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 1-6 and 8-12 are rejected. Claim 7 is canceled.
Response to Arguments
Claim Rejections - 35 USC § 101
The previous 101 rejection of claim 12 is withdraw in view of the amendment “non-transitory.”
Applicant's arguments filed 12/12/25 have been fully considered but they are not persuasive.
Applicant argues that the training of the machine learning model using the specific training data cannot be practically performed by a human mind, because it involves highly complicated calculations that are beyond the capability of a human observer. However, claims do not currently recite any limitations of training data that would suggest highly complicated calculations that are beyond the capability of a human observer. Further, the machine learning model is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting.
Applicant asserts that such training of the machine learning model using the specific data is necessarily rooted in the computer technology. However, the Examiner disagrees. MPEP 2106.05(f) states:
Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983.
Applicant argues that claim 1 includes additional elements that are sufficient to amount to significantly more than the judicial exception. However, the Examiner disagrees. The sensor amounts to nothing more than pre-solution activity of data gathering. The storage circuit and processor are recited at a high-level of generality and amount to nothing more than parts of a generic computer. A machine learning model is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting.
Claim Rejections - 35 USC § 102 & 103
Applicant’s amendments and remarks filed on 12/12/25 merit new grounds of rejection under 35 U.S.C. 103 over Lopes (WO 2021152549 filed on 1/30/21) in view of Martinmäki (US 20180242902 filed on 2/23/18), hereinafter referred to as Martin.
Applicant’s remarks on pages 10-12 are directed solely to whether the Marin reference teaches newly amended subject matter. Therefore, this is unpersuasive as the rejection is made in view of Lopes. Lopes teaches wherein the step of determining the second sleep assessment result of the subject based on the second sleep signal comprises: requesting the subject to self-assess a self sleep score of the sleep process corresponding to the first sleep signal (¶31-the self-reported user feedback can include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent)): feeding the second sleep signal into a machine learning model, wherein the machine learning model generates a second sleep score as the second sleep assessment result in response to the second sleep signal (¶7-a score associated with second one of the set of sleep- related parameters; ¶96-the algorithm can be trained using data from the user profile data; ¶94-the user profile can include…self-reported user feedback; ¶5-receiving second data associated with the user subsequent to the first sleep session and prior to a second sleep session, wherein the second data includes user feedback; ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score)): feeding the second sleep signal into a machine learning model, wherein the machine learning model generates a second sleep score as the second sleep assessment result in response to the second sleep signal (¶7-a score associated with second one of the set of sleep- related parameters; ¶96-the algorithm can be trained using data from the user profile data; ¶94-the user profile can include…self-reported user feedback; ¶5-receiving second data associated with the user subsequent to the first sleep session and prior to a second sleep session, wherein the second data includes user feedback; ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score)), wherein the machine learning model is learned using a plurality of pieces of training data (¶96; ¶113), and each piece of training data comprises the first sleep signal and the self sleep score of the sleep process corresponding to the first sleep signal (¶5 updating the user profile to include at least a portion of the determined first set of sleep-related parameters and at least a portion of the second data; ¶96-the algorithm can be trained using data from the user profile data (e.g., previously recorded data associated with the user), the algorithm can be, for example, a machine learning algorithm, (e.g., supervised or unsupervised) or a neural network (e.g., shallow or deep approaches); ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score); ¶94; ¶5). See the rejection below for additional details and reasoning.
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 1-6 and 8-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more.
Step 1
The claimed invention in claims 1-6 and 8-12 are directed to statutory subject matter as the claims recite a method, device, and a non-transitory computer-readable storage medium for sleep quality assessment.
Step 2A, Prong One
Regarding claims 1, 11, and 12, the recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsection (III)).
Regarding claims 1, 11, and 12, the limitations of “determining a plurality of sleep indicators associated with a sleep process of a subject based on the first sleep signal, determining a first sleep assessment result of the subject based on the plurality of sleep indicators; determining a second sleep assessment result of the subject based on the second sleep signal, wherein the step of determining the second sleep assessment result of the subject based on the second sleep signal comprises: requesting the subject to self-assess a self sleep score of the sleep process corresponding to the first sleep signal: generating a second sleep score as the second sleep assessment result in response to the second sleep signal; and determining a sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional analyzing a print out of a first sleep signal to determine sleep indicators, further analyzing the sleep indicators for a first sleep assessment result, analyzing a print out of a second sleep signal to determine a second sleep assessment result that is based on a user writing down a self sleep score, determining a second sleep score based on the second sleep assessment result, and using the first and second sleep assessment results to determine a sleep quality.
