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 .
Response to Arguments
With regards to the discussion of the 112f discussion, Applicants argue that there was no “means” for the means plus function. As discussed, the claim does not have to explicitly state “means” it can also be “a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function”. In the present case that was engine, which Applicants argue “the terms "recommendation engine" and "sleep reactivity estimator engine," where "engine" is a term of art, take their names from the functions they perform when executed by a processor”. Defining software functions/algorithms by the “function they perform” does not make it structure, the structure is the italicized portion the processor which was not previously claimed. In conclusion, the 112f is withdrawn in view of the amendment reciting the structure (the processor).
Applicant's arguments filed 3/18/26 have been fully considered but they are not persuasive.
With regards to the previous 112b rejection the rejection is amended to further clarify the issue.
With regards to the 101 rejection Applicants first argue, under Step 2A prong 1, that the claims do not contain limitations which are mental processes or mathematical concepts. First arguing the steps cannot practically be performed in the mind because they recite a processor. Examiner disagrees. When considered collectively and under the broadest reasonable interpretation, the limitations of claim 1 recite “comparing” and applying analysis (i.e. the “based on”) to the data from sensors. The steps such as “comparing” and performing an analysis in the “recommendation engine” (aka determination etc.) represents an abstract idea falling within the category of "mental processes." See MPEP § 2106.04(a). Applicants appear to be arguing that because it an “engine” which runs on a processor it thus can’t be down in the human brain. This is not persuasive, the “engine” as applicants themselves have recited is a model (aka software, an algorithm, instructions to run on the processor) which performs a function and the performing the function on the processor with the current claims set is “merely including instructions to implement an abstract idea on a computer”. Applicants next mention the sensors, the receiving of the data from the sensors is considered extra solution activity see step 2a prong 2 and step 2b. Examiner further notes that he sensors are not even claimed as performing any of the method steps. Additionally, to the extent SRI is mentioned, SRI is non-analogous to the facts of the present case when comparing the specific elements and discussion of that case with the current claims. Therefore, Examiner finds the arguments not persuasive.
With regards to the mathematical concept Applicants appear to be arguing that while the real process would require mathematical concepts the specifics of those concepts are not claimed (see 3.18.26 Remarks pg 12/20), thus relying on the explanation in Example 39. Stated differently making the claim more abstract overcomes the 101 rejection. To be clear, Examiner notes if Applicants are actually arguing how these elements are actually practiced would not include mathematical concepts (including their own recited “model”) then this would create a 112a issue, as it would be unclear how they are practiced based on Applicants statements. Examiner also notes that with regards to the comment “is at least arguably based on or involve mathematical concepts”, the underlying process undoubtedly includes mathematical concepts but Examiner recognizes the point Applicants are arguing that the underlying math is not explicitly recited (Example 39 vs. 47). In view of the above and under the current guidance, Examiner is removing the mathematical concept aspect.
Applicants next argue, under Step 2A prong 2, that the claims recites that it is integrated into a practical application. Applicants more specifically appear to be arguing that the output of a recommendation renders the claim to have a practical application. Applicants have amended in “treatment for insomnia”, however, Examiner notes treatment is not recited anywhere in their specification so it is either a 112a new matter issue or it is being interpreted in line with the “recommendations”. For the record, Examiner is interpreting it in line with the “recommendations”, thus there is no 112a rejection based on the current consideration. Furthermore, Examiner notes that the claimed “treatment” or previously claimed “at least one recommendation” is broad and is not a particular treatment or specific prophylaxis. As such the argument that the “treatment” imposes a meaningful limitation is not persuasive.
