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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Re. Claim 14: Claim 14 recites “third in-bed state monitoring result” and “fourth in-bed state monitoring result.” However, none of the preceding claims in the dependency chain (i.e., claim 7 and claim 1) recite a “first” or “second” in-bed monitoring result. Thus, it is unclear whether prior in-bed state monitoring results are required to determining a third and fourth in-bed state monitoring result, and the number of in-bed state monitoring results required by the claim is indefinite.
Claim 15 is rejected as being dependent upon rejected claim 14 under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of the claims recites steps or instructions for ascertaining and processing data to assess quality of sleep of subject, which is grouped as a mental process. Accordingly, each of the claims recites an abstract idea.
Independent claim 1 recites:
determining, by a processor, sleep data of a subject (data-gathering and/or evaluation, judgement, or observation, additional element);
extracting, by the processor, sleep feature data based on a reference core sleep period of the subject and the sleep data (evaluation, judgement, or observation); and
evaluating, by the processor, sleep quality of the subject based on the sleep feature data (evaluation, judgement, or observation).
As indicated above, the independent claim recites at least one step or instruction grouped as a mental process. Therefore, each of the independent claims recites an abstract idea. Each limitation, aside from language reciting a generic computer components, can be grouped as a mental process (see italicized portions above), and is addressed as follows:
The limitation of determining… sleep data of a subject can be interpreted as obtaining sleep data (e.g., extra-solution activity) and/or performing observation, evaluation, or judgement on requisite sensor data to form an assessment relative to a user’s sleep based on such sensor data, which is a mental process.
The limitation of extracting, by the processor, sleep feature data based on a reference core sleep period of the subject and the sleep data can be similarly interpreted as performing observation, evaluation, or judgement on acquired sleep data to identify features thereof, which is a mental process.
The limitation of evaluating, by the processor, sleep quality of the subject based on the sleep feature data is an evaluation carried out by means of a generic computer process, whereby the evaluation itself is a mental process.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
The dependent claims merely include limitations that either further define the abstract idea (further steps which are entirely embodied in the mental process) or extra-solution activity (e.g. limitations relating to the data gathered), or implementation of steps of the abstract idea via generic computer components or well-understood, routine, and conventional structures. Thus, such claims amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial), collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), collection, storage, and recognition of data (Smart Systems Innovations).
Step 2A, Prong 2
The above-identified abstract idea is not integrated into a practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use.
More specifically, independent claim 1 recites the additional element of a processor, which is recited at a high-level of generality (i.e., as a generic processors and memory performing a generic computer function of performing calculations and storing data, respectively) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, such an additional element does not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified generically recited elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea is not integrated into a practical application.
Moreover, the above-identified abstract idea is not integrated into a practical application under because the claimed method and system merely implements the above-identified abstract idea using rules (e.g., computer instructions) executed by a computer (e.g., processor as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract ideas identified above in the independent claims (and their respective dependent claims) are not integrated into a practical application.
Dependent claim 7 recites the use of a wearable device. Such an additional element is generically recited element, and does not improve the functioning of a computer or any other technology or technical field. Claim 7 recites merely acquiring data from a generically recited sensor, having no operative connection to the processor besides communication of obtained data, which amounts to insignificant, extra-solution activity in the form of mere data gathering, which does not constitute an integration into a practical application. Although the sensors may imply particular structure, their use in the mental process is merely extra-solution. See MPEP 2106.05(b).III:
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted)”
Dependent claim 18 similarly claims a wearable device, and is interpreted similarly to claim 7. Having a memory communicatively coupled to a processor of the wearable device itself does nothing to improve the functioning of a computer or any other technical field and is interpreted as generally linking the use of the computer-implemented abstract idea to a particular technological environment or field of use (e.g., smart wearables).
Dependent claims 17, 19, and 20 merely recite implementation of steps of the abstract idea via generic computer components. See analysis of a processor as recited in claim 1.
Accordingly, the claims are each directed to an abstract idea.
Step 2B
None of the claims include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than the abstract idea for at least the following reasons:
Independent claim 1 recites a processor.
