DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the present action filed on 12/01/2025.
Claims 1, 17, 20, 3639, 49, and 55 are currently amended.
Claims 8-10, 27-29, and 46-48 have been canceled.
Claims 1-7, 11-26, 30-45, and 49-57 are currently pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 21, 2025 were filed after the mailing date of the first action on the merits. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112(b)
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 1-7, 11-26, 30-45, and 49-57 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.
Claims 1, 20, and 39 recite using “supervised and unsupervised machine learning modules” and then recite “in said machine learning model”. It is unclear which of the supervised or unsupervised modules is being referred to. Claims 2-7, 11-19, 21-26 30-38, 40-45 and 49-57 are further rejected as being dependent on a rejected claim. Appropriate correction is required.
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 1-7, 11-26, 30-45, and 49-57 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1, 20, and 39 are within the four statutory categories. Claims 1, 20, and 39 are directed to a method, system, and non-transitory computer-readable storage medium (i.e. machine) respectively. Dependent Claims 2-7 and 11-19 are drawn toward a method, 21-26 and 30-38 are drawn toward a system, and 40-45 and 49-57 are drawn toward a non-transitory computer-readable storage medium, and therefore also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A computer-implemented method for modeling biobehavioral rhythms of a subject, said method comprising:
receiving sensor data collected from a mobile device and/or wearable device;
extracting specified sensor features from said received sensor data;
modeling biobehavioral rhythms for each of said extracted specified sensor features to provide modeled biobehavioral rhythm data of the subject;
determining rhythmicity characteristics of cyclical behavior of said modeled biobehavioral rhythm data of the subject;
measuring stability of said determined rhythmicity characteristics of the subject across different time windows and/or across different populations to determine the deviation of the subject's rhythmicity characteristics from normal rhythmicity characteristics to predict health status and/or readiness status of the subject using supervised and unsupervised machine learning modules;
and transmitting said predication of health status and/or readiness status to a secondary source;
wherein said extracted specified sensor features are segmented into different windows of interest and sent to a rhythm discovery component that applies periodic functions on each windowed stream of said extracted specified sensor feature to detect their periodicity;
said detected periods are then used to model rhythmic function that represents the time series data stream for said extracted specified sensor feature,
wherein said model rhythmic function incudes parameters; wherein: a) said parameters of said model rhythmic function are aggregated and further processed to characterize the stability or variation in rhythms;
and b) said parameters of said model rhythmic function are used as features in said machine learning module for said prediction of health status and/or readiness status or the subject;
and further comprising identifying rhythmicity in said time series data stream for detecting and observing cyclic behavior.
The limitations as shown in underline above, given the broadest reasonable interpretation, cover the abstract ideas of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teachings, and following rules or instructions- in this case, receiving data, modeling biobehavioral rhythms, determining rhythmicity characteristics of cyclical behavior, measuring stability, and predicting health status or readiness of the subject), e.g., see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 3-7, 11-17, 19, 22-26, 30-36, 38, 41-45, 49-55, and 57 include other limitations directed toward the abstract idea. For example, Claims 3, 22, and 41 recite the sensor data are behavioral signals or biosignals, Claims 4, 23, and 42 recite the types of signals encompassed by the behavioral signals, Claims 5, 24, and 43 recite the types of signals encompassed by the biosignals, Claims 6, 25, and 44 recite what features are included in the health status, Claims 7, 26, and 45 recite the modeling applies to specific durations, Claims 11, 30, and 49 recite identifying rhythmicity in the time series data stream by applying autocorrelation or periodogram process, Claims 12, 31, and 50 recite the autocorrelation process includes an autocorrelation function equation, Claims 13, 32, and 51 recite the calculation for the periodogram process, Claims 14, 33, and 52 recite modeling rhythmic behavior of a time series using a periodic function, Claims 15, 34, and 53 recite extracting rhythm parameters from the modeling and what the parameters include, Claims 16, 35, and 54 recite modeling rhythmic behavior using Cosinor and what the cosine function equation includes, Claims 17, 36, and 55 recite using rhythm features of windows of interest for a specific population, Claims 19, 38, and 57 recite measuring stability using an autocorrelation and Cosinor function process. The series of steps as described in Claims 12-16, 31-35, and 50-54 recite a different abstract idea of mathematical concepts because they merely recite mathematical equations. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in the dependent clams not addressed above are part of the abstract idea and will be further addressed below. Hence, dependent Claims 3-7, 11-17, 19, 22-26, 30-36, 38, 41-45, 49-55, and 57 are nonetheless directed fundamentally the same abstract idea as independent Claims 1, 20, and 39 of certain methods of organizing human activity and Claims 12-16, 31-35, and 50-54 additionally recite the abstract idea of mathematical concepts.
Step 2A Analysis – Prong Two:
Claims 1, 20, and 39 are not integrated into practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the mobile device, wearable device, and machine learning module of Claim 1, the computer processor, memory, mobile device, wearable device, and machine learning module of Claim 20, and the non-transitory computer-readable storage medium, mobile device, wearable device, and machine learning module of Claim 39) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that the processor is configured to execute the instructions to: receive sensor data collected from a mobile device and/or wearable device (p. 8, lines 27-28). The biobehavioral rhythm models provide a series of characteristic features which are further used for measuring stability in biobehavioral rhythms and to predict different outcomes such as health status through a machine learning component (p. 6, lines 25-28). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1, 20, and 39 are directed to an abstract idea without practical application.