Step 2A, Prong Two
For claims 1, 11, and 12, the judicial exception is not integrated into a practical application. In particular, claims 1, 11, and 12 recite “a sensor, a storage circuit, and a processor.” The sensor amounts to nothing more than pre-solution activity of data gathering. The storage circuit and processor are recited at a high-level of generality and amount to nothing more than parts of a generic computer.
Additionally, Applicant includes a machine learning model which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into
a practical application, the additional element of a sensor amounts to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Moreover, the sensor is well-understood, routine, and conventional structure as evidenced by
US 20210169417 (¶2883-conventional PSG sensor(s)), US 20180353125 (¶5-conventional method using the motion sensor), and US 20100152600 (¶166-conventional sensor). Further, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). In this case, elements of general computer are being used to implement the abstract idea.
Regarding dependent claims 2-6 and 8-10, the limitations of claims 1, 11, and 12 further define the limitations already indicated as being directed to the abstract idea.
Claims 2 and 10 are further directed to the abstract idea.
Claim 3 is further directed to the abstract idea. The limitations of “wherein the step of determining the first sleep assessment result of the subject based on the plurality of sleep indicators comprises: obtaining a sleep quality score corresponding to each of the plurality of sleep indicators; and determining the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of a sleep quality score corresponding to each of the plurality of sleep indicators and using judgment to determine the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators.
Claim 4 is further directed to the abstract idea. The limitations of “wherein the plurality of sleep indicators comprise a first sleep indicator, the first sleep indicator is determined to have a first numerical result, and the step of obtaining the sleep quality score corresponding to each of the plurality of sleep indicators comprises: obtaining a plurality of numerical ranges corresponding to the first sleep indicator; and using a score corresponding to a first numerical range as the sleep quality score corresponding to the first sleep indicator in response to determining that the first numerical result is in the first numerical range among the plurality of numerical ranges” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of a plurality of numerical ranges corresponding to the first sleep indicator; and using a score corresponding to a first numerical range as the sleep quality score corresponding to the first sleep indicator in response to determine that the first numerical result is in the first numerical range among the plurality of numerical ranges.
Claim 5 is further directed to the abstract idea. The limitations of “wherein the step of obtaining the plurality of numerical ranges corresponding to the first sleep indicator comprises: determining the plurality of numerical ranges corresponding to the first sleep indicator based on an age of the subject” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional making a determination that the plurality of numerical ranges correspond to the first sleep indicator based on an age of the subject.
Claim 6 is further directed to the abstract idea. The limitations of “wherein the step of determining the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators comprises: determining a first sleep score as the first sleep assessment result by executing a linear combination on the sleep quality score corresponding to each of the plurality of sleep indicators” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to execute a linear combination on the sleep quality score corresponding to each of the plurality of sleep indicators to determine a first sleep score as the first sleep assessment result.
Claim 8 is further directed to the abstract idea. The limitations of “wherein the first sleep assessment result and the second sleep assessment result are represented as a first sleep score and a second sleep score respectively, and the step of determining the sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result comprises: determining a specific sleep score as the sleep quality of the sleep process by executing a weighted operation on the first sleep score and the second sleep score” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to determine a specific sleep score as the sleep quality by using a combination of the first sleep score and the second sleep score.
Claim 9 is further directed to the abstract idea. The limitations of “wherein the first sleep score and the second sleep score are respectively configured with a first weight and a second weight for executing a weighted operation, and before the step of determining the specific sleep score as the sleep quality of the sleep process by executing the weighted operation on the first sleep score and the second sleep score, the method further comprises: increasing the first weight and decreasing the second weight in response to determining that the first sleep score is lower than a preset threshold value; and maintaining the first weight and the second weight in response to determining that the first sleep score is not lower than the preset threshold value” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to increase the first weight and decrease the second weight based on a first sleep score being lower than a preset threshold value and maintain the first weight and the second weight when the first sleep score is not lower than the preset threshold value.
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 1-4, 6, 8, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes (WO 2021152549 filed on 1/30/21) in view of Martinmäki (US 20180242902 filed on 2/23/18), hereinafter referred to as Martin.
