Prosecution Insights
Last updated: April 19, 2026
Application No. 18/126,100

SYSTEM AND METHOD FOR CHARACTERIZING, DETECTING AND MONITORING SLEEP DISTURBANCES AND INSOMNIA SYMPTOMS

Final Rejection §101§102
Filed
Mar 24, 2023
Examiner
CHRISTIANSON, SKYLAR LINDSEY
Art Unit
3792
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
Feel Therapeutics Inc.
OA Round
2 (Final)
60%
Grant Probability
Moderate
3-4
OA Rounds
3y 0m
To Grant
90%
With Interview

Examiner Intelligence

Grants 60% of resolved cases
60%
Career Allow Rate
85 granted / 141 resolved
-9.7% vs TC avg
Strong +30% interview lift
Without
With
+29.6%
Interview Lift
resolved cases with interview
Typical timeline
3y 0m
Avg Prosecution
53 currently pending
Career history
194
Total Applications
across all art units

Statute-Specific Performance

§101
8.5%
-31.5% vs TC avg
§103
46.0%
+6.0% vs TC avg
§102
15.3%
-24.7% vs TC avg
§112
23.5%
-16.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 141 resolved cases

Office Action

§101 §102
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 1. Applicant's arguments, regarding the U.S.C. 101 and 102(a)(2) rejections, filed 10/20/2025 have been fully considered but they are not persuasive. The Applicant argues that the claims as a whole should not be rejected under U.S.C. 101. The Examiner respectfully disagrees. The claims merely encompass data gathering and data analysis, all of which could be done mentally. For instance, looking at claim 1, a user can access the biological data via a screen or a print out, a user can then look at the biological data at particular times to determine an insomnia profile (consisting of mood, resilience, circadian rhythm, energy level, etc.), and then observe how particular treatment pathways are working to treat these health conditions/insomnia. This is typically how a doctor’s appointment would work. The use of a processer is well-understood, routine, conventional. The use of processors and machine learning algorithms in this manner are merely used to quickly execute what could be done mentally – Machine learning systems are designed to model human-like learning, problem-solving, and decision-making. Further, there is nothing in the claims that would inhibit a person from doing these steps, if given enough time. The rejection still stands. Regarding the U.S.C. 102(a)(2) rejection, the art of Lou teaches the amendments to the claims. For instance, Par. 010 and Par. 0051 teach using processors and cloud-based algorithms, i.e. machine learning, to analyze and make predictions about data provided from the user. The rejection still stands. 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. 2. Claims 1-2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claim 1 recite a method for generating treatment pathways to help with insomnia. The limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, compiling a treatment recommendation for a patient could simply be done by looking at the sensor health data on a printout or screen or could be accomplished mentally. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. This judicial exception is not integrated into a practical application. The components are recited at a high-level of generality. Further, the use of sensors in the claims, are merely insignificant extra-solution activity of data gathering. Accordingly, these additional elements do 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. The additional elements, such as the wearable device with sensors to gather health data, while being mere structures for data gathering are also well-understood, routine, conventional activity that is widely prevalent or common use in the relevant industry. The use of sensors to gather patient information are well known in the art as disclosed by the following references: US 20040122790 A1 and US 20210343384 A1. Well-understood, routine and conventional activity cannot be significantly more than the abstract idea itself. The claims are not patent eligible. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 3. Claim(s) 1-2 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lou (US 20220134052 A1). In regards to claim 1, Lou teaches a method comprising: during a first time period: Accessing by at least one processor of a computing system associated with a user, and via a communications interface, a first timeseries of biosignal data collected via a set of sensors integrated into a wearable device worn by a user during the first time period (Par. 010 teaches using a processor system and Par. 0042 teaches a user communication interface. Par. 0040 teaches a wearable device that comprises sensors for collecting data over time/in real-time, see also Par. 0095.); characterizing by the at least one processor, a set of health indicators exhibited by the user during the first time period based on the first timeseries of biosignal data; deriving, by the at least one processor, and by an application of a trained machine learning or artificial intelligence process configured to identify correlations between the first timeseries of biosignal data and the set of health indicators (Par. 0051 teaches using cloud based algorithms to analyze the data), a first insomnia profile representative of an (i) energy level, (ii) sleep quality, (iii) circadian