DETAILED ACTION
Claims 1-3, 5-8, 14, 17-19, 21, 24-26, 31, 34, 38-39, & 49 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-8, 14, 17-19, 21, 24-26, 31, 34, 38-39, & 49 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claim 49 is directed to a system (i.e., a machine); Claims 1-3, 5-8, 14, 17-19, 21, 24-26, 31, 34, & 38-39 are directed to a method (i.e., a process). Accordingly, claims 1-3, 5-8, 14, 17-19, 21, 24-26, 31, 34, 38-39, & 49 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 49 includes limitations that recite at least one abstract idea. Specifically, independent claim 49 recites:
49. A system comprising:
a control system including one or more processors; and
a memory having stored thereon machine readable instructions;
wherein the control system is coupled to the memory, and wherein the one or more processors of the control system are configured to execute the machine readable instructions in the memory to:
receive sensor data from one or more sensors, the sensor data being associated with a user engaging in a sleep therapy plan;
receive one or more therapy parameters associated with the sleep therapy plan;
dynamically generate at least one updated therapy parameter associated with the sleep therapy plan based at least in part on the one or more therapy parameters and the received sensor data: and
present the at least one updated therapy parameter in association with the sleep therapy plan.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because receiving sensor data, receiving sleep therapy parameters, generating updating therapy parameters based on the sensor and parameter data, and presenting the updated paramters are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 49 and analogous independent claims 1 & 26 recite at least one abstract idea.
Furthermore, dependent claims 2-3, 5-8, 14, 17-19, 21, 24-25, 31, 34, & 38-39, further narrow the abstract idea described in the independent claims. Claims 2-3, 5-8, 14, 17, 24-25, & 39 recite presenting updated therapy parameters and generating a new therapy plan, Claims 21, 31, 34 recites determining sleep quality information and generating a sleep quality score. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 49 and analogous independent claims 1 & 26, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
49. A system comprising:
a control system including one or more processors; and
a memory having stored thereon machine readable instructions;
wherein the control system is coupled to the memory, and wherein the one or more processors of the control system are configured to execute the machine readable instructions in the memory to:
receive sensor data from one or more sensors, the sensor data being associated with a user engaging in a sleep therapy plan;
receive one or more therapy parameters associated with the sleep therapy plan;
dynamically generate at least one updated therapy parameter associated with the sleep therapy plan based at least in part on the one or more therapy parameters and the received sensor data: and
present the at least one updated therapy parameter in association with the sleep therapy plan.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the control system, processor, memory; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of a sensor, the Examiner submits that these additional limitations do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the steps of the at least one abstract idea are performed (see MPEP § 2106.05(h)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception 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, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, independent claim 49 and analogous independent claims 1 & 26do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 17-19, 38: These claims recite various sensors and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(h)).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 49 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the control system, processor, memory; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of a sensor, the Examiner submits that these additional limitations do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the steps of the at least one abstract idea are performed (see MPEP § 2106.05(h)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-3, 5-8, 14, 17-19, 21, 24-26, 31, 34, 38-39, & 49 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-8, 14, 17-18, 25-26, 31, 34, 38-39, & 49 are rejected under 35 U.S.C. 103 as being unpatentable over Moturu (US20170189641).
As per claim 1, Moturu discloses a method, comprising:
receiving sensor data from one or more sensors, the sensor data being associated with a user engaging in a sleep therapy plan (para. 46: sensor data obtained from user regarding sleep quality);
receiving one or more therapy parameters associated with the sleep therapy plan (para. 55, 67-68: therapeutic intervention model generates parameters based on sleep care plan);
dynamically generating at least one updated therapy parameter associated with the sleep therapy plan based at least in part on the one or more therapy parameters and the received sensor data (para. 65, 69: sleep care plan is updated based on evaluation of improvement of user’s sleep quality using sleep-related parameters; plan parameters can be changed to improve user’s sleep quality); and
presenting the at least one updated therapy parameter in association with the sleep therapy plan (para. 61: modification to sleep plan displayed).