Regarding claims 1, 11, and 12, Lopes teaches a sleep quality assessment method, a sleep quality assessment device, and a non-transitory computer-readable storage medium comprising: a storage circuit, which stores a program code (¶4-the memory stores machine-readable instructions); and a processor (¶6-one or more processors), which is coupled to the storage circuit and accesses the program code to execute: obtaining a first sleep signal via a sensor (¶4-receive (i) first physiological data associated with a user during a first sleep session, the first physiological data being generated by a first sensor), determining a plurality of sleep indicators associated with a sleep process of a subject based on the first sleep signal (¶4-determine a first set of sleep-related parameters for the first sleep session based at least in part on the first physiological data), and determining a first sleep assessment result of the subject based on the plurality of sleep indicators (¶125-identify the highest or “best” one of the first plurality of insomnia-related scores and communicate that score to the user; ¶124-determining a first plurality of insomnia-related scores for the first sleep session based at least in part on the first set of sleep-related parameters); obtaining a second sleep signal corresponding to the sleep process via the sensor (¶8-the first physiological data and the second physiological data being generated by one or more sensors), and determining a second sleep assessment result of the subject based on the second sleep signal (¶7-a score associated with second one of the set of sleep-related parameters), wherein the step of determining the second sleep assessment result of the subject based on the second sleep signal comprises: requesting the subject to self-assess a self sleep score of the sleep process corresponding to the first sleep signal (¶31-the self-reported user feedback can include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent)): feeding the second sleep signal into a machine learning model, wherein the machine learning model generates a second sleep score as the second sleep assessment result in response to the second sleep signal (¶7-a score associated with second one of the set of sleep- related parameters; ¶96-the algorithm can be trained using data from the user profile data; ¶94-the user profile can include…self-reported user feedback; ¶5-receiving second data associated with the user subsequent to the first sleep session and prior to a second sleep session, wherein the second data includes user feedback; ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score)): feeding the second sleep signal into a machine learning model, wherein the machine learning model generates a second sleep score as the second sleep assessment result in response to the second sleep signal (¶7-a score associated with second one of the set of sleep- related parameters; ¶96-the algorithm can be trained using data from the user profile data; ¶94-the user profile can include…self-reported user feedback; ¶5-receiving second data associated with the user subsequent to the first sleep session and prior to a second sleep session, wherein the second data includes user feedback; ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score)), wherein the machine learning model is learned using a plurality of pieces of training data (¶96; ¶113), and each piece of training data comprises the first sleep signal and the self sleep score of the sleep process corresponding to the first sleep signal (¶5 updating the user profile to include at least a portion of the determined first set of sleep-related parameters and at least a portion of the second data; ¶96-the algorithm can be trained using data from the user profile data (e.g., previously recorded data associated with the user), the algorithm can be, for example, a machine learning algorithm, (e.g., supervised or unsupervised) or a neural network (e.g., shallow or deep approaches); ¶113-updating the user profile subsequent to the first sleep session is advantageous in that the updated user profile can be used for subsequent sleep sessions (e.g., the second sleep session) to make more accurate predictions (e.g., for identifying the target parameter and/or target score); ¶94; ¶5). However, Lopes does not teach determining a sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result.
Martin relates generally to a field of measuring a human and, in particular, to evaluating sleep quality through measurements (¶2). Martin further teaches the invention using the following step:
determining a sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result (¶46-an overall sleep quality metric is computed in block 604 as an average of the first and second sleep quality metric).
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 invention of Lopes to include determining a sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result of Martin in order to detect unusual sleep behaviour (Martin, ¶65) and provide feedback instruction as how to improve the sleep quality (Martin, ¶83).
Regarding claim 2, the combination of Lopes and Martin teaches the method according to claim 1, wherein the plurality of sleep indicators comprise at least one of a sleep latency, a sleep duration, a wake after sleep onset (WASO), a sleep efficiency, a proportion of rapid eye movement period, a proportion of falling asleep period, a proportion of light sleep period, and a proportion of deep sleep period (Lopes, ¶46-the one or more sleep-related parameters that can be determined for the user during the sleep session based on the sleep-wake signal include, for example, a total time in bed, a total sleep time, a sleep onset latency, a wake-after-sleep- onset parameter, a sleep efficiency, a fragmentation index, or any combination thereof).
Regarding claim 3, the combination of Lopes and Martin teaches the method according to claim 1, wherein the step of determining the first sleep assessment result of the subject based on the plurality of sleep indicators comprises: obtaining a sleep quality score corresponding to each of the plurality of sleep indicators (Lopes, ¶8-each one of the first plurality of insomnia-related scores being associated with a corresponding one of the first set of sleep-related parameters); and determining the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators (Lopes, ¶125-identify the highest or “best” one of the first plurality of insomnia-related scores and communicate that score to the user).