As stated in Step 2A, Prong Two, the processor is generically recited, and, when viewed as part of the claim as a whole, merely implements the abstract idea via generic computer components. As per MPEP 2106.05:
“Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f))…”
Dependent claim 7 requires a wearable device. However, Applicant discloses that the steps of the invention may be carried out via an electronic device, encompassing “various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and the like” (Paragraph 0236). Applicant’s disclosure is not particular regarding the particular structure of such devices, including wearable devices. No special programming or algorithms is indicated for how such sensors operate. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since these hardware components merely perform non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that these hardware components are conventional and performs well understood, routine and conventional activities in the medical technology industry or medical technology arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of what is described as a wearable device in the context of the claims because the disclosure describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
Dependent claim 18 requires that the steps of the invention be carried out by a wearable device comprising:
at least one of an acceleration sensor and a physiological sensor;
one or more wearable accessories;
at least one processor; and
a memory communicatively coupled to the at least one processor.
The concept of a wearable device comprising each of the above elements is known as a “smart wearable,” and is considered well-understood, routine, and conventional, by at least:
Zeller (US 20200251206 A1) – Paragraph 0006: “On the other hand a plurality of so-called “wearables” exists nowadays (e.g. fitness bracelets, Apple Watch, Samsung Gear), which collect a plurality of physiological data of the person wearing them;” Paragraph 0053: “The physiological data recorded by wearables is evaluated as a rule on servers of the provider (e.g. FitBit or Apple) and can be displayed in an app;”
Bonistalli (US 20230040102 A1) – Paragraph 0002: “There are physiologic monitoring wearables, for example, for gauging biofeedback, health, sleep, fitness, metabolism, and vital status. These devices can process physiological signals that may be obtained from one or more sensors, and may be configured to extract one or more physiological metrics from physiological waveforms;”
Krishnan et al. (US 20220061701 A1) – Paragraph 0002: “The development of smart wearable devices within the IoT (Internet of Things) framework, has promoted the use of miniature sensors such as accelerometers, gyroscopes, and magnetometers to capture large amounts of sensor data from the human body to monitor daily activity. In one example, wearable devices use an accelerometer to detect physical activity. An accelerometer sensor provides three-dimensional coordinates that measure the acceleration force. Typically, accelerometer-based wearables, such as actigraphs, generate signals within the sampling rate of 16-2000 Hz with a resolution of 8-16 bits/sample. Currently available accelerometer-based wearables such as smartwatches generally over-quantize (i.e. quantize motion data more than necessary) and tend to sample the accelerometer sensor's signal infrequently.”
Additionally, each of the citations provided in for claim 18 further demonstrate that the wearable device configured to obtain an acceleration signal as recited in claim 7 is also well-understood, routine, and conventional.
Dependent claim 6 requires a tree model as well as a pre-trained neural network, which may both be interpreted as abstract ideas of evaluation carried out through generic computer processors. Additionally, the recitation that the neural network is “pre-trained” with no training steps or structure of the network provided in Applicant’s disclosure indicates that the skilled artisan is well-apprised of how such algorithms/statistical tools operate; thus, even if the pre-trained neural network were considered an additional element and not an abstract idea, such a concept is described in Applicant’s disclosure in such a way as to demonstrate its well-understood, routine, and conventional nature.
Each other dependent claim merely recites steps which further define the abstract idea and data/data-processing steps. Examiner notes that the dependent claims recite limitations which are extra-solution or part of the abstract idea itself do not constitute significantly more. See MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process.
The recitation of the above-identified additional limitations in the claims amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
For at least the above reasons, the claims are directed to applying an abstract idea on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. In other words, none of the claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment (processing of sensor data). That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself, or (ii) provide a technical solution to a problem in a technical field.
Therefore, none of the claims amounts to significantly more than the abstract idea itself.
Accordingly, the claims are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-12, 14, 17, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by:
Mollicone et al. (US 20120232414 A1) (hereinafter – Mollicone).
Re. Claim 1: Mollicone teaches a method for sleep quality assessment (Figs. 3, 4: determination of sleep stress index),
comprising:
determining, by a processor, sleep data of a subject (Figs. 3-6A: obtaining sleep data; Paragraph 0008: “…cause the processor to carry out the methods of the invention”);
extracting, by the processor, sleep feature data based on a reference core sleep period of the subject and the sleep data (Paragraph 0076: “…determining a difference metric between a value of a sleep parameter S.sub.i determined in a time interval of interest and a reference sleep parameter value”); and
evaluating, by the processor, sleep quality of the subject based on the sleep feature data (Paragraph 0076: “… determining the sleep stress index I.sub.s in block 307 may comprise determining a difference metric between a value of a sleep parameter S.sub.i determined in a time interval of interest and a reference sleep parameter value;” Examiner notes that a sleep index is a metric interpretable as “sleep quality”).