Applicant next argues that the it is an improvement to the technical field. This is not persuasive. Applicants argue that no solutions exist for “predicting the occurrence” of sleep disorder or the habits most closely related. The prior art shows there are solutions for both preexisting. Applicants themselves in the same paragraph [0005] contradict themselves “Several solutions exist for sleep disorders detection and diagnosis of insomnia. However, no solutions are available for predicting the occurrence of a specific sleep disorder”, effectively saying solutions exist for detection of insomnia and then in the next sentence saying no solutions exist for predicting insomnia. With regards to the second part about “most closely” related, Applicants aren’t claiming that they invented some new way of doing so they recite the ability already exists in a tool (see [0060]). If a new algorithm was the claimed, as opposed to the SHAP or known algorithms, there would be a 112a issue as the specification does not provide sufficient explanation for that see MPEP 2161.01. Applicants are not arguing or claiming they came up with new sensor (like some new HR sensor with less noise etc.), a new processor that processes more data on the same chip, memory that’s stores more on the same amount of space, or even that they invented a way of establishing a way of explaining the most likely parameter to an outcome (the specification recites an off the shelf algorithm and even provides a link to the prior art published code). Applicants note Enfish by citing Desjardins, the current claim set is not analogous to Enfish in that case the software was not off the shelf it was itself new, and actually did store more in the same space. Thus, for the above reasons this is not persuasive.
Lastly for 101, Applicants argue that there is significantly more under Step 2B, Examiner disagrees. The elements discussed here have been discussed above. As laid out in more detail in earlier discussion and below in the rejection, the determinations of the “engine” and the computing elements are abstract ideas the “receiving” and “outputting” are extra solution activities that do not amount to significantly more. Processing the elements on a processor also does not render it to be significantly more. As such this is not persuasive.
With regards to the 103 arguments, Applicants specifically argue that while Shouldice “may teach using historical data to make recommendations generally, there is no teaching of making recommendations specifically based on the degree to which each parameter of the historical data contributes to past insomnia of the patient.” In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The claim is rejected by the combination of references of the Shouldice (Sho) and the Molnar reference. Applicants’ argument against Shouldice appears to be that it doesn’t disclose determining the degree to which each of the parameters causes a past insomnia, but specifically calculating the degree to which each causes a result is based on Molnar. Applicant’s argument against Molnar appears to be that it doesn’t teach specifically performing its process with insomnia, but insomnia is provided by Shouldice. The issue is that what Applicants are arguing against them each individually for is based on the combination of the references not them individually. Arguing against them individually is not persuasive.
The Shouldice reference as Applicants themselves have shown through citations collects a plurality of data (having occurred, aka past) and that data is used to determine what the causes/most likely cause of the issue (wherein the issue is insomnia) and output advice/recommendations based on the most likely cause(s). Further, using previous recommendations and historical data the future determinations/recommendations are adjusted in order to provide better recommendations. Examiner notes that in order to determine what the “most likely” cause is the there is by definition some comparison between the different possible causes (ranking etc.); for example, determining the greatest deviation ([0548]). The secondary reference Molnar discloses the known process of using SHAP (SHapley Additive exPlanations) which is a method of explaining individual predictions by machine learning models by identifying the degree to which each parameter is related to the result. Taking Shouldice in view of Molnar, renders obvious computing a degree to which parameters contributed to past insomnia. Therefore, these arguments are not persuasive. Applicant next argues that there is no reason a PHOSITA would combine them, Examiner notes this is merely conclusory as they simply say there is no reason and that it is improper hindsight bias. This appears to be a mere allegation of patentability; Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Additionally, In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Finally, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Shouldice recites determining what the most likely causes is including determining associated weightings the Molnar reference discloses the known SHAP algorithm which discloses a way to determine such weightings to determine the most likely cause(s) and would have a reasonable chance of success.
The remaining discussion relies on the arguments discussed above and are not persuasive for the same reasons.
Claim Rejections - 35 USC § 112b
Claims 1, 3-10 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.
Claim 1 recites “a plurality of parameters of the patient that comprise two or more awake inputs comprising one or more of” and later recites “. . . each of the plurality of parameters . . .” this causes confusion because it is unclear what this requires. The plurality of parameters comprises two or more of the awake inputs, implies that there are two or more awake inputs and the Markush group is the list of awake inputs. Assuming so, its unclear what the “comprising one or more” is referring to as the claim seemingly already requires two or more awake inputs, this causes confusion. Examiner is unsure if there is a redundant statement in the “comprising one or more” or if this is meant to further limit somehow? Additionally, based on the claimed elements are “plurality of parameters” synonymous with “two or more awake inputs”, because parameters are defined “comprising” the “awake inputs” which are defined as the Markush group. Is there a difference between “parameters” and “awake inputs”? Examiner notes that in one interpretation, if “plurality of parameters” is broader than the awake inputs and in view of the claim language not reciting “awake inputs” again in claim 1 (or the dependents except 10), could the awake inputs be interpreted as a contingent claim element with the later recitations of the parameters being some other “plurality of parameters” not in the “awake inputs” at all (effectively non-electing any of the Markush group)? Phrased differently are the “awake inputs” required by the claim? For the above reasons, the claim does not clearly define the metes and bounds of the claim and is indefinite. The claims depending from this claim share this issue and are likewise rejected.