Dependent Claims 2, 4, 17, 18, 21, 23, 36, 37, 40, 42, 55, and 56 recite additional elements. Claims 2, 21, and 40 recite new additional elements of a local memory, remote memory, and display/graphical user interface. Claims 4, 23, and 42 recite new additional elements of Bluetooth, wifi, GPS, and logs of phone usage and communication. Claims 17, 36, and 55 recite machine learning methods. Claims 18, 37, and 56 recite new additional elements of regression, classification, or clustering process (machine learning methods). However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the mobile device, wearable device, and machine learning module of Claim 1, the computer processor, memory, mobile device, wearable device, and machine learning module of Claim 20, and the non-transitory computer-readable storage medium, mobile device, wearable device, and machine learning module of Claim 39 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1, 20, and 39 are not patent eligible.
Dependent Claims 3, 5-7, 11-16, 19, 22, 24-27, 30-35, 38, 41, 43-45, 49-54, and 57 do not recite any additional elements and only narrow the abstract idea. Claims 3, 22, and 41 narrow the abstract idea by specifying the sensor data is behavioral signals or biosignals, Claims 5, 24, and 43 narrow the abstract idea by specifying the types of signals encompassed by the biosignals, Claims 6, 25, and 44 narrow the abstract idea by specifying what features are included in the health status, Claims 7, 26, and 45 narrow the abstract idea by specifying the modeling applies to specific durations, Claims 11, 30, and 49 narrow the abstract idea by specifying identifying rhythmicity in the time series data stream by applying autocorrelation or periodogram process, Claims 12, 31, and 50 narrow the abstract idea by specifying the autocorrelation process includes an autocorrelation function equation, Claims 13, 32, and 51 narrow the abstract idea by specifying the calculation for the periodogram process, Claims 14, 33, and 52 narrow the abstract idea by specifying modeling rhythmic behavior of a time series using a periodic function, Claims 15, 34, and 53 narrow the abstract idea by specifying extracting rhythm parameters from the modeling and what the parameters include, Claims 16, 35, and 54 narrow the abstract idea by specifying modeling rhythmic behavior using Cosinor and what the cosine function equation includes, Claims 19, 38, and 57 narrow the abstract idea by specifying measuring stability using an autocorrelation and Cosinor function process.
Dependent Claims 2, 4, 17-18, 21, 23, 36-37, 40, 42, and 55-56 recite new additional elements. Claims 2, 21, and 40 recite new additional elements of a local memory, remote memory, and display/graphical user interface. Claims 4, 23, and 42 recite new additional elements of Bluetooth, wifi, GPS, and logs of phone usage and communication. Claims 17, 36, and 55 recite a new additional element of machine learning methods. Claims 18, 37, and 56 recite new additional elements of regression, classification, or clustering process (machine learning methods). Hence, Claims 1-7, 11-26, 30-45, and 49-57 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-7, 11-26, 30-45, and 49-57 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
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.
Claims 1-7, 11, 19, 20-26, 30, 38-45, 49, and 57 are rejected under 35 USC § 103 as being unpatentable over Zhang et al. (US 20100280564 A1) in view of Doryab et al. (Doryab et al. 2019. Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 1, Article 8 (March 2019)) and Li et al. (US 20210365831 A1).
Regarding Claim 1, Zhang discloses the following:
A computer-implemented method for modeling biobehavioral rhythms of a subject, said method comprising: (Zhang discloses a method comprises sensing or receiving at an implantable device, information about at least one physiological process having a chronobiological rhythm [0018]. Figs. 7A-B display graphs modeling the biobehavioral rhythms.)
receiving sensor data collected from a mobile device and/or wearable device; (Zhang discloses the first and second information sensor modules include one of more physiologic process sensors, such as a temperature sensor 260, a blood pressure sensor 258, a respiratory rate/respiratory rate variability sensor 262, …an activity sensor 270, a heart rate/heart rate variability sensor 266, a posture sensor 268, or an accelerometer or microphone 267. In one example, each information sensor module 226, 228 also includes one or more interface circuits that receive one or more control signals and preprocesses the sensor signal(s) received [0065].)
modeling biobehavioral rhythms for each of said extracted specified sensor features to provide modeled biobehavioral rhythm data of the subject; (Zhang discloses data analysis and comparison of sensed or received circadian rhythms may involve both graphical and numerical procedures, and may further be characterized by one or more of a mean/median level, an amplitude, a phase, a period, a wave form, or robustness, for example [0080].)
determining rhythmicity characteristics of cyclical behavior of said modeled biobehavioral rhythm data of the subject; (Zhang discloses since the sleep circadian rhythm of the unhealthy subject 702 is lost or changed relative to the healthy subject's baseline circadian rhythm 700, a prediction of impending disease, such as heart failure, may have been in order for the unhealthy subject as soon as such loss (marked by irregularity) could be made with a reasonable degree of certainty [0110]. See also Fig. 7A where the unhealthy patient displays a different cyclic behavior than that of the healthy patient.)