Re. Claim 2: Mollicone teaches the invention according to claim 1. Mollicone further teaches the invention wherein determining, by the processor, the sleep data of the subject comprises:
obtaining physiological data of the subject in a case that an in-bed state of the subject within a predetermined time period is not out of bed (Paragraph 0059: activity data is obtained, including while in bed since “rest in bed” is a classification for periods of time segments); and
performing sleep recognition on the physiological data to obtain sleep data of the subject (Paragraphs 0059, 0086, 0087: classification of sleep based on obtained activity data).
Re. Claim 3: Mollicone teaches the invention according to claim 1. Mollicone further teaches the invention wherein the reference core sleep period comprises
an individual core sleep period of the subject (Paragraph 0066: “Non-limiting examples of sleep parameters… include: total sleep time (TST); time in bed (TIB)… average length of sleep episodes… and/or the like in suitable time period (e.g. a 12 or 24 hour time period or a time period of interest);” Paragraph 0067: “Each block 303 sleep parameter S.sub.i is representative of a corresponding characteristic of human sleep… Such characteristics of human sleep may include: sleep quality, sleep duration, sleep timing, and/or the like;” Paragraph 0076: particularly, “… determining the reference sleep parameter value based on a previously determined sleep parameter value for the same subject for a time interval different than the time interval of interest…;” Paragraph 0087: “In some embodiments, other sleep history data sources 404 may comprise: a software module producing sleep history data as an output (e.g. software that interacts with an individual to interrogate the individual about their sleep history); a database (e.g. containing historical sleep history data, baseline sleep history data, population average sleep history data and/or the like)”) and
a collective core sleep period of a group in an area to which the subject belongs (Paragraph 0076: “…determining the reference sleep parameter value based on sleep parameter values associated with one or more averages of one or more identified populations, including in some embodiments a representative sample of the population at large; and/or the like;” see citations of Paragraphs 0066, 0067, 0087 above); and
wherein extracting, by the processor, the sleep feature data based on the reference core sleep period of the subject and the sleep data comprises:
determining a first sleep feature of the subject based on the sleep data and the individual core sleep period (Fig. 3: step 301: sleep data received in step 301, sleep parameters determined in step 303; Fig. 4: sleep data includes sleep history, sleep estimator data, and physical activity history data; Paragraphs 0066, 0067: parameters extracted or representative values thereof based on an individual core sleep period may include total sleep time (TST), average length of sleep episodes, sleep duration, sleep timing);
determining a second sleep feature of the subject based on the sleep data and the collective core sleep period (see population sleep data considered at Paragraphs 0076, 0087; Fig. 4: sleep parameters generated at element 412 based on population data from sleep history data sources 404); and
determining the sleep feature data based on the first sleep feature as well as the second sleep feature (Paragraph 0076: “…determining a difference metric between a value of a sleep parameter S.sub.i determined in a time interval of interest and a reference sleep parameter value;” see previous citations regarding parameters extracted including individual parameters and population parameters based on respective sleep periods).
Re. Claim 4: Mollicone teaches the invention according to claim 1. Mollicone further teaches the invention further comprising:
obtaining attribute information of the subject (Fig. 4: physical activity history data 416 derived from actigraph sensor 401 and other physical activity history sources 403); and
wherein evaluating, by the processor, the sleep quality of the subject based on the sleep feature data comprises:
evaluating the sleep quality of the subject based on the sleep feature data and the attribute information (Fig. 4: sleep stress index estimator 407 (i.e., estimating sleep stress as a sleep quality) takes data from sleep history data 415, other sleep history data sources 404, and physical activity history data 416).
Re. Claim 5: Mollicone teaches the invention according to claim 4. Mollicone further teaches the invention wherein evaluating the sleep quality of the subject based on the sleep feature data and the attribute information comprises:
obtaining a sleep quality assessment model (Paragraph 0077: sleep stress index calculated based on sleep function comprising weighting parameters and fitting thereafter; Paragraph 0086: sleep estimator 405 (feeding into sleep stress index estimator 407) includes probability-based techniques and/or heuristic algorithms);
inputting the sleep feature data and the attribute information into the sleep quality assessment model to obtain a sleep discomfort symptom (Fig. 4: see blocks 404, 405, 415, 412 used to determine parameters for sleep stress estimator 407 to identify a sleep stress (i.e., interpretable as a “sleep discomfort symptom”)); and
determining the sleep discomfort symptom as a sleep quality assessment result of the subject (see previous citation: a sleep stress is interpretable as a sleep discomfort symptom, and thus also determinable as one).