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 1-10 are rejected under 101 because they are directed to an abstract idea as the claims recite a mental process, see the analysis below.
Step 1
The invention claimed in claim 1 is directed to statutory subject matter as the claims recite a process.
Step 2A, Prong 1
Regarding Claim 1, the recited step of “computing a degree to which each of the plurality of parameters contributed to past insomnia”, and the step of recommendation engine determining at least one recommendation “based at least in part upon at least a subset of the plurality of parameters and further based at least in part upon the degree to which each of at least the subset of the plurality of parameters has contributed to past insomnia for treatment of the insomnia in the patient” is directed to a mental process of performing concepts in the human mind (including by a human using the aid of pen and paper). For example, this limitation simply amounts to the mental process of a clinician reading a data printout and making a mental determination as to the most relevant recommendation to the patient.
Step 2A, Prong 2
Regarding Claim 1, the judicial exception is not integrated into a practical application. The claim includes the additional elements of “during a given awake period of the patient: receiving at a processor a plurality of parameters of the patient that comprise two or more of awake inputs comprising one or more of a Heart Rate (HR), a Heart Rate Variability (HRV), a galvanic skin response, a respiration rate, a temperature, an oxygen saturation, a physical activity, a consumption of a substance, a light exposure, a workload, an emotional or physical stress, or a diary entry;” and “outputting from a recommendation engine at least one recommendation to the patient to reduce insomnia in the patient”. The steps of “receiving . . .” amounts to insignificant, extra-solution activity in that the it is data gathering; while the steps of “outputting . . .” amounts to insignificant, extra-solution activity in that the it is outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Regarding Claim 1, the judicial exception is not integrated into a practical application. The claim includes the additional elements of “during a given awake period of the patient: receiving at a processor a plurality of parameters of the patient that comprise two or more of awake inputs comprising one or more of a Heart Rate (HR), a Heart Rate Variability (HRV), a galvanic skin response, a respiration rate, a temperature, an oxygen saturation, a physical activity, a consumption of a substance, a light exposure, a workload, an emotional or physical stress, or a diary entry;” and “outputting from a recommendation engine at least one recommendation to the patient to reduce insomnia in the patient”. The steps of “receiving . . .” amounts to insignificant, extra-solution activity in that the it is data gathering; while the steps of “outputting . . .” amounts to insignificant, extra-solution activity in that the it is outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, per the Berkheimer requirement, element (1) computer processing and storage elements; (2) EEG; and, accelerometer, PPG, GPS data being sensed for analysis are well-known, routine and conventional (WRC). (1) is shown Per references: Sho (see citations below); Hen (reference cited below); US 20170055899 to Bandyopadhyay et al. (hereinafter Ban) see [0160], [0165]. (2) is shown Per references: Hen (reference cited below); Ban see [0096], [0138], [0152], [0160]. As such elements (1) and (2) are shown to be WRC.
The claim limitations when viewed individually and in combination therefore do not amount to significantly more than the abstract idea itself. The claims are therefore ineligible.
Claims 3-10 only further define the data gathering (insignificant, extra-solution activity) or the decisions made with the gathered data (i.e., only further define the mental process or mathematical concept; mathematical concept for the more specific analysis in claim 8). Therefore, the claims do not include any additional elements that show integration into a practical application and do not include any additional elements that amount to significantly more than the abstract idea. The claims are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-7, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20160151603 to Shouldice et al. (hereinafter Sho) in view of Christoph Molnar. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019. https://web.archive.org/web/20191214004638/https://christophm.github.io/interpretable-ml-book/shap.html#definition (hereinafter Molnar).
