in [134] that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
a first input from the user through the input element, the first input specifying a subjective alertness rating reported by the user for the particular sleep-session (fig. 1; paragraph 71, 118 and 173); It is disclosed in [118] that “The subjective user data 145 represents user-entered data, for example in response to a questionnaire. One form of subjective user data 145 relates to the user's subjective or self-perceived sleepiness. It is disclosed in [71] Sleepiness, sleep health and cognitive performance questionnaires (as used to obtain subjective user data) may be completed by the user on the same processing device. Answering a questionnaire for sleep health may be performed after a sleep session if the user wishes to input information into the fatigue monitoring module 110 for accurate subjective user data.
However, Heneghan does not explicitly teach wherein providing the behavior recommendation comprises: accessing a plurality of templates each indexed with at least one user parameter of demographic information; classifying each of the plurality of templates as either one of a group of shown templates or one of a group of unshown templates, based on a determination of whether each of the plurality of templates has been used for the user before; determining demographics for the user; and selecting a selected template from the unshown templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template; and completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user.
Sayadi, in the same field of endeavor, teaches wherein providing the behavior recommendation comprises: accessing a plurality of templates each indexed with at least one user parameter of demographic information; determining demographics for the user; completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user (paragraph 135, 146, 189-190, 217, 224, and 229). A plurality of demographics are disclosed to be age, sex, and weight, and are completed in a user identification module 1408. It is also disclosed that one or multiple demographics of the user is being compared to with the demographics of other sleepers. This equates to the matching of user demographic parameters to at least one selected template because the matching process is based on similar demographics of other sleepers. It is further disclosed that the system may “recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system of Heneghan with the template analysis step of Sayadi for the benefit providing more data to recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.
Ulrich, in the same field of endeavor, teaches classifying each of the plurality of templates as either one of a group of shown templates or one of a group of unshown templates, based on a determination of whether each of the plurality of templates has been used for the user before; selecting a selected template from the unshown templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template (col. 3, lines 62-67; col. 4, lines 1-48; col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that medical records may be stored in a dictionary, which are groups of related entries (records). They may be viewed as reference material that does not change substantially over time and which may be selected for use during entry of data or for timeline entries. Therefore, a selected group of templates may be a relevant unshown group of templates based on related demographic entries that were used before.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the classification of templates of Heneghan in view of Sayadi with the group matching classification program of Ulrich for the benefit of recalling past data entries and includes template groups that were not used before to increase the processing accuracy based on patient demographics.
Regarding claim 18, Heneghan in view of Sayadi in view of Ulrich teaches the claimed invention
and Heneghan further teaches wherein to provide a behavior recommendation to the user, the computer system is configured to assemble the behavior recommendations from a template of general behavior recommendations that has been completed with information specific to the user (fig. 1; paragraph 118-126 and 173). It is disclosed in [173] that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.” An example of the sleepiness scale is the Karolinska Sleepiness as disclosed in [119]. A template of 5 thresholds are used to determine a general sleepiness state of the user.
Regarding claim 19, Heneghan teaches a method for generation of computer system output, the
method comprising:
receiving, from a pressure sensor in fluid communication with an air chamber of a mattress and configured to sense physical phenomenon of a user of the mattress, objective sleep-quality data based on readings for a particular sleep-session of the user on the mattress (fig. 1; paragraph 50, 83, and 86); It is disclosed in [50] that “an activity sensor configured to generate physical activity data of the user.” It is further disclosed in [83] that ““Sleep sensors” that monitor a user's sleep and breathing may be utilized to provide objective sleep measures 120 such as daily and longitudinal trending of objective sleep quality and biomotion levels in the bedroom or other sleep location.” It is disclosed that “Where the sleep sensor has the capability to measure heart rate, e.g., RF Doppler movement sensors or mattress-based pressure sensors monitoring the ballistocardiogram, power spectral analysis may be performed on the signal to reveal variability of inter-cardiac intervals that may be predictive of fatigue.”