Re. Claim 7: Mollicone teaches the invention according to claim 1. Mollicone further teaches the invention further comprising:
before determining, by the processor, the sleep data of the subject:
obtaining an acceleration signal output by a wearable device associated with the subject for a predetermined time period (Fig. 4: actigraphy sensor 401; Paragraph 0031: “An actigraphy sensor measures the movements of a user's body typically (although not exclusively) by employing an accelerometer”);
determining a motion feature of the subject during the predetermined time period based on the acceleration signal (Paragraph 0033: “Physical activity history data may include without limitation: a time series of activity counts, a sequence of times representative of physical activity, a rate of change of gross motor activity, translational motion rates, rotational motion rates, a set of discrete samples indicative of amplitudes of gross motor movement(s) and/or other characteristics related to gross motor movement(s) of the individual;” Paragraph 0080: particularly, “Gross motor activity of the individual to which actigraphy sensor 401 is attached may result in translational movement of actigraphy sensor 401, which is detected by the accelerometer. Non-limiting examples of gross motor activity include arm movements, walking, running, and/or the like;” Paragraph 0059: classification of time segments as “wake,” “sleep,” and “resting in bed” states based on activity levels; similar subject matter recited in Paragraph 0086);
determining a posture feature of the subject during the predetermined time period based on the acceleration signal (Fig. 4: physical activity history data is entered into sleep estimator 405; Paragraph 0080: gross motor activity includes determination of movements including changes in posture, such as “arm movements, walking, running, and/or the like;” Paragraph 0086: “Sleep estimator 405 processes physical activity history data 416 to generate sleep history data 415”); and
determining an in-bed state of the subject during the predetermined time period based on the posture feature and the motion feature (Paragraph 0086: physical activity history data (including gross motor activity and also encompassing posture changes) is processed to classify sleep periods, and particularly “resting in bed,” and “sleep”).
Re. Claim 1: Mollicone teaches the invention according to claim 7. Mollicone further teaches the invention wherein the acceleration signal comprises acceleration values at a plurality of moments (see prior citations of how an accelerometer outputs data), and
wherein determining the motion feature of the subject during the predetermined time period based on the acceleration signal comprises:
dividing the predetermined time period into a plurality of time windows based on a specified time length (Paragraph 0057: “In some embodiments, the block 306 physical activity history data may be separated into multiple time segments or may otherwise comprise multiple time intervals of interest and block 305 may involve the determination of one or more corresponding physical activity parameters P.sub.i for each time segment/interval”);
determining a type label corresponding to each time window based on the acceleration values at the plurality of moments within the time window, wherein the type label is used for characterizing an activity state of the subject during a corresponding time window (Paragraph 0059: “In some embodiment heuristic rules may be combined with activity count threshold classifications, non-limiting examples of which include: classifying three or more consecutive time segments with activity counts below a low threshold value as sleep; classifying time segments as "rest in bed" if it has activity counts below a medium threshold level, and is adjacent to a segment previously classified sleep; and/or the like. Using techniques known to those skilled in the art, periods of extended minimal activity may be classified at sleep. Periods of low activity at the beginning and end of a sleep period may be classified at resting in bed”); and
determining the motion feature of the subject during the predetermined time period based on the type labels of the time windows within the predetermined time period (Paragraph 0059: classification of time segments as “wake,” “sleep,” and “resting in bed” states based on identified activity levels per time segment; similar subject matter recited in Paragraph 0086).
Re. Claim 9: Mollicone teaches the invention according to claim 8. Mollicone further teaches the invention wherein determining the motion feature of the subject during the predetermined time period based on the type labels of the time windows within the predetermined time period comprises:
determining a moment corresponding to an activity change point within the predetermined time period based on the type label corresponding to each of the plurality of time windows (Figs. 5A, 5B, as described in Paragraphs 0059, 0086: classification of activity data as “wake,” “sleep,” and “rest in bed;” Examiner notes that classification of time segments in these classes also determines moments of when such classes change, i.e., an activity change); and
determining a time interval between each time window and an adjacent previous activity change point (see previous citation – since moments of time changes are known, time intervals therebetween are also known; Examiner notes that the claim does not require any evaluation to be performed after determination of a time interval).