Regarding Claim 1, an interpretation of Sho discloses a method of reducing insomnia ([0058]-[0062], [0505] see also [0627], [0629]) in a patient, comprising:
during a given awake period of the patient ([0111]-[0112], [0395]):
receiving at a processor ([0111]-[0112] see also [0235], [0238], [0659]) a plurality of parameters of the patient that comprise two or more awake inputs comprising one or more of:
a physical activity ([0111]-[0112], [0395] including “amount of exercise”),
a consumption of a substance ([0111]-[0112] including “The processor may be further configured to prompt for input of user parameters comprising one or more of daily caffeine consumption, daily alcohol consumption, daily stress level and daily exercise amount.”, [0395]),
a light exposure ([0112], [0115] including “measured environmental data may comprise one or more of detected light, detected sound and detected temperature.”, [0395] including “ambient light level”),
an emotional or physical stress ([0111]-[0112], [0214] including “the system may monitor the user's heart rate, and heart rate variability in order to estimate their level of stress.”, [0395] including “users may input amount of caffeine drank during a day, amount of exercise, stress etc.”), and
a diary entry ([0520] including “journal entries”, [0575] see also [0668]); and
determining and outputting from a recommendation engine, executable by the processor ([0196] including “the system may generate sleep related output as well personalized recommendations”, [0246], [0512] see also [0114]-[0115], [0235]-[0238] system hardware processors and storage, [0659]; system gathers data determines recommendations and outputs the advice/recommendations), at least one recommendations to the patient to reduce insomnia in the patient based at least in part upon at least a subset of the plurality of parameters ([0503], [0505] including “For a user with normal sleep or (perhaps) basic insomnia, . . . the pathway may be via the advice engine to try to improve the user's sleep.”, [0520], [0548]-[0549] including “advice messages over time with respect to a detected issue may be selected based on their association with the different causes and the detected issue.” See also [0058]-[0062] discusses symptoms of insomnia, [0114]-[0115], [0395], [0550], [0627], [0668]; outputs advice/recommendations based on correlation between the cause and the issue) and further based at least in part upon which of the at least the subset of the plurality of parameters has contributed to past insomnia ([0253] including “The Advice engine is able to draw from the user's history such as previous sleep histories, previous advice given to the user and pre-sleep questionnaires answered by the user on the SmD device.”, [0505] including “For a user with normal sleep or (perhaps) basic insomnia, . . . the pathway may be via the advice engine to try to improve the user's sleep.”, [0520] including “over time, the advice engine can generate personalized advice for the user based on the user's sleep patterns, changes in sleep patterns, journal entries and a personal profile . . . . If the issue remains persistent then it will move into the advice phase for informing/correcting a user with these issues utilising the advice nuggets.”, [0537], [0546] including “given a user's history of sleep records and advices, the most relevant advice template available on the system may be selected by the processor for addressing issues with the user's most recent sleep record”, [0548]-[0550], Figs. 41-44, 51 see also [0058]-[0062], [0395], [0503], [0543], [0627], [0668]) for a treatment of the insomnia in the patient ([0503], [0505] including “For a user with normal sleep or (perhaps) basic insomnia, . . . the pathway may be via the advice engine to try to improve the user's sleep.”, [0520], [0548]-[0549] including “advice messages over time with respect to a detected issue may be selected based on their association with the different causes and the detected issue.” See also [0058]-[0062] discusses symptoms of insomnia, [0114]-[0115], [0395], [0550], [0627], [0668]; outputs advice/recommendations based on correlation between the cause and the issue, including insomnia).
As discussed above the Sho reference discloses gathering in data from various sources as discussed above identifying a most likely cause and outputting a recommendation overcome the sleep issue tied to the most likely cause with the recommendation gaining an “awareness” over time and issuing new recommendations in view of current/historical data and previously provided recommendations. An interpretation of Sho may not explicitly disclose computing a degree to which each of the plurality of parameters contributed to result; or, incorporating the user of the degree to which each of the plurality of parameters contributed to a result to determine the next recommendation.