…determine the deviation of the subject's rhythmicity characteristics from normal rhythmicity characteristics to predict health status and/or readiness status of the subject… (Zhang discloses comparing the chronobiological rhythm of the at least one physiological process to one or more chronobiological rhythm prediction criteria; and at least one of predicting, detecting, or identifying an occurrence of disease using the comparison [0018]. The occurrence of a disease is interpreted as the health status of a patient.)
and transmitting said predication of health status and/or readiness status to a secondary source. (Zhang discloses the programmable controller 224 may include a rhythm collection module 276 that receives from the physiological information collection device 104 information about the at least one physiological process having a circadian rhythm whose presence, absence, or baseline change is associated with a disease state [0068].)
Zhang does not disclose the following limitations met by Doryab:
extracting specified sensor features from said received sensor data; (Doryab teaches we have developed a generic and flexible Feature Extraction Component (FEC) to extract as many features as possible from passive data streams (p. 5, ¶ 0003). We further extract rhythm features following the list in [16] (see Tables 1 and 2).)
measuring stability of said determined rhythmicity characteristics of the subject across different time windows and/or across different populations… (Doryab teaches we use passively collected biobehavioral data from consumer wearable devices to detect instability in biobehavioral rhythms before surgery, in hospital, and after discharge. We observe significant differences in rhythms within- and between-patients across those three stages (p. 2, ¶ 0003). Further, Table 2. shows the definition of interdaily and intradaily stabilities and how they are measured. We are curious to understand 1) how different biobehavioral rhythms of each patient are in the three stages of treatment, 2) how different the rhythms of the readmitted group is from the not-readmitted group, and 3) what rhythm parameters are significantly different in each stage and between the two populations (p. 10, ¶ 0003).)
…using…machine learning modules; (Doryab teaches we also use rhythm metrics extracted from sensor data in a machine learning pipeline to predict readmission risk in those patients (p. 2, ¶ 0003).)
wherein said extracted specified sensor features are segmented into different windows of interest and sent to a rhythm discovery component that applies periodic functions on each windowed stream of said extracted specified sensor feature to detect their periodicity; (Doryab teaches FEC computes features from timestamped streams of data in specified time windows ranging from 1 minute to several months. From the data streams, FEC extracts a set of common statistical features such as min, median, mean, max, and standard deviation, as well as more complex behavioral features such as circadian movement and travel distance. For this analysis, we extracted features on an hourly basis to capture more variations (p. 5, ¶ 0003). The periodicity is then detected from the extracted sensor data (see p. 17, ¶ 0002).)
said detected periods are then used to model rhythmic function that represents the time series data stream for said extracted specified sensor feature, (Doryab teaches the modeling of data from pancreatic surgery patients showed we can detect and observe periodicity in patients’ time series sensor data. Our study and analysis demonstrated the feasibility of using passively collected activity and heart rate data from consumer devices to model biobehavioral rhythms (p. 17, ¶ 0002).)
wherein said model rhythmic function includes parameters; (Doryab teaches we use cosinor for our analysis as it provides the means to estimate and quantify parameters of a rhythm with an assumed period and use those parameters as features in our machine learning analysis for readmission prediction (p. 6, ¶ 0003). Table 2 displays the rhythm parameters extracted from the sensors and used to model the rhythmicity.)
wherein: a) said parameters of said model rhythmic function are aggregated and further processed to characterize the stability or variation in rhythms; (Doryab teaches repeated peaks above the confidence interval indicate strong periodicity in the data and rapid decay in the amplitude of peaks shows variation between cycles in data (p. 7, ¶ 0003).)
and b) said parameters of said model rhythmic function are used as features in said machine learning module for said predication of health status and/or readiness status of the subject. (Doryab teaches we use features obtained from the rhythm models in a machine learning analysis to predict readmission within 90 days of discharge and demonstrate significant differences in those features between readmitted and non-readmitted patients as well as superiority of our rhythms-based model over traditional clinical approaches (p. 2, ¶ 0004). Table 2 shows circadian variance as a rhythmic parameter used to predict the health and readmission status of a patient.)
and further comprising identifying rhythmicity in said time series data stream for detecting and observing cyclic behavior. (Doryab teaches we use the data to 1) detect cycles and their periods in our study population and 2) model the cyclic biobehavioral rhythms from passive data and use these features to predict readmission, a clinically significant outcome (p. 4, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the measurement of the stability and the use of a machine learning model as taught by Doryab. This modification would create a system and methods capable of more accurately identifying risk in patients (see Doryab, p. 2, ¶ 0003).
Zhang and Doryab are silent regarding the machine learning being both supervised and unsupervised, which is met by Li:
using supervised and unsupervised machine learning modules… (Li teaches method and computer readable storage medium includes a combining output of supervised and unsupervised machine learning models to portray an accurate prediction of an outcome for a claim [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the use of both supervised and unsupervised machine learning methods as taught by Li. This modification would create a system and methods capable of providing a more accurate, holistic prediction (see Li, ¶ 0002).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Zhang further discloses:
A system…comprising: a computer processor; and a memory (Zhang discloses FIG. 2 illustrates just one conceptualization of various modules, circuits, and interfaces of system 100, which are implemented either in hardware or as one or more sequences of steps carried out on a microprocessor or other controller [0058]. The programmable controller 224 includes … control circuitry, a RAM or ROM memory 274, logic and timing circuitry 277 to keep track of the timing of sensing or receiving circadian rhythm representative signals [0068].)
Regarding Claim 39, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Zhang further discloses:
A computer program product, comprising a non-transitory computer-readable storage medium… (Zhang discloses circadian rhythm representative signals associated with the at least one physiological process may be output to a programmable controller 224 for performing the prediction, monitoring, or treatment of the occurrence of impending heart failure or other disease state [0059].