Re. Claim 10: Mollicone teaches the invention according to claim 8. Mollicone further teaches the invention wherein determining the motion feature of the subject during the predetermined time period based on the type labels of the time windows within the predetermined time period comprises at least one of:
determining a time window type sequence within the predetermined time period based on the type labels of the time windows within the predetermined time period (see citations of rejection of claim 9; additionally, see Paragraph 0059: “classifying time segments based on activity as "rest in bed" if it has activity counts below a medium threshold level, and is adjacent to a segment previously classified sleep;” Examiner notes that determination of adjacency is a determination of a sequence; Paragraph 0033: determination of physical activity history data (e.g., input into sleep estimator 405 at Fig. 4) is determined as “a sequence of times representative of physical activity”); or
determining a number of windows of each type within the predetermined time period based on the type labels of the time windows within the predetermined time period (see citations of rejection of claim 9; additionally, see Figs. 5A, 5B: time segments labeled as sleep 522, 571A, 571B requires identification of a number of time segments corresponding to sleep, and a similar operation is performed for time segments 521A, 521B, 572A, 572B, 572C, 572D labeled “resting in bed;” see similarly, Figs. 6A, 6B).
Re. Claim 11: Mollicone teaches the invention according to claim 7. Mollicone further teaches the invention wherein the acceleration signal comprises acceleration values at a plurality of moments
(Paragraph 0031: “An actigraphy sensor measures the movements of a user's body typically (although not exclusively) by employing an accelerometer… Typical actigraphy sensors use the output of the accelerometers to generate so called ‘counts’”), and
wherein determining the posture feature of the subject during the predetermined time period based on the acceleration signal comprises:
dividing the predetermined time period into a plurality of time windows based on a specified time length (see “counts” recited in Paragraph 0031, and/or Paragraph 0031: “…physical activity history data of an individual should be understood to comprise data representative of gross motor activity of the individual. Physical activity history data may include without limitation: a time series of activity counts, a sequence of times representative of physical activity, a rate of change of gross motor activity, translational motion rates, rotational motion rates, a set of discrete samples indicative of amplitudes of gross motor movement(s) and/or other characteristics related to gross motor movement(s) of the individual”);
determining a window acceleration corresponding to each time window based on the acceleration values at the plurality of moments within the time window (see previous citation of Paragraph 0033: determination of “rate of change” and “motion rates” requires determining changes in acceleration values over time);
in a case where the window acceleration corresponding to a time window is within a specified range, determining an acceleration vector corresponding to the time window (Fig. 5A: see threshold at top graph, and see high activity flags as described in Paragraph 0060); and
determining the posture feature of the subject during the predetermined time period based on the acceleration vector (see citations above – since determining “high” and “low activity” includes identifying acceleration values corresponding to large movements (e.g., “Non-limiting examples of gross motor activity include arm movements, walking, running, and/or the like”), and low movements (below threshold of activity shown in Fig. 5A, encompassing movements which correspond to what can be detected as having less gross motion or an in-bed state, i.e., lying down)).
Re. Claim 12: Mollicone teaches the invention according to claim 7. Mollicone further teaches the invention wherein determining the in-bed state of the subject during the predetermined time period based on the posture feature and the motion feature comprises:
determining a first in-bed state monitoring result of the subject during the predetermined time period based on the posture feature;
determining a second in-bed state monitoring result of the subject during the predetermined time period based on the motion feature; and
determining the in-bed state of the subject during the predetermined time period based on the first in-bed state monitoring result and the second in-bed state monitoring result
(see citations of rejection of claim 11 – “activity history data” as described in Mollicone encompasses both motion features (accelerometer signals including translational and rotational motion and rates of change of motion sequences) as well as posture features (“Non-limiting examples of gross motor activity include arm movements, walking, running, and/or the like”).