rhythm, (iv) mental resilience, (v) mood, or (vi) any combination of (i)-(v) exhibited by the user during the first time period; and selecting by the at least one processor, a first treatment pathway for managing the set of health indicators for the user based on the first insomnia profile (Par. 0098 and Fig 15 teach gathering sensor data in real-time, which can include sleep quality, and determining if a user exhibits insomnia tendencies and then generating a treatment plan (in this case a light treatment plan) for treating this insomnia); and accessing, by the at least one processor, and via the communications interface, a second timeseries of biosignal data collected via the set of sensors integrated into the wearable device worn by the user during the second time period (Par. 0096 teaches that data can be gathered over particular time periods including real time or other times during the day/week); characterizing, by the at least one processor, the set of health indicators exhibited by the user during the second time period based on the second timeseries of biosignal data; deriving, by the at least one processor, a second insomnia profile representative of the set of health indicators exhibited by the user during the second time period; characterizing, by the at least one processor, a difference between the first insomnia profile and the second insomnia profile; characterizing, by the at least one processor, effectiveness of the first treatment pathway based on the difference; and in response to characterizing effectiveness of the first treatment pathway below a threshold effectiveness, selecting, by the at least one processor, a second treatment pathway in replacement of the first treatment pathway for managing the set of health indicators in replacement of the first treatment pathway (Par. 0107-0108 and Fig 15 teach that real-time sensor/health information can be obtained during and after the first treatment session and then this information can be used to update the treatment plan for a second session window to improve its efficacy) In regards to claim 2, Lou teaches the method of Claim 1 further comprising, during an initial time period preceding the first time period: accessing, by the at least one processor, and via the communications interface an initial timeseries of biosignal data collected via the set of sensors integrated into the wearable device worn by the user during the initial time period; accessing by the at least one processor a timeseries of indicator markers derived from a series of health evaluations executed for the user and representative of the set of health indicators for the user during the initial time period (Par. 0098 and 0107-0108 teach gathering sensor data from the wearable device and then making health evaluations based on this sensor data) labeling by the at least one processor, the initial timeseries of biosignal data according to the timeseries of indicator markers to generate a first indicator-labeled timeseries of biosignal data; and deriving, by the at least one processor, an insomnia model linking biosignal data to the set of health indicators for the user based on the first indicator-labeled timeseries of biosignal data (Par. 0066 teahces employing algorithms to take in the sensor data from the wearable device and then generate models and treatment plans from this data); wherein characterizing the set of health indicators exhibited by the user during the first time period based on the first timeseries of biosignal data comprises characterizing, by the at least one processor, the set of health indicators exhibited by the user during the first time period based on the first timeseries of biosignal data and the insomnia model; and wherein characterizing, by the at least one processor, the set of health indicators exhibited by the user during the second time period based on the second timeseries of biosignal data comprises characterizing by the at least one processor, the set of health indicators exhibited by the user during the second time period based on the second timeseries of biosignal data and the insomnia model (Par. 0066 teaches using algorithms to create and employ treatment models to treat insomnia [see Par. 0098] and then using real-time feedback from the sensor and the user to update the treatment plan for the second tome period). 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 SKYLAR LINDSEY CHRISTIANSON whose telephone number is (571)272-0533. The examiner can normally be reached Monday-Friday, 7:30-5:30 EST. 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, Niketa Patel can be reached at (571) 272-4156. 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. /S.L.C./Examiner, Art Unit 3792 /NIKETA PATEL/Supervisory Patent Examiner, Art Unit 3792
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Prosecution Timeline

Mar 24, 2023
Application Filed
Jun 12, 2025
Non-Final Rejection — §101, §102
Oct 20, 2025
Response Filed
Feb 04, 2026
Final Rejection — §101, §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
60%
Grant Probability
90%
With Interview (+29.6%)
3y 0m
Median Time to Grant
Moderate
PTA Risk
Based on 141 resolved cases by this examiner. Grant probability derived from career allow rate.

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