Regarding claim 4, the combination of Lopes and Martin teaches the method according to claim 3, wherein the plurality of sleep indicators comprise a first sleep indicator, the first sleep indicator is determined to have a first numerical result (Lopes, ¶97-determining a first set of insomnia-related sleep scores for the first sleep session based at least in part on the first set of sleep-related parameters (step 502), an insomnia-related sleep score can be, for example, a numerical value that is on a predetermined scale) and the step of obtaining the sleep quality score corresponding to each of the plurality of sleep indicators comprises: obtaining a plurality of numerical ranges corresponding to the first sleep indicator (Martin, ¶63-the sleep states may be defined within the range according to a determined criterion, e.g. as illustrated in Table 2 below. For example, the awake state may be associated with the maximum value of the range, while the deep non-REM sleep state may be associated with the minimum value of the range. Boundaries of the remaining states may then be set accordingly between sub-ranges of the awake state and the deep non-REM sleep state); and using a score corresponding to a first numerical range as the sleep quality score corresponding to the first sleep indicator in response to determining that the first numerical result is in the first numerical range among the plurality of numerical ranges (Martin, ¶63-the minimum value may set the lowest value of the range, and the maximum value may set the highest value of the range. In an embodiment, the range is [0, 1], and the variation is mapped to this scale depending on the variation with respect to the minimum and maximum values. The sleep states may be defined within the range according to a determined criterion, e.g. as illustrated in Table 2 below; ¶49-combined into the sleep quality according to…sleep stages).
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 invention of Lopes to include and the step of obtaining the sleep quality score corresponding to each of the plurality of sleep indicators comprises: obtaining a plurality of numerical ranges corresponding to the first sleep indicator; and using a score corresponding to a first numerical range as the sleep quality score corresponding to the first sleep indicator in response to determining that the first numerical result is in the first numerical range among the plurality of numerical ranges of Martin in order to detect unusual sleep behaviour (Martin, ¶65) and provide feedback instruction as how to improve the sleep quality (Martin, ¶83).
Regarding claim 6, the combination of Lopes and Martin teaches the method according to claim 3, wherein the step of determining the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators comprises: determining a first sleep score as the first sleep assessment result by executing a linear combination on the sleep quality score corresponding to each of the plurality of sleep indicators (Martin, ¶48- the sleep score may be computed by using a function of a weighted sum, the aggregation may include a (weighted) sum of the sleep scores for the different sleep states).
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 invention of Lopes to include wherein the step of determining the first sleep assessment result based on the sleep quality score corresponding to each of the plurality of sleep indicators comprises: determining a first sleep score as the first sleep assessment result by executing a linear combination on the sleep quality score corresponding to each of the plurality of sleep indicators of Martin in order to detect unusual sleep behaviour (Martin, ¶65) and provide feedback instruction as how to improve the sleep quality (Martin, ¶83).
Regarding claim 8, the combination of Lopes and Martin teaches the method according to claim 1, wherein the first sleep assessment result and the second sleep assessment result are represented as a first sleep score and a second sleep score respectively, and the step of determining the sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result comprises: determining a specific sleep score as the sleep quality of the sleep process by executing a weighted operation on the first sleep score and the second sleep score (Martin, ¶46-both the first and second sleep quality metrics are scaled between [0, 1], and an overall sleep quality metric is computed in block 604 as an average of the first and second sleep quality metric. The average may weight all sleep quality metrics equally or unequally).
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 invention of Lopes to include wherein the first sleep assessment result and the second sleep assessment result are represented as a first sleep score and a second sleep score respectively, and the step of determining the sleep quality of the sleep process based on the first sleep assessment result and the second sleep assessment result comprises: determining a specific sleep score as the sleep quality of the sleep process by executing a weighted operation on the first sleep score and the second sleep score of Martin in order to detect unusual sleep behaviour (Martin, ¶65) and provide feedback instruction as how to improve the sleep quality (Martin, ¶83).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lopes in view of Martin as applied to claims 1, 3, and 4 above, and further in view of Russell (US 20180256094 filed on 3/11/17).
Regarding claim 5, the combination of Lopes and Martin teaches the method according to claim 4. However, the combination of Lopes and Martin does not teach wherein the step of obtaining the plurality of numerical ranges corresponding to the first sleep indicator comprises: determining the plurality of numerical ranges corresponding to the first sleep indicator based on an age of the subject.
Russell teaches wherein the step of obtaining the plurality of numerical ranges corresponding to the first sleep indicator comprises: determining the plurality of numerical ranges corresponding to the first sleep indicator based on an age of the subject (¶79-population-normalized metrics 308 are sleep quality metrics assessed with respect to a plurality of users. In certain embodiments, determining a respective value for a respective population-normalized metric 310 includes comparing determined values for a user to those of other users of a similar demographic (e.g., same gender, same age range, same occupation)).