Re. Claim 14: Mollicone teaches the invention according to claim 7. Mollicone further teaches the invention
wherein the predetermined time period comprises a plurality of time segments,
the motion feature of the subject during the predetermined time period comprises a motion feature corresponding to each of the plurality of time segments, and
the posture feature of the subject during the predetermined time period comprises a motion feature corresponding to each of the plurality of time segments; and
wherein determining the in-bed state of the subject during the predetermined time period based on the posture feature and the motion feature comprises:
determining a third in-bed state monitoring result of the subject within each time segment based on a posture feature corresponding to the time segment;
determining a fourth in-bed state monitoring result of the subject within each time segment based on a motion feature corresponding to the time segment; and
determining, based on the third in-bed state monitoring result and the fourth in-bed state monitoring result corresponding to each time segment within the predetermined time period, in-bed states of the subject corresponding to the plurality of time segments within the predetermined time period
(see citations of rejection of claim 7: as best understood, Mollicone contemplates detection of a variety of gross motor activity (i.e., a posture feature), as well as a variety of motion, rates thereof, rates of change thereof, and activity levels therefrom; thus, Mollicone also contemplates additional monitoring results arising from both a posture feature and a motion feature, and identifying sleep states therefrom).
Re. Claim 17: Mollicone teaches an electronic device (Fig. 4: processor 450),
comprising:
at least one processor (see previous citation); and
a memory communicatively coupled to the at least one processor (Paragraph 0085: “Processor 450 may comprise internal memory (not shown) and/or have access to external memory (not shown). Processor 450 may be programmed with, or otherwise have access to, software (not shown) which may cause processor 450 to implement at least portions of the methods described herein”);
wherein the memory stores instructions executable by the at least one processor, and execution of the instructions by the at least one processor causes the at least one processor to perform the method of claim 1 (see previous citation).
Re. Claim 19: Mollicone teaches a non-transitory computer readable storage medium, having computer instructions stored thereon, wherein the computer instructions are configured to cause a computer to perform the method of claim 1 (Paragraph 0008: “Other aspects of the invention provide computer program products comprising computer instructions which, when executed by a processor, cause the processor to carry out the methods of the invention”).
Re. Claim 20: Mollicone teaches a computer program product comprising a computer program, the computer program, when executed by a processor, implements the method of claim 1 (Paragraph 0008: “Other aspects of the invention provide computer program products comprising computer instructions which, when executed by a processor, cause the processor to carry out the methods of the invention”).
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over:
Mollicone et al. (US 20120232414 A1) (hereinafter – Mollicone) in view of
Narayanan et al. (US 20220148708 A1) (hereinafter – Narayanan).
Re. Claim 6: Mollicone teaches the invention according to claim 5 but does not teach the invention wherein the sleep quality assessment model is a tree model; and
wherein obtaining the sleep quality assessment model comprises:
acquiring training data, wherein the training data comprises a predefined number of sleep feature samples;
determining sleep discomfort symptom samples corresponding to the sleep feature samples based on a pre-trained neural network model and the predefined number of the sleep feature samples;
training an initial tree model with the sleep feature samples and the corresponding sleep discomfort symptom samples to obtain a trained tree model; and
using the trained tree model as the sleep quality assessment model.
Narayanan teaches analogous art in the technology of processing physiological data from wearable devices using machine learning techniques (Paragraph 0007). Narayanan further teaches a model formed by:
acquiring wherein the training data comprises a predefined number of feature samples (Paragraph 0042: “…training an algorithm which know classifications (e.g., form self-reporting) to be used in classification. A combination of self-report data, coding of interviews, observations, videos, and/or audio recordings can be compared to the signal-derived features to determine the accuracy of the systems”);
determining another set of samples corresponding to the feature samples based on a pre-trained neural network model and the predefined number of the feature samples (Fig. 4A; Paragraph 0042: “Machine learning methods can then be used to retroactively determine what features or combinations thereof predict changes… The objective of the autoencoder is to learn a reduced representation of the original feature set”);
training an initial tree model with the feature samples and the other set corresponding samples to obtain a trained tree model (Paragraph 0042: “The reduced feature set is obtained from the bottleneck layer, which is learned so that the right side of the autoencoder network can generate a representation as close as possible to the original signal from the reduced encoding. From the bottleneck layer, inputs for drafting a binary decision tree are extracted”); and
using the trained tree model as the assessment model (Figs. 4A, 4B; Paragraph 0042: “Once trained, the neural network and the decision tree can be used to perform the classification. FIG. 4B is an example of a decision tree. In a variation, statistics, e.g., regression analyses, latent class analysis can be used to predict changes in relationship functioning”).