receiving, from a clock, time data (fig. 1; paragraph 134); It is disclosed that “The time-of-day data 170, obtained from a clock, may be used as a key lookup measure in the assessment of fatigue.”
receiving, through an input element, user selection of an alertness rating for the particular sleep-session after awakening from the particular sleep-session (fig. 1; paragraph 71, 118 and 173); It is disclosed in [118] that “The subjective user data 145 represents user-entered data, for example in response to a questionnaire. One form of subjective user data 145 relates to the user's subjective or self-perceived sleepiness. It is disclosed in [71] Sleepiness, sleep health and cognitive performance questionnaires (as used to obtain subjective user data) may be completed by the user on the same processing device. Answering a questionnaire for sleep health may be performed after a sleep session if the user wishes to input information into the fatigue monitoring module 110 for accurate subjective user data.
accessing a template of general behavior recommendation that has been completed with information specific to the user (fig. 1; paragraph 118-126 and 173); It is disclosed in [173] that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.” An example of the sleepiness scale is the Karolinska Sleepiness as disclosed in [119]. A template of 5 thresholds are used to determine a general sleepiness state of the user.
generating the information specific to the user based at least on the: objective sleep-quality for a particular sleep-session of the user based on the readings, time data, and user selection of alertness rating (fig. 1; paragraph 50, 118, 134, and 173); It is disclosed in [173] that “As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
assembling behavior recommendation by completing the template with the information specific to the user (fig. 1; paragraph 173 and 177); It is disclosed in [177] that “the user information module 185 could provide specific personalized recommendations for the user.”
and generating the computer system output comprising the assembled behavior recommendation (fig. 1; paragraph 173). It is disclosed in [173] that “The fatigue assessment 180 can be used to make recommendations to the user and the user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment.”
However, Heneghan does not explicitly teach wherein providing the behavior recommendation comprises: accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein the plurality of templates are each configured to be completed with information specific to the user and with the at least one user parameter of demographic information to provide the behavior recommendation to the user; determining demographics for the user; and selecting a selected template from the plurality of templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template; completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user.
Sayadi, in the same field of endeavor, teaches wherein providing the behavior recommendation comprises: determining demographics for the user; and selecting a selected template from the plurality of templates based on a match of the demographics for the user with the at least one user parameter of demographic information for the selected template; completing the selected template i) with information specific to the user and also ii) with the at least one user parameter of demographic information to provide the behavior recommendation to the user (paragraph 135, 146, 189-190, 217, 224, and 229). A plurality of demographics are disclosed to be age, sex, and weight, and are completed in a user identification module 1408. It is also disclosed that one or multiple demographics of the user is being compared to with the demographics of other sleepers. This equates to the matching of user demographic parameters to at least one selected template because the matching process is based on similar demographics of other sleepers. It is further disclosed that the system may “recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system of Heneghan with the template analysis step of Sayadi for the benefit providing more data to recommend sleep enhancement tips such as optimal time to go to bed, optimal position to start sleeping and optimal sleep duration; and/or viii) recommending optimal bed and bedroom environment parameters such as optimal mattress firmness (adjusted using Sleep Number setting), optimal bed temperature and optimal bedroom temperature.
Ulrich, in the same field of endeavor, teaches accessing a plurality of templates each indexed with at least one user parameter of demographic information, wherein the plurality of templates are each configured to be completed with information specific to the user and with the at least one user parameter of demographic information to provide the behavior recommendation to the user (col. 6, lines 1-67; col. 7, lines 1-39, and col. 8, lines 27-53). It is disclosed that “a patient’s history 128 includes a list box that contains patient’s demographic information and all timeline entries for the selected patient, wherein the list box contains a specific date and time. It is further disclosed that a care plan (behavior recommendation) is formulated with a group of reminders to remind the patient of a timeline event, which will occur at a particular data and time in the future based on the patient’s demographic template.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the template recording system of Heneghan in view of Sayadi with the specified template timeline and recommendation system from Ulrich for the benefit of providing optimal treatment plans with corresponding reminders and notifications to the patient.
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 THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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, Benjamin Klein can be reached on 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792