As per claim 2, Moturu discloses the method of claim 1, wherein presenting the at least one updated therapy parameter includes automatically updating the sleep therapy plan based at least in part on the at least one updated therapy parameter (para. 65, 69: sleep care plan is updated based on evaluation of improvement of user’s sleep quality using sleep-related parameters; plan parameters can be changed to improve user’s sleep quality).
As per claim 3, Moturu discloses the method of claim 1, wherein;
the one or more therapy parameters associated with the sleep therapy plan include i) a target in-bed time; ii) a target out-of-bed time; iii) a target sleep time; iv) a target awaken time; v) an alarm time; vi) a target sleep duration; vii) a pharmacological dosage parameter; viii) a sleep environment parameter; ix) a pre-sleep activity parameter; or x) any combination of i-ix (para. 66: various therapy parameters); and
wherein the at least one updated therapy parameter includes i) an updated in-bed time; ii) an updated out-of-bed time; iii) an updated target sleep time; iv) an updated target awaken time; v) an updated alarm time; vi) an updated target sleep duration; vii) updated pharmacological dosage parameter; viii) an updated sleep environment parameter; ix) an updated pre-sleep parameter; or x) any combination of i-ix (para. 66, 69: various sleep parameters can be updated).
As per claim 5, Moturu discloses the method of claim 1, wherein receiving the sensor data occurs while the user is engaging in a sleep session (para. 46: sensor data gathered during sleep session), and wherein presenting the at least one updated therapy parameter occurs while the user is engaging in the sleep session (para. 63-64: sleeping intervention can be sent to user during specific time periods such as night time or during sleep period).
As per claim 6, Moturu discloses the method of claim 5, wherein the one or more therapy parameters includes an alarm time, wherein the at least one updated therapy parameter includes an updated alarm time, and wherein presenting the at least one updated therapy parameter includes adjusting the alarm time based at least in part on the updated alarm time (para. 62-63: provide the user with a personalized sleep care plan according to the user's desired sleep goals (e.g., wakeup times, bedtimes, etc.).
As per claim 7, Moturu discloses the method of claim 1, wherein dynamically generating the at least one updated therapy parameter includes:
receiving target sleep efficiency information (para. 45, 60, 66: sleep efficiency data obtained from historical population data);
determining current sleep efficiency information based at least in part on the sensor data (para. 44: sleep-related parameters determined based on obtained log data);
identifying a difference between the target sleep efficiency information and the current sleep efficiency information (para. 59: comparative analyzes between different components of sleep parameters and threshold conditions) ; and
generating the updated therapy parameter in response to identifying the difference, wherein the updated therapy parameter is based at least in part on the target sleep efficiency information and the current sleep efficiency information (para. 66: determining sleep care plan based on sleep-related parameter mapped to specific therapeutic intervention types associated with the sleep quality parameter value).
As per claim 8, Moturu discloses the method of claim 1, wherein dynamically generating the at least one updated therapy parameter is based at least in part on i) sleep quality information based at least in part on the sensor data (para. 66: determining sleep care plan based on sleep-related parameter mapped to specific therapeutic intervention types associated with the sleep quality parameter value), ii) one or more sleep events detected based at least in part on the sensor data, iii) an apnea-hypopnea index calculated based at least in part on the sensor data, iv) sleep stage information based at least in part on the sensor data, or v) any combination of i)-iv).
As per claim 14, Moturu discloses the method of claim 1, wherein dynamically generating the at least one updated therapy parameters includes:
accessing a historical log associated with the sleep therapy plan (para. 69: first log of use dataset associated with therapeutic intervention); and
generating the at least one updated therapy parameter based at least in part on the historical log (para. 69: dynamically updating care plan based on previous sleep session data associated with therapeutic intervention).
As per claim 17, Moturu discloses the method of claim 1, wherein receiving the sensor data from the one or more sensors includes receiving non-contact sensor data from at least one non-contact sensor (para. 21, 33: non contact sensor such as GPS sensor), and wherein dynamically generating the at least one updated therapy parameter includes:
extracting biomotion information based at least in part on the non-contact sensor data (para. 21: mobility behavior data extracted);
identifying body movement information based at least in part on the extracted biomotion information (para. 53: extracting a set of features from a log of use dataset and a motion supplementary dataset); and
generating the at least one updated therapy parameter based at least in part on the body movement information (para. 55, 60: sleep-related parameters extracted and used to generate updated sleep interventions).