Regarding Claim 2, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang further discloses:
wherein said secondary source includes one or more of anyone of the following: local memory; remote memory; or display or graphical user interface. (Zhang discloses the programmable controller 224 includes various functional modules, circuits, and detectors, one conceptualization of which is illustrated in FIG. 2. Among other things, the programmable controller 224 may include control circuitry, a RAM or ROM memory 274, logic and timing circuitry 277 to keep track of the timing of sensing or receiving circadian rhythm representative signals [0068].)
Regarding Claims 21 and 40, these claims recite limitations that are substantially similar to those recited in Claim 2 above; thus, the same rejection applies.
Regarding Claim 3, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang further discloses:
wherein said received sensor data comprises one or more of the following: behavioral signals or biosignals. (Zhang discloses signals representative of such circadian rhythm(s) and may further classify such rhythm(s) as being associated with one or more of body temperature (core or peripheral), heart rate, heart rate variability, respiration rate, respiration rate variability, minute ventilation, activity, blood pressure, posture, tidal volume, sleep quality or duration, thoracic impedance, or heart sounds [0068]. These are interpretted as biosignals.)
Regarding Claims 22 and 41, these claims recite limitations that are substantially similar to those recited in Claim 3 above; thus, the same rejection applies.
Regarding Claim 4, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 3 above. Zhang does not disclose the following limitations met by Doryab:
wherein said behavioral signals comprises one or more of the following: movement, audio, bluetooth, wifi, GPS, or logs of phone usage and communication. (Doryab teaches From the data streams, FEC extracts a set of common statistical features such as min, median, mean, max, and standard deviation, as well as more complex behavioral features such as circadian movement and travel distance (p. 5, ¶ 0003).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the measurement of the signals such as a subject’s movement as taught by Doryab. This modification would create a system and methods capable of more accurately identifying risk in patients by utilizing complex features from the subject data (see Doryab, p. 5, ¶ 0003).
Regarding Claims 23 and 42, these claims recite limitations that are substantially similar to those recited in Claim 4 above; thus, the same rejection applies.
Regarding Claim 5, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 3 above. Zhang further discloses:
wherein said biosignals comprises one or more of the following: heart rate, skin temperature, or galvanic skin response. (Zhang discloses signals representative of such circadian rhythm(s) and may further classify such rhythm(s) as being associated with one or more of body temperature (core or peripheral), heart rate,… [0068].)
Regarding Claims 24 and 43, these claims recite limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang further discloses:
wherein health status includes one or more of the following: loneliness, depression, cancer, diabetes, or productivity. (Zhang discloses the health state is determined by sensing or receiving information about at least one physiological process having a circadian rhythm whose presence, absence, or baseline change is associated with impending disease, and comparing such rhythm to baseline circadian rhythm prediction criteria. A breakdown in chronobiological rhythm may occur during general sickness (e.g., a flu or cold), neurological, mental or pulmonary disease, a viral or bacterial infection, other cardiovascular diseases (e.g., diabetes) or even cancer [0121].)
Regarding Claims 25 and 44, these claims recite limitations that are substantially similar to those recited in Claim 6 above; thus, the same rejection applies.
Regarding Claim 7, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang further discloses:
wherein said modeling of biobehavioral rhythms for each of said extracted specified sensor features applies to specified durations or periods. (Zhang discloses the impending disease state prediction module is adapted to predict the occurrence of impending disease during a specified prediction time period [0007].)
Regarding Claims 26 and 45, these claims recite limitations that are substantially similar to those recited in Claim 7 above; thus, the same rejection applies.
Regarding Claim 11, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang does not disclose the following limitations met by Doryab:
wherein said identification rhythmicity in said time series data stream is accomplished by applying an autocorrelation process or a periodogram process. (Doryab teaches our first question is whether we can detect rhythmicity in the passively collected biobehavioral data. Different methods have been used for rhythmicity detection such as ANOVA, Fourier analysis, cosinor, periodograms, autocorrelation, and cross-correlation (p. 6, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate identifying rhythmicity using a periodogram process as taught by Doryab. This modification would create a system and methods capable of identifying existing periods in the data that were not evident before (see Doryab, p. 7, ¶ 0004).
Regarding Claims 30 and 49, these claims recite limitations that are substantially similar to those recited in Claim 11 above; thus, the same rejection applies.
Regarding Claim 19, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang does not disclose the following limitations met by Doryab:
wherein said measuring of stability is provided using an autocorrelation process and a Cosinor function process. (Doryab teaches we use passively collected biobehavioral data from consumer wearable devices to detect instability in biobehavioral rhythms before surgery, in hospital, and after discharge (p. 2, ¶ 0003). We use autocorrelation, periodogram, and cosinor to model patient rhythms in three main time segments. All three methods provide visual interpretation of rhythms in the data. In addition to periodic representation of data, cosinor outputs rhythmic parameters such as MESOR, phase, and amplitude (as described in the Methods section) for a given period (e.g., 24 hours) (p. 9, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate measuring the stability using autocorrelation and cosinor function processes as taught by Doryab. This modification would create a system and methods which can identify risk in patients based on their rhythm’s stability (see Doryab, p. 2, ¶ 0003).
Regarding Claims 38 and 57, these claims recite limitations that are substantially similar to those recited in Claim 19 above; thus, the same rejection applies.