However, in order to solve the similar issue of explanation of attribution of a feature to a result, Molnar teaches the SHAP (SHapley Additive exPlanations) method and its associated algorithmic implementations see section 5.9-5.10. More specifically as recited in 5.10.1 “The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction.” And the discussion in 5.9.1 discussing the general idea of shapley values. The SHAP processing determines the degree to which each parameter/feature contributed to the result. The combination of using the SHAP analysis (see citations above) with the disclosed process from Sho (see citations above) renders obvious the determination of the generation outputting being based at least in part upon the degree to which each of at least the subset of the plurality of parameters has contributed to insomnia in the past.
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the analysis associating the sleep issue of insomnia with its causes and outputting recommendations based on the analysis as recited by Sho to include the SHAP method because the SHAP approach to determining relevant causes provides the advantage of contrasting explanations (5.9.4, 5.10.10). Additionally, it would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the analysis associating the sleep issue of insomnia with its causes as recited by Sho to include the SHAP method because as it is merely combining the gathering of the data, identifying insomnia, determining causes and providing advice to improve the users sleep with a known specific approach to identify the most likely causes (features); or phrased differently, it is merely combining prior art elements according to known methods to yield predictable results.
Regarding Claim 3, an interpretation of Sho further discloses outputting from the recommendation engine as a plurality of recommendations as the at least one recommendation, at least some of the plurality of recommendations each being related to a corresponding parameter and being ranked in order of the degree to which the corresponding parameter has contributed to past insomnia ([0537] including “An advice message or advice nugget may be characterized in two forms, leading and trailing. A leading nugget may be related to a cause which the advice engine estimates is responsible for the issue being addressed. These might involve alcohol and caffeine levels being too high or exercise level too low and/or suboptimal environmental conditions. A trailing nugget may be related to particular causes of the sleep issues being addressed by the advice engine.”, [0548] including “The identified trend may generate a queue of advices, selecting at least one likely cause” [0549] including “A probability analysis process 4206 with respect to the issues and their relationship to cause 4204 such as measured or input information (e.g., threshold comparisions involving measured light levels, sound levels, temperature levels and other user input) may result in selection of one or more advice messages 4208 over time.”, [0550], Figs. 41-43 see also [0503]; recommendations/advice is based on ranking of what is most likely cause the problem with sleep and changes over time using the ongoing data gathering to adjust (and learn) for later).
Regarding Claim 4, an interpretation of Sho discloses the above in claim 2. Sho further discloses
determining a stress level based at least in part upon at least a portion of the plurality of parameters ([0111]-[0112], [0214] including “subjective measures (such as perceived stress level. . . ”, [0395] including “users may input amount of caffeine drank during a day, amount of exercise, stress etc.”),
inputting the stress level to a sleep reactivity estimator engine executable by the processor ([0395]-[0396], [0547]-[0549] including “In a causes process 4109 causes 4110 may be evaluated for the sleep issues based on measured factors. The potential measured factors may include: . . . (2) Lifestyle (enabled by specific issues): (a) Stress . . .”, Figs. 41-44 see also [0659], [0668]);
during a given sleep period of the patient subsequent to the given awake period ([0208], [0395] including “Other data provided in the pre-sleep questionnaire, that can prompt the user on a nightly basis for daytime sleep related information”), receiving a plurality of sleep architecture inputs of the patient and determining therefrom one or more of a Sleep Onset Latency (SOL), a Sleep Efficiency (SE), a Wake After Sleep Onset (WASO), a Total Sleep Time (TST), and an amount of time spent in each of a number of sleep stages ([0097], [0208] including “measure various physiological parameters of the user, such as a breathing rate and various sleep parameters”, [0272], [0372] gathered sleep parameters includes “Bin 1: Sleep Onset; Bin 2: Light Sleep; bin 3: Total Sleep Time (Tst); Bin 4: Deep Sleep; Bin 5:REM Sleep; Bin 6: Wake After Sleep Onset (WASO).”, [0505], [0659] see also [0668]), determining a sleep impairment based at least in part upon the