As per claim 18, Moturu discloses the method of claim 1, wherein receiving the sensor data from the one or more sensors includes receiving environment data from i) a temperature sensor; ii) a light sensor; iii) a presence sensor; iv) a microphone; or v) any combination of i-iv (para. 78: ambient data obtained from various sensors); and wherein dynamically generating the at least one updated therapy parameter is based at least in part on the environment data (para. 78: ambient data can be used to generate therapeutic intervention).
As per claim 25, Moturu discloses the method of claim 1, wherein receiving the sensor data includes receiving first sensor data while the user is not engaging in a sleep session (para. 46: sensor data obtained in other activities), and receiving second sensor data while the user is engaging in the sleep session (para. 46: sensor data obtained during sleep session); and wherein dynamically generating the at least one updated therapy parameter is based at least in part on the first sensor data and the second sensor data (para. 46, 60-65: sleep data uses sensor data from various sensors obtaining data at various times; sleep therapeutic intervention determined based on obtained sleep data).
As per claim 26, Moturu discloses a method comprising:
receiving sensor data from one or more sensors, the sensor data being associated with a user (para. 46: sensor data obtained from user regarding sleep quality);
determining one or more physiological parameters based at least in part on the received sensor data (para. 44: sleep-related parameters determined);
generating a sleep disorder prediction based at least in part on the one or more physiological parameters (para. 44: diagnosis can be characterized based on analysis of sleep parameters);
identifying a future sleep therapy plan associated with the user (para. 15: sleep care plan generated);
generating a sleep therapy plan recommendation based at least in part on the generated sleep disorder prediction and the identified sleep therapy plan (para. 44, 60, 65,69: sleep plan generated based on detection of disorder and to improve specific sleep parameters); and
facilitating application of the sleep therapy plan recommendation to the future sleep therapy plan prior to implementation of the future sleep therapy plan (Fig. 6A; para. 62: sleep care plan promoted and displayed to user).
As per claim 31, Moturu discloses the method of claim 26, wherein the sensor data includes subjective user feedback associated with a sleep session (para. 38: survey dataset from user), wherein the method further comprises generating a sleep score indicative of a stress level of the user based at least in part on the subjective user feedback (para. 40-41: survey dataset includes stress level data and generating a quantitative score), and wherein identifying the future sleep therapy plan is based at least in part on the stress score (para. 66: sleep care plan can be generated using survey dataset).
As per claim 34, Moturu discloses the method of claim 26, further comprising generating a sleep quality log based at least in part on the sensor data, wherein the sleep quality log includes i) sleep state information; ii) sleep stage information; iii) subjective user feedback associated with a sleep session, or iv) any combination of i)-iii) (para. 66: determining sleep care plan based on sleep-related parameter mapped to specific therapeutic intervention types associated with the sleep quality parameter value); wherein identifying the future sleep therapy plan is based at least in part on the sleep quality log (para. 55, 60: sleep-related parameters extracted and used to generate updated sleep interventions).
Claim 38 recites substantially similar limitations as those already addressed in claim 17, and, as such, is rejected for similar reasons as given above.
As per claim 39, Moturu discloses the method of claim 26, identifying the future sleep therapy plan includes:
determining one or more sleep duration parameters associated with a target sleep duration used in the future sleep therapy plan, and wherein the sleep therapy plan recommendation includes i) suggested changes to at least one of the one or more sleep duration parameters, ii) suggested values for at least one of the one or more sleep duration parameters, or iii) both i and ii;
automatically providing one or more default therapy parameters;
determining one or more therapy parameters associated with the futures sleep therapy plan, wherein the one or more sleep therapy parameters includes: i) a sleep restriction parameter; ii) a sleep compression parameter; iii) a pharmacological parameter; iv) a sleep onset latency parameter; v) a sleep environment parameter;
vi) a pre-sleep activity parameter; or vii) any combination of i-vi; or determining that the future sleep therapy plan is a cognitive behavior therapy for insomnia (CBTi) plan.