Claims 12, 17-18, 31, 36-37, 50, and 55-56 are rejected under 35 USC 103 as being unpatentable over Zhang, Doryab, and Li in view of Mormont et al. (Mormont et al. Marked 24-h Rest/Activity Rhythms Are Associated with Better Quality of Life, Better Response, and Longer Survival in Patients with Metastatic Colorectal Cancer and Good Performance Status1. Clin Cancer Res 1 August 2000).
Regarding Claim 12, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 11 above. Zhang and Doryab do not teach the following limitations met by Mormont:
The method of claim 11, wherein said autocorrelation process includes an autocorrelation function (ACF) between two values yt, yt-k in a time series yt that is defined as…
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where k is the time gap and is called the lag. (Mormont teaches Fig. 1c displays an autocorrelation function showing the autocorrelation coefficient (Y axis) calculated as a function of successive time lags (Fig 1.). For the autocorrelation, if Xi is the measurement at time i, the correlation coefficient rk, between Xi and Xi1k is computed for lags k, with k 5 1–4320 min (72 h); the coefficient at 24:00 h (r24) can, in theory, range between 21 and 1. If there is a circadian variation, the correlation coefficient will increase around 24-h lags, and a more pronounced circadian rhythm will result in a higher coefficient at 24:00 h (Fig. 1a, p/ 2, ¶ 0005).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the autocorrelation process including an autocorrelation function with a time gap and a lag as taught by Mormont. This modification would create a system and methods which is capable of accurately modeling the biological rhythms (see Mormont, p. 1, ¶ 0002).
Regarding Claims 31 and 50, these claims recite limitations that are substantially similar to those recited in Claim 12 above; thus, the same rejection applies.
Regarding Claim 17, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 1 above. Zhang does not disclose the following limitations met by Doryab:
and for a population of D data samples (Doryab teaches in a sample of 49 pancreatic surgery patients, we demonstrated that we can capture patients’ biobehavioral rhythms across the perioperative period using passively sensed heart rate and activity data from commercial devices (p. 17, ¶ 0001).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the use of a population of data samples as taught by Doryab. This modification would create a system and methods capable of modeling each patient as compared to the population to identify similarities and differences (see Doryab, p. 7, ¶ 0002).
Zhang, Doryab, and Li do not teach the following limitation met by Mormont:
using rhythm features of k consecutive time windows of said windows of interest (Mormont teaches patients were asked to wear the actigraph for at least three consecutive 24-h spans, which is the recommended duration for evaluating activity circadian rhythm (29) (p. 2, ¶ 0004).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the autocorrelation process including an autocorrelation function with a time gap and a lag as taught by Mormont. This modification would create a system and methods which is capable of accurately modeling the biological rhythms (see Mormont, p. 1, ¶ 0002).
Zhang, Doryab, and Mormont are silent regarding the machine learning being both supervised and unsupervised, which is met by Li:
incorporates said supervised and unsupervised machine learning methods. (Li teaches method and computer readable storage medium includes a combining output of supervised and unsupervised machine learning models to portray an accurate prediction of an outcome for a claim [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the use of both supervised and unsupervised machine learning methods as taught by Li. This modification would create a system and methods capable of providing a more accurate, holistic prediction (see Li, ¶ 0002).
Regarding Claims 36 and 55, these claims recite limitations that are substantially similar to those recited in Claim 17 above; thus, the same rejection applies.
Regarding Claim 18, Zhang, Doryab, Mormont and Li teach the limitations as seen in the rejection of Claim 17 above. Zhang does not disclose the following limitations met by Doryab:
includes one of the following: regression, classification, or clustering process. (Doryab teaches we evaluate the performance of Random Forest, Logistic Regression, Support Vector Machine, Bayesian Network, and Boosted Logistic Regression. Boosted Logistic Regression with Linear Regression or Decision Stump as base learners provides the best performance according to our evaluation criteria explained below (p. 8, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the machine learning model including regression as taught by Doryab. This modification would create a system and methods capable of modeling both equidistant and non-equidistant data (see Doryab, p. 6, ¶ 0004).
Zhang, Doryab, and Mormont are silent regarding the machine learning being both supervised and unsupervised, which is met by Li:
wherein said supervised and unsupervised machine learning methods (Li teaches method and computer readable storage medium includes a combining output of supervised and unsupervised machine learning models to portray an accurate prediction of an outcome for a claim [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the use of both supervised and unsupervised machine learning methods as taught by Li. This modification would create a system and methods capable of providing a more accurate, holistic prediction (see Li, ¶ 0002).
Regarding Claims 37 and 56, these claims recite limitations that are substantially similar to those recited in Claim 18 above; thus, the same rejection applies.
Claims 13, 32, and 51 are rejected under 35 USC 103 as being unpatentable over Zhang, Doryab, and Li in view of Refinetti et al. (Refinetti et al. (2007). Procedures for numerical analysis of circadian rhythms. Biological rhythm research, 38(4), 275-325. (Year: 2007)).
Regarding Claim 13, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 11 above. Zhang, Doryab, and Li do not teach the following limitations met by Refinetti:
wherein said periodogram process provides a measure of strength and regularity of the underlying rhythm through estimation of the spectral density of a signal, wherein for a time series yt, t = 1, 2, ...,, the spectral energy Pk of frequency k can be calculated as:
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(Refinetti teaches in Fourier analysis, the spectral energy (Rj2) of each frequency j/N…can be calculated as: (equations 1-3, shown below)…By computing Rj2 for all j from 1 to (N-1)/2, a periodogram can be constructed. The periodogram shows the spectral energy associated with each frequency (p. 6, ¶ 0002).)