Thus, the technique of identifying features from a particular data set using a trained neural network to then input features to train a decision tree model to perform classification is known from the citations above. The skilled artisan, when applying the model of Narayanan to the invention of Mollicone for the purpose of assessing sleep quality (e.g., a sleep stress index), would necessarily perform the steps of utilizing data identifiable as “sleep feature samples” and “sleep discomfort symptom samples” since these are merely terms for the relevant data to be analyzed by the algorithm. The modification of Mollicone to utilize the model of Narayanan may be viewed as a simple substitution of two statistical/machine learning methods for performing the same purpose (e.g., forming predictions based on input data), or, alternatively, as an inclusion of an alternative model to further bolster or reinforce the assessment of the model of Mollicone.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over:
Mollicone et al. (US 20120232414 A1) (hereinafter – Mollicone) in view of
Surbur et al. (US 20170156666 A1) (hereinafter – Surbur).
Re. Claim 6: Mollicone teaches the invention according to claim 7 but does not teach the invention further comprising:
after determining the in-bed state of the subject during the predetermined time period, generating at least one sleep suggestion for the subject based on the in-bed state of the subject and the sleep data of the subject.
Surbur teaches analogous art in the technology of analyzing sleep activity (Paragraphs 0005-0007). Surbur teaches the invention further comprising:
after determining the in-bed state of the subject during the predetermined time period, generating at least one sleep suggestion for the subject based on the in-bed state of the subject and the sleep data of the subject (Paragraphs 0005-0007, 0010, 0011: insight engine includes generation of suggestion based on sleep activity of user).
It would have been obvious to one having skill in the art before the effective filing date to have modified Mollicone to have included an insight engine to include generation of a suggestion based on the sleep activity of a user as taught by Surbur, the motivation being that doing so allows the device to guide the user to a particular sleep goal, and particularly, provides beneficial suggestion relative to scheduled events or physical activity (Paragraph 0057).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over:
Mollicone et al. (US 20120232414 A1) (hereinafter – Mollicone) in view of
Krishnan et al. (US 20220061701 A1) (hereinafter – Krishnan).
Re. Claim 6: Mollicone teaches a memory communicatively coupled to a processor, wherein the memory stores instructions executable by the at least one processor, and execution of the instructions by the at least one processor causes the at least one processor to perform the method of claim 1 (see of claim 1).
Mollicone does not teach a wearable device, comprising:
at least one of an acceleration sensor and a physiological sensor;
one or more wearable accessories;
at least one processor; and
a memory communicatively coupled to the at least one processor.
Krishnan teaches analogous art in the technology of smart wearables (Abstract). Krishnan further teaches wearable device (Paragraphs 0001-0002: wearable actigraph sensors are routine), comprising:
at least one of an acceleration sensor and a physiological sensor (Paragraph 0022: smart wearables embedded with accelerometers);
one or more wearable accessories (see previous citations – a “smart wearable” implies that such the device comprises a “wearable accessory,” e.g., an accessory which allows the device to be worn);
at least one processor (Paragraph 0022: smart wearables embedded with accelerometers process physiological data therefrom, thus implying the existence of a processor for performing such steps); and
a memory communicatively coupled to the at least one processor (Paragraph 0025: “As noted above, the smart device(s) 102 can include smart electronic wearables such as smart watches, actigraphs, electronic textiles such as smart electronic clothing, smart electronic fabrics which include accelerometers or other sensors for capturing activity data;” Fig. 1: wearable smart device 102 having instructions module 103 or communication to handheld smart device 124, computing device 126, or storage server 128).
It would have been obvious to one having skill in the art before the effective filing date to have modified the system of Mollicone to be integrated into a single wearable device, the motivation being that doing so enables the system to be more compact and portable.
Examiner’s Note
Claims 13 and 15 are not provided with prior art rejections.
Claim 13 recites a particular determination of an in-bed state based on a non-matching result between a first in-bed monitoring result based on a posture feature and a second in-bed monitoring result based on a motion feature. Such a limitation is not taught or suggested in the prior arts of reference.
Claim 15 requires a particular method of determining in-bed states using specific time segments, which is a process not taught or suggested in the prior arts of reference.
Conclusion
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/JUSTIN XU/ Primary Examiner, Art Unit 3791