As per claim 49, Moturu discloses a system comprising:
a control system including one or more processors (para. 79: processor); and
a memory having stored thereon machine readable instructions;
wherein the control system is coupled to the memory, and wherein the one or more processors of the control system are configured to execute the machine readable instructions in the memory to:
receive sensor data from one or more sensors, the sensor data being associated with a user engaging in a sleep therapy plan (para. 46: sensor data obtained from user regarding sleep quality);
receive one or more therapy parameters associated with the sleep therapy plan (para. 55, 67-68: therapeutic intervention model generates parameters based on sleep care plan);
dynamically generate at least one updated therapy parameter associated with the sleep therapy plan based at least in part on the one or more therapy parameters and the received sensor data (para. 65, 69: sleep care plan is updated based on evaluation of improvement of user’s sleep quality using sleep-related parameters; plan parameters can be changed to improve user’s sleep quality); and
present the at least one updated therapy parameter in association with the sleep therapy plan (para. 61: modification to sleep plan displayed).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Moturu in view of Ning (US20180060507).
As per claim 19, Moturu teaches the method of claim 1, but does not expressly teach wherein receiving the sensor data from the one or more sensors includes receiving pharmacological data from i) a pharmacological container sensor; ii) a camera; iii) a weight sensor; or iv) any combination of i-iii; and wherein dynamically generating the at least one updated therapy parameter is based at least in part on the pharmacological data.
Ning, however, teaches to a method for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks where a sensor such as a camera is used to obtain user sleep data (para. 27). Ning also teaches to using the sensor data to generate new patient sleep intervention plan (para. 39).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Ning with Moturu based on the motivation of provides a system for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks (Ning – para. 22).
Claims 21 & 24 are rejected under 35 U.S.C. 103 as being unpatentable over Moturu in view of Molina (US20190083028).
As per claim 21, Moturu teaches the method of claim 1, further comprising:
determining sleep quality information based at least in part on the sensor data, the sleep quality information including i) sleep efficacy information; ii) sleep state information; iii) sleep stage information; iv) detected sleep event information; v) a calculated apnea-hypopnea index; vi) or any combination of i)-v) (para. 144: sleep quality parameters extracted).
Moturu does not expressly teach generating a sleep therapy plan score based at least in part on the sleep quality information; and storing the sleep therapy plan score in association with the one or more therapy parameters of the sleep therapy plan.
Molina, however, teaches to facilitating sleep improvement for a user where a score is generated and stored based on sleep parameter data together in a database (para. 84, 85).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Molina with Moturu based on the motivation of provide a meaningful and quantifiable metric for an individual to understand a quality of his/her sleep (Molina – para. 3).
As per claim 24, Moturu and Molina teach the method of claim 21. Moturu does not expressly teach wherein dynamically generating the at least one updated therapy parameter includes: accessing a historical sleep therapy plan score associated with one or more historical parameters; comparing the historical sleep therapy plan score with the sleep therapy plan score; and generating the at least one updated therapy parameter based at least in part on the one or more historical parameters, the one or more parameters, and the comparison between the historical sleep therapy plan score and the sleep therapy plan score.
Molina, however, teaches to facilitating sleep improvement for a user where a score is generated and stored based on sleep parameter data together in a database (para. 84, 85). Molina also teaches to accessing historical sleep scores and metrics and comparing those metrics with reference metrics (para. 37). Molina also teaches to generating new sleep parameters based on the comparison (para. 83, 89, 91).
The motivations to combine the above mentioned references are discussed in the rejection of claim 21, and incorporated herein
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heneghan (US20220061752) teaches to a system for monitoring a user's fatigue state, the system comprising one or more data sources such as objective measures of sleep and SDB, subjective user data, objective fatigue measurements, and environmental data, and a monitoring module that analyses the data to generate an assessment of the fatigue state of the user.
Shouldice (US20160151603) teaches to a method of a processor for promoting sleep of a user. The method may involve with a processor, analyzing signals from a motion sensor to detect sleep information from the signals. The method may involve with the processor, upon receiving an activation signal, recording by a microphone a voice sound message of the user and storing data of the voice sound message in a memory coupled to the processor. The method may permit a user to record thoughts so as to clear a mind of the user and promote sleep.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/ Primary Examiner, Art Unit 3619