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It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the calculation of the spectral energy as taught by Mormont. This modification would create a system and methods which is capable of accurately modeling the time series of the rhythm and assessing its parameters (see Refinetti, p. 25, ¶ 0002).
Regarding Claims 32 and 51, these claims recite limitations that are substantially similar to those recited in Claim 13 above; thus, the same rejection applies.
Claims 14-16, 33-35, and 52-54 are rejected under 35 USC 103 as being unpatentable over Zhang, Doryab, and Li in view of Cornelissen et al. (Cornelissen et al. Cosinor-based rhythmometry. Theor Biol Med Model 11, 16 (2014)).
Regarding Claim 14, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 11 above. Zhang, Doryab, and Li do not teach the following limitations met by Cornelissen:
further comprising modeling rhythmic behavior of said time series data, which is accomplished through a periodic function. (Cornelissen teaches the problem of stationarity arises primarily in long time series, when the MESOR, amplitude, acrophase and/or period can change as a function of time (p. 10, ¶ 0004). Although periodic regression presents its own limitations, being sensitive to outliers and not having any constraint to conserve the variance in the data, it possesses two important features: first, when data are equidistant, results at Fourier frequencies are identical to those of the discrete Fourier transform [38]; and second, it advantageously uses prior information (p. 2, ¶ 0002).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the modeling of rhythmic behavior of time series data as taught by Cornelissen. This modification would create a system and methods which is capable of detection and parameter estimation, and the early diagnosis of altered rhythm characteristics indicative of a heightened risk (see Cornelissen, p. 2, ¶ 0001).
Regarding Claims 33 and 52, these claims recite limitations that are substantially similar to those recited in Claim 14 above; thus, the same rejection applies.
Regarding Claim 15, Zhang, Doryab, and Li teach the limitations as seen in the rejection of Claim 14 above. Zhang, Doryab, and Li do not teach the following limitations met by Cornelissen:
further comprising extracting rhythm parameters from the said modeling rhythmic behavior, wherein said rhythm parameters include one or more of the following: fundamental period, MESOR, magnitude, acrophase (PHI), orthophase, bathyphase, P- value (P), percent rhythm (PR), Integrated p-value (IP), integrated percent rhythm (IPR), or longest cycle of the model (LCM). (Cornelissen teaches the MESOR, amplitude, acrophase and/or period can change as a function of time (p. 10, ¶ 0004). The nonlinear procedure can yield an acceptable estimate of the fundamental period on the basis of very short records not even covering a full cycle [84] (p. 16, ¶ 0004).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the modeling of rhythmic behavior of time series data as taught by Cornelissen. This modification would create a system and methods which is capable of detection and parameter estimation, and the early diagnosis of altered rhythm characteristics indicative of a heightened risk (see Cornelissen, p. 2, ¶ 0001).
Regarding Claims 34 and 53, these claims recite limitations that are substantially similar to those recited in Claim 15 above; thus, the same rejection applies.
Regarding Claim 16, Zhang, Doryab, Li, and Cornelissen teach the limitations as seen in the rejection of Claim 14 above. Zhang, Doryab, and Li do not teach the following limitations met by Cornelissen:
wherein said modeling rhythmic behavior comprises modeling rhythms with known periods using Cosinor, wherein a cosine function to model said time series includes:…
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where yi is the observed value at time ti ; M presents the MESOR; ti is the sampling time; C is the set of all periodic components; Ac,Wc,pc respectively presents the amplitude, frequency, and acrophase of each periodic components; and ei is the error term. (Cornelissen teaches the single-component cosinor is easily extended to a multiple-component model: (p. 11, equation 17).
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)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for receiving sensor data and modeling biobehavioral rhythms and identifying a deviation in a subject’s rhythmicity from normal to determine the health status of said subject as disclosed by Zhang to incorporate the modeling of rhythmic behavior of time series data using cosinor as taught by Cornelissen. This modification would create a system and methods which is capable of detection and parameter estimation, and the early diagnosis of altered rhythm characteristics indicative of a heightened risk (see Cornelissen, p. 2, ¶ 0001).
Regarding Claims 35 and 54, these claims recite limitations that are substantially similar to those recited in Claim 16 above; thus, the same rejection applies.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-7, 11-26, 30-45, and 49-57, Applicant’s arguments have been considered, but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues under Step 1 of the analysis, the Office Action did not reference the specification, so it did not determine the “character of the claim” as required by the first step in determining whether the claims are “directed to” a judicial exception (see Applicant’s Remarks, p. 3).
Regarding (a), Examiner respectfully disagrees. First, Examiner notes that Step 1 of the Alice test is determining if the claims are within one of the four statutory categories (MPEP 2106.03), so Examiner presumes the Applicant is referring to Step 2A: Prong One. There is nothing in the MPEP that requires the Examiner to provide support for the existence of the judicial exception from the specification. The claims recite a judicial exception because of the identified abstract ideas in the 101 analysis above.
Applicant argues the office actions states the limitations in claim 1 “cover the abstract ideas”, and therefore did not use the correct terminology of “directed to” or “recited” (p. 3).