plurality of sleep architecture inputs ([0097], [0208] including “measure various physiological parameters of the user, such as a breathing rate and various sleep parameters”, [0272], [0372] gathered sleep parameters includes “Bin 1: Sleep Onset; Bin 2: Light Sleep; bin 3: Total Sleep Time (Tst); Bin 4: Deep Sleep; Bin 5:REM Sleep; Bin 6: Wake After Sleep Onset (WASO).”, [0505], [0659] see also [0668]; Reading “sleep impairment” in view of Applicants [0057] including “Sleep characteristics are used in order to quantify the degree of sleep impairment. Examples of measures of sleep impairment include SOL, WASO, (1-SE), (8 hrs—TST), and others. Other measures of sleep impairment can be defined by patient dissatisfaction with sleep (e.g. on a Likert scale) or other subjective metrics.”, a value from one or more of those recited sleep parameters can be considered the “sleep impairment” value), and inputting the sleep impairment to the sleep reactivity estimator engine ([0395]-[0396], [0547]-[0549], Figs. 41-44 see also [0659], [0668]; Examiner notes that while the claim recites “inputting” the stress level and sleep impairment the claim does not recite making a determination or output); and
storing the stress level and the sleep impairment in the sleep reactivity estimator engine ([0238], [0248]-[0249], [0513] including “The advice engine may access user data, measured sleep information and trends, etc. from a user data engine service module 3706. This information may be stored in a user database 3708.”, [0546], [0595] including “the system stores data for sleep analysis and management. Such data may be included in one or more databases, such as a database accessible to the SmD and/or server(s) 3004 of the cloud system.”, Fig. 4 see also [0396], [0550]; data both raw and processed is stored).
Regarding Claim 5, an interpretation of Sho further discloses the stress level based upon at least one of a subjective input from the patient ([0111]-[0112], [0214] including “subjective measures (such as perceived stress level. . . ”, [0395] including “users may input amount of caffeine drank during a day, amount of exercise, stress etc.”)).
Regarding Claim 6, an interpretation of Sho further discloses comprising outputting from the recommendation engine the at least one recommendation additionally based at least in part upon a sleep reactivity index from the sleep reactivity estimator engine ([0395]-[0396], [0547]-[0549], Figs. 41-44 see also [0659], [0668]; output is based on correlation between stress level and impairment (i.e., one or more of the recited sleep parameters).
Regarding Claim 7, an interpretation of Sho further discloses determining an insomnia probability using an insomnia risk model executable by the processor and based at least in part upon the sleep reactivity index ([0547]-[0550], [0630]-[0631], [0634], [0657] including “classification of risky sleep may be based on a number of data inputs . . . The decision engine analyses the stored user data, applies probabilistic model and estimates the probability of risky sleep.”, Figs. 50-52 see also figs. 41-43, [0668]; the elements in [0547]-[0550] discuss determining relationships including a relationship between the user’s daily stress level and the quality of sleep (i.e., sleep reactivity) these relationships are monitored over time. If the system determines based on the elements including the aforementioned relationship that there is a probability of chronic insomnia occurring and output corresponding recommendations up to and including see a medical professional); and outputting from the recommendation engine the number of recommendations further based at least in part upon the insomnia probability ([0547]-[0550], [0630]-[0631] including “This facilitates the user in receiving the appropriate advice and support required if a sleep issue is detected.”, [0634], [0657] including “classification of risky sleep may be based on a number of data inputs . . . The decision engine analyses the stored user data, applies probabilistic model and estimates the probability of risky sleep.”, Figs. 50-52 see also figs. 41-43, [0668]).
Regarding Claim 10, an interpretation of Sho further discloses receiving as the two or more awake inputs one or more of a global positioning system (GPS) input ([0196], [0241], [0535] including “check if the user is travelling”), an accelerometer input ([0206], [0384]; accelerometer data is gathered when user is both awake and asleep).
Claim Rejections - 35 USC § 103
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sho in view of Molnar and further in view of US 20160270718 to Heneghan et al. (hereinafter Hen). Hen is recited in the IDS dated 12/29/21.
Regarding Claim 8, an interpretation of Sho in view of Molnar discloses the above in claim 6, including the determination of sleep reactivity index based on stress level as discussed in the rejection of claim 4 above; Also, per [0214] Sho recognizes monitoring HR/HRV to in order to determine a person’s level of stress.