Russell relates to the field of computing devices with one or more sensors to collect physiological information of a user (¶1).
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 invention of Lopes to include wherein the step of obtaining the plurality of numerical ranges corresponding to the first sleep indicator comprises: determining the plurality of numerical ranges corresponding to the first sleep indicator based on an age of the subject of Russell in order to compare determined values for a user to those of other uses of a similar demographic (Russell, ¶79).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes in view of Martin as applied to claim 8 above, and further in view of Dai (US 20190053753 filed on 10/24/15).
Regarding claim 9, the combination of Lopes and Martin teaches the method according to claim 8. However, the combination of Lopes and Martin does not teach wherein the first sleep score and the second sleep score are respectively configured with a first weight and a second weight for executing a weighted operation, and before the step of determining the specific sleep score as the sleep quality of the sleep process by executing the weighted operation on the first sleep score and the second sleep score, the method further comprises: increasing the first weight and decreasing the second weight in response to determining that the first sleep score is lower than a preset threshold value; and maintaining the first weight and the second weight in response to determining that the first sleep score is not lower than the preset threshold value.
Dai teaches wherein the first sleep score and the second sleep score are respectively configured with a first weight and a second weight for executing a weighted operation (¶66-weighting coefficients are set for each of the scoring items according to the influence of the scoring items on the sleep quality), and before the step of determining the specific sleep score as the sleep quality of the sleep process by executing the weighted operation on the first sleep score and the second sleep score (¶66-weighting coefficients for weighted averaging are set according to the influence of the scoring items on sleep quality), the method further comprises: increasing the first weight and decreasing the second weight in response to determining that the first sleep score is lower than a preset threshold value (¶109-the weighting coefficients corresponding to the scoring items, and/or corresponding relationships between the scoring items and the corresponding step scoring coefficients according to the selection instruction; while the reference doesn’t explicitly mention the situation of increasing the first weight and decreasing the second weight, the reference clearly teaches adjusting weights of each item based on a corresponding relationship); and maintaining the first weight and the second weight in response to determining that the first sleep score is not lower than the preset threshold value (¶109-the weighting coefficients corresponding to the scoring items, and/or corresponding relationships between the scoring items and the corresponding step scoring coefficients according to the selection instruction; ¶69-different weighting coefficients are set for a plurality of step scoring coefficients corresponding to the scoring items respectively, thereby achieving higher flexibility, facilitating the user to adjust a weighting ratio according to their own situation, and improving the user experience; while the reference doesn’t explicitly mention the situation of maintaining the first and second weight, the reference clearly teaches adjusting the weighting ratio based on the situation).
Dai relates to a sleep monitoring technology, and more particularly relates to a method and a device for sleep evaluation display and an evaluation equipment applicable to terminal equipment such as a mobile phone (¶1).
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 invention of Lopes to include wherein the first sleep score and the second sleep score are respectively configured with a first weight and a second weight for executing a weighted operation, and before the step of determining the specific sleep score as the sleep quality of the sleep process by executing the weighted operation on the first sleep score and the second sleep score, the method further comprises: increasing the first weight and decreasing the second weight in response to determining that the first sleep score is lower than a preset threshold value; and maintaining the first weight and the second weight in response to determining that the first sleep score is not lower than the preset threshold value of Dai in order to ensure a more accurate evaluation of sleep quality, provide the user a larger amount of information, and achieve a higher reliability of monitoring result (Dai, ¶27).
Regarding claim 10, the combination of Lopes, Martin, and Dai teaches the method according to claim 9, wherein the increased first weight is higher than the decreased second weight (Dai, ¶109-the weighting coefficients corresponding to the scoring items, and/or corresponding relationships between the scoring items and the corresponding step scoring coefficients according to the selection instruction; ¶69-different weighting coefficients are set for a plurality of step scoring coefficients corresponding to the scoring items respectively, thereby achieving higher flexibility, facilitating the user to adjust a weighting ratio according to their own situation, and improving the user experience; while the reference doesn’t explicitly mention the situation of the increased first weight being higher than the decreased second weight, the reference clearly teaches adjusting the weighting ratio based on the situation).
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 invention of Lopes to include wherein the increased first weight is higher than the decreased second weight of Dai in order to ensure a more accurate evaluation of sleep quality, provide the user a larger amount of information, and achieve a higher reliability of monitoring result (Dai, ¶27).
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 LAURA HODGE whose telephone number is (571) 272-7101. The examiner can normally be reached M-F: 8:00 am-5:00 pm.
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/L.N.H./Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792