Regarding (b), Examiner respectfully disagrees. There is nothing in the MPEP that requires the use of the stated terminology. The claimed invention does not merely involve an abstract idea, it is directed to nothing more than abstract ideas with the inclusion of additional elements that do not provide a practical application or “significantly more” (see the rejection above).
Applicant argues the Office Action states that Claims 12-16, 31-35, and 50-54 recite the abstract idea of mathematical concepts. However, using a mathematical equation “to complete the claimed method and system does not doom the claims to abstraction”. The claims do not purport to recite the underlying mathematical concepts, but rather leverage these techniques within the context of the claimed system to achieve a specific end. The general concept and use of the underlying mathematical operations remain outside the scope of the claims (p. 3-4).
Regarding (c), Examiner respectfully disagrees. Examiner notes that the primary abstract idea grouping of the claimed invention is certain methods of organizing human activity, and the dependent claims 12-16, 31-35, and 50-54 were given the additional grouping of mathematical concepts because they are claims that recite a numerical formula/equation (see MPEP 2106.04(a)(2)(B)).
Applicant argues the claims improve technology and therefore amount to more than mere instructions to apply an exception using generic computer parts because they improve technology rather than merely incorporate generic computer parts. A claim that improves technology can integrate an abstract idea into practical application, because "[s]ince the claim improves technology, the claim imposes meaningful limits on any recited judicial exception." [USPTO October 2019 Update: Subject Matter Eligibility, p. 11]. But, in order to demonstrate that a disclosure improves technology, it "must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." [MPEP 2106.05(a)]. The claims therefore disclose more than mere instructions, but a solution to existing problems in modeling biobehavioral rhythms of a subject (p. 4).
Regarding (d), Examiner respectfully disagrees. Here, the claims do not show how the biobehavioral modeling of the claims improve the functioning of the device. On review of the Specification, the present invention provides means for processing different data sources, extracting information from them and discovering and modeling rhythms for each biobehavioral signal (Detailed Description in ¶ 0016). It is unclear to Examiner how the specific additional elements, alone or in combination, provide a solution to the stated problem in context of the abstract idea of modeling biobehavioral rhythms. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art (MPEP § 2106.05(a)). Additionally, an important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03 (MPEP § 2106.05(a)(II)). The instant claims seem analogous to MPEP § 2106.05(a)(II) examples that the courts have indicated may not be sufficient to show an improvement to technology, example iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
Applicant argues as discussed in paragraph [0022] of the specification, the present invention addresses and overcomes these challenges. "An aspect of an embodiment of the present invention provides, among other things, a computational framework to address the aforementioned challenges through a series of data processing and modeling steps." [0022].
As written in the specification, "We introduce the first computational framework for modeling biobehavioral rhythms to the mobile and ubiquitous computing community that provides the ability to: (a) flexibly process massive sensor data in different time granularity thus providing the ability to model and observe short- and long-term rhythmic behavior. (b) identify variation and stability in individual and groups of time series data. (c) help observe the impact of cyclic biobehavioral parameters in revealing and predicting different outcomes (e.g., health)." [0023]-[0026]. As written in the specification, "An aspect of an embodiment of the present invention overcomes, among other things, several challenges in processing and modeling biobehavioral time series data from mobile and wearable devices that motivated the development of our novel computational framework. These challenges include, but are not limited thereto, 1) automated handling and processing of massive multimodal sensor data, 2) granular and fine-grained exploration of all signals to extract knowledge about biobehavioral cycles, and 3) computational steps for modeling, discovering, and quantification of common patterns." [0175] (p. 5-6).
Regarding (e), Examiner respectfully disagrees. As stated in the response to (d) above, an important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome, McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03 (MPEP § 2106.05(a)(II)). The claims are recited at a high level of generality such that they amount to no more than iii. Gathering and analyzing information using conventional techniques and displaying the result (see MPEP § 2106.05(a)(II)).
Efficiency is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2). Examiner notes that “automated” (e.g., automated handling) merely indicates the implementation of a computer. If the limitation of “automated handling” does simply mean a computer carries out the steps, then that means that the computer is being utilized in an “apply it” manner. Further, Examiner notes that the “supervised and unsupervised machine learning modules” are only used in step (b) where the parameters of the model rhythmic function are fed into the ML module to predict health status and/or readiness status, and the claims state that the rest of the steps are carried out by systems computer processor (Claim 20).
Regarding the improvement to the challenge of “granular and fine-grained exploration of all signals to extract knowledge about biobehavioral cycles”, the claims only recite “extracting specified sensor features from said received sensor data” with no details or particularity in how this is done. The claims don’t make it clear how or why it is “granular and fine-grained”. Further, the focus of the invention is not data extraction, it is for modeling biobehavioral rhythms, so extracting of the data to do so is merely data gathering, an insignificant extra-solution activity.
Regarding the improvement to the challenge of “computational steps for modeling, discovering, and quantification of common patterns", the computational steps are a part of the abstract idea identified above, and these steps are carried out by machine learning modules in an “apply it” manner.
Applicant argues as written in the specification, "We observed that the combination of multiple sensor features contributed to the improvement of prediction results." [0175] (p. 6).