An interpretation of Sho in view of Molnar may not explicitly disclose determining the stress level based at least in part upon a frequency domain analysis of the HRV and an analysis of a plurality of features that are extracted from the power spectrum density of the HRV.
However, in the same field of endeavor (diagnostic medical systems), Hen teaches determining the stress level based at least in part upon a frequency domain analysis of the HRV and an analysis of a number of features that are extracted from the power spectrum density of the HRV ([0080] including “stress may also manifest in daytime fatigue, and may be monitored based on daytime physical activity data 115, daytime vital signs data 118, and objective sleep measures 120 (described below).”, [0082] including “sleep problems may be associated with reduced daytime heart rate variability (HRV).”, [0086] including “the ratio of HF (high frequency) to LF (low frequency) power in the heart rate spectrum may be used to estimate the parasympathetic nervous activity, with reduced parasympathetic component (of the autonomic nervous system) suggesting increased stress level and increased fatigue” see also [0087]-[0088]; Examiner notes that Applicants themselves, see [0035]-[0047], have recognized features of power spectral density (PSD) of the frequency include LF and HF. Hen recites Analyzing the LF and HF to be representative of stress).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the receiving of the data, analysis to correlations between the sleep issues and causes of such issues and outputting relevant recommendations as recited by Sho in view of Molnar to include determining the stress level using PSD for determining the stress level as recited by Hen because it is the simple substitution of one known element (stress level provided by user as recited by Sho) for another (stress level determined using PSD as recited by Hen) to obtain predictable results.
Regarding Claim 9, an interpretation of Sho in view of Molnar discloses the above in claim 6, including the determination of sleep reactivity index based on stress level as discussed in the rejection of claim 4 above.
An interpretation of Sho may not explicitly disclose determining the stress level based at least in part upon an electroencephalogram (EEG) input from the patient.
However, in the same field of endeavor (diagnostic medical systems), Hen teaches determining the stress level based at least in part upon an electroencephalogram (EEG) input from the patient ([0080] including “stress may also manifest in daytime fatigue, and may be monitored based on daytime physical activity data 115, daytime vital signs data 118, and objective sleep measures 120 (described below).”, [0082] including “sleep problems may be associated with reduced daytime heart rate variability (HRV).”, [0086] including “the ratio of HF (high frequency) to LF (low frequency) power in the heart rate spectrum may be used to estimate the parasympathetic nervous activity, with reduced parasympathetic component (of the autonomic nervous system) suggesting increased stress level and increased fatigue. . . Heart rate data (and thus HRV) can also be obtained . . . or headbands with EEG electrodes for example.” see also [0087]-[0088]; Examiner notes that Applicants themselves, see [0035]-[0047], have recognized features of PSD of the frequency include LF and HF. Hen recites Analyzing the LF and HF to be stress).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the receiving of the data, analysis to correlations between the sleep issues and causes of such issues and outputting relevant recommendations as recited by Sho in view of Molnar to include determining the stress level using EEG data for determining the stress level as recited by Hen because it is the simple substitution of one known element (stress level provided by the user as recited by Sho) for another (stress level determined using EEG as recited by Hen) to obtain predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Scott Lundberg, An introduction to explainable AI with Shapley values, https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html, 2018 (based on the copyright)
US 20140276119 [0342] including “Stress may be quantified or evaluated through heart rate, heart rate variability, skin temperature, changes in motion-activity data and/or galvanic skin response.”
US 20210321942 discusses health determinations including sleep issues and recites the SHAP method/algorithm as an approach to determine the most important features
US 20220287632 discusses health determinations including sleep interruptions and recites the SHAP method/algorithm as an approach to determine the most important features
US 20230048000 to O’Mahony et al. (hereinafter Mah). Mah incorporates by reference in its entirety International Publication No. WO 2015/006364 to Shouldice et al. in [0102]. Sho is a US publication of the WO publication. Examiner notes the WO reference is recited in the IDS dated 12/29/21.
THIS ACTION IS MADE FINAL. 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 JAMES R MOSS whose telephone number is (571)272-3506. The examiner can normally be reached Monday - Friday (9:30 am - 5:30 pm).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Unsu Jung can be reached at (571)272-8506. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/James Moss/ Examiner, Art Unit 3792
Prosecution Insights