Regarding (f), Examiner notes that the “sensor features” are not positively recited and therefore cannot provide an inventive concept. Further, making predictions of health status and/or readiness status is part of the abstract idea (see the 101 rejection above), so any improvement in that area would at most be an improvement in the abstract idea, and would not serve to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Applicant argues similarly to Example 42, as discussed in Applicant's specification, claims 1, 20, and 39 recite a specific improvement over the prior art by allowing for: "1) automated handling and processing of massive multimodal sensor data, 2) granular and fine-grained exploration of all signals to extract knowledge about biobehavioral cycles, and 3) computational steps for modeling, discovering, and quantification of common patterns." [0174] (p. 6-7).
Regarding (g), Examiner respectfully disagrees with the comparisons drawn and finds Example 42 distinguishable from the instant claims. Example 42's abridged background provides the technical problem of latency and incompatibility between remote devices stating "medical providers must continually monitor a patient’s medical records for updated information, which is often-times incomplete since records in separate locations are not timely or readily-shared or cannot be consolidated due to format inconsistencies as well as physicians who are unaware that other physicians are also seeing the patient for varying reasons" wherein the technical solution is reflected in the claim language. The instant application, however, presents a non-technical problem – modeling biobehavioral rhythms of a subject. The solution to the problem is rooted in an improvement to the abstract idea itself and not a technical failure of a computer system. The additional elements can best be characterized as tools to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples., v.”).
Applicant argues under Step 2B, the claims amount to significantly more than a judicial exception and they are also unconventional, which means they amount to an inventive concept. The invention employs "passive sensing of physiological and behavioral signals from mobile and wearable devices" to "study human rhythms more broadly and holistically in the wild through collection of biobehavioral data from different sources." [0005]. This is unlike other studies, which "have often been limited to controlled settings to observe certain behaviors and effects." [0005]. As discussed in the specification, recent tools "use visualization to analyze human behavior." [0010]. But "...these tools primarily enable understanding of rhythms through visualization whereas in the present inventor's framework, an aspect of an embodiment of the present invention provides means for processing different data sources, extracting information from them and discovering and modeling rhythms for each biobehavioral signal with different periods other than 24 hours." [0010] (p. 7).
Regarding (h), Examiner respectfully disagrees. The “passive sensing” argued above for providing an inventive concept is not the invention provided in the claims. Further, passive sensing is not claimed at all. The instant claims merely receive data from a generic sensor from a mobile/wearable device. The problem being solved in the claims that is actively recited is the modeling of the received data. Further, passive sensing is not an unconventional solution in the technological field, as the secondary reference, Doryab, teaches the use of passive sensing in biobehavioral modeling (see Doryab, p. 1, ¶ 0001).
Regarding rejections under 35 USC 103 to Claims 1-7, 11-26, 30-45, and 49-57, Applicant’s arguments have been considered, and are persuasive in light of the amendments. Therefore the rejection has been withdrawn. However, upon further consideration, a new rejection has been made, rejecting the claims over Zhang in view of Doryab and Li.
Applicant argues in claim 1, the measurement of stability of the determined rhythmicity is done for the purpose of determining the deviation of the subject’s rhythmicity characteristics from normal rhythmicity characteristics, which is not taught by Doryab (p. 9).
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). 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, as stated in the rejection of Claim 1 above, Zhang discloses the determining a deviation of the subject’s rhythmicity characteristics (see Zhang, ¶ 0018, 0068). It would have been obvious to incorporate Doryab’s use of a measurement of stability into Zhang’s disclosure of determining a deviation of the subject’s rhythmicity. These references are considered to be analogous art because they both relate to the field of modeling biobehavioral rhythms and this modification would serve to improve readmission prediction accuracy (see Doryab, p. 2, ¶ 0002).
Applicant argues in claim 10, the claims state “identifying rhythmicity in said time series data stream for detecting and observing cyclic behavior”, which is not taught by Doryab because Doryab does not teach identifying rhythmicity before detecting and observing cyclic behavior (p. 10).
Examiner respectfully disagrees. As stated in Doryab, “Our first question is whether we can detect rhythmicity in the passively collected biobehavioral data (p. 6, ¶ 0003).” Further, Doryab states “in Figure 2a, the longest spectral line is the 24-hour period indicating the dominant rhythm followed by approximately 6- and 3-hour periods. This method, in addition to identifying circadian disruptions, helps detect other existing cycles in the data that were not evident before (p. 7, ¶ 0004).” This shows that the first step of the analysis is detecting the rhythms and the second step is using the rhythms to model the cyclic behavior.
Applicant argues the use of a “supervised and unsupervised” machine learning model cannot be taught by Li because Li is not analogous art. Li is relevant to “the world of claims” and end users such as “ an insurance company” (Li [0002], 0022]).
Examiner respectfully disagrees. It has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the technical field being taught by Li is the use of machine learning models. The Technical Field summary of Li’s specification states “The disclosure generally relates to the field of machine learning, and more particularly relates to integrating output from supervised and unsupervised machine learning models [0001].”
Li further states in paragraph [0049] “The systems and methods disclosed herein lean on insurance examples for convenience, and may apply to more broadly to other fields. For example, where a dataset needs to be segmented, such as, segmenting financial data by fraud likelihood, or predicting people groups ' income levels based on other demographics data. For each of those different purposes, the integrated technique of supervised and unsupervised learnings disclosed herein may be applied to optimize the data segmentation by using supervised learning to achieve optimized predictions.” The use of insurance claims is merely an example of an implementation of this high level machine learning model, but is not the invention.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681