Office Action Predictor
Last updated: April 15, 2026
Application No. 18/244,064

DEVICE FOR PROVIDING INFORMATION FOR IMPROVING SLEEP QUALITY AND METHOD THEREOF

Non-Final OA §102§103
Filed
Sep 08, 2023
Examiner
NGUYEN, AN T
Art Unit
2686
Tech Center
2600 — Communications
Assignee
Samsung Electronics Co., LTD.
OA Round
1 (Non-Final)
68%
Grant Probability
Favorable
1-2
OA Rounds
2y 10m
To Grant
95%
With Interview

Examiner Intelligence

Grants 68% — above average
68%
Career Allow Rate
403 granted / 596 resolved
+5.6% vs TC avg
Strong +27% interview lift
Without
With
+27.1%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
11 currently pending
Career history
607
Total Applications
across all art units

Statute-Specific Performance

§101
3.2%
-36.8% vs TC avg
§103
50.5%
+10.5% vs TC avg
§102
22.4%
-17.6% vs TC avg
§112
16.9%
-23.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 596 resolved cases

Office Action

§102 §103
Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claim Rejections - 35 USC § 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)(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. Claim(s) 1-4, 7-12 and 15 is/are rejected under 35 U.S.C. 102 a(2) as being anticipated by NARAYANAN et al. (US 2023/0148956). Claim 1 and 9, NARAYANAN teaches an electronic device (Fig. 2, electronic device 3) comprising: a display (par. 127: GUI generated on device 3); at least one processor operatively connected to the display (par. 6: processor); and a memory operatively connected to the at least one processor (par. 6: readable medium), wherein the memory is configured to store instructions causing the at least one processor to, when executed: identify sleep time information of a user (par. 26: the wearable device can be configured to automatically determine when the user has begun attempting to go to sleep to start monitoring of the sleep of the user); acquire sleep evaluation information (par. 174: The user's input responsive to each of these questions can be stored in connection with date and time information so that the user's input can be stored and used in the subsequent analysis for the generation of different charts, graphs, other types of displays and for analyzing different sleep patterns and behaviors that can be linked or associated with those sleep patterns) and action information corresponding to the sleep time information (par. 194: The data that is collected can also be utilized at a user-specific level to evaluate the type of sleep a user is experiencing and to predict different user activities that can trigger good sleep or trigger insomnia or other sleep related problems); analyze a pattern of the action information, based on the sleep evaluation information (par. 280: The collection and storage of the user's objective and subjective data related to the user's sleep, sleep quality, and/or sleep duration can be stored for analysis and evaluation); generate sleep guide information for the user, based on a result of the analyzing (par. 165: The subjective and objective data can be analyzed and the patient can subsequently be provided output to suggest a behavioral change to try and improve the patient's sleep quality, sleep duration, or other health condition); and output the sleep guide information generated to the display (par. 280: Such displays can include graphs, bar charts, or other graphical displays or video displays illustrating such graphs or charts in a video form. The stored data can also be stored for evaluation of different user specific patterns of behavior for use in suggesting changes to that behavior for attempting to improve the user's sleep quality and/or duration). Claim 2 and 10, NARAYANAN teaches wherein the instructions cause the at least one processor to identify the sleep time information, based on at least one of a user input, a screen on or screen off record detected by the display, or biometric data of the user acquired from an external electronic device (par. 116: FIG. 17 is a flow chart illustrating an exemplary embodiment of process for prompting a user to provide subjective and objective data via visual, video and/or audio output at different times to solicit input via at least one input device for collecting subjective user data including sleep start time, met claimed user input). Claim 3 and 11, NARAYANAN teaches wherein the instructions cause the at least one processor to acquire the sleep evaluation information, based on at least one item among a sleep score calculated based on a sleep state recorded during sleep, sleep satisfaction input by the user, sleep efficiency indicating a ratio of an actual sleep time to a total sleep time, or a sleep rating determined based on the sleep time information (par. 116: FIG. 17 is a flow chart illustrating an exemplary embodiment of process for prompting a user to provide subjective and objective data via visual, video and/or audio output at different times to solicit input via at least one input device for collecting subjective user data including user sleep experienced, met claimed sleep satisfaction input by the user). Claim 4 and 12, NARAYANAN teaches wherein the instructions cause the at least one processor to acquire the action information, based on at least one of a usage record of an application executed by the electronic device, a motion-related record detected using at least one sensor, and context data estimated based on a network connection state of the electronic device, and the action information is converted into a first format comprising at least one of an action type, an action name, a time difference between an action occurrence time point and a sleep time, an action start time, or an action end time and stored in the memory (par. 272: the activity score can be determined based on accelerometer data indicating movement of the user. The movement data obtained from the accelerometer as well as other sensors can be used to determine the type of activity the user is engaged in (e.g. walking, running, etc.) and can also determine a number of steps the user has taken in a particular time period (e.g. a day, a week, a month, a year, etc.). For example, the accelerometer data can be evaluated to determine the speed of each stride a user has taken and the length of the stride. When the detected stride length and speed of each stride is at or above a running threshold, the user can be determined to be running. When the detected stride length and speed of each stride is below the running threshold and at or above a walking threshold, the user can be determined to be walking, met claimed motion related). Claim 7 and 15, NARAYANAN teaches wherein the instructions cause the at least one processor to generate the sleep guide information, based on a user profile comprising at least one of gender, age, or residence of the user (par. 186: the microphone can be activated for recording sound emitted by the user or in the environment around the user to evaluate environmental noise or other sound issues that may affect the user's sleep, met claimed residence of the user). Claim 8, NARAYANAN teaches wherein the instructions cause the at least one processor to output the sleep guide information generated to the display at a predetermined time point (par. 186: The stored data can also be linked or associated with other stored data, such as a duration at which the user was detected as being asleep or attempting to go to sleep via other sensor data. This collected and stored data can then be processed to evaluate the quality of the user's sleep, whether the user had an insomnia event, or other issues. The wearable device 3, input/output device 13, docking station 5, or central server 7 can be configured to utilize the sensor data and perform this calculation. as well as provide data for the generation of a graph, chart, text, or other display of information via a GUI for providing information to a user about the user's detected sleep via a display 13a). Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over NARAYANAN et al. in view of PAVLOV et al. (US 2022/0005580). Claim 5 and 13, NARAYANAN does not teach wherein the instructions cause the at least one processor to classify the action information into a positive pattern or a negative pattern, based on the sleep evaluation information and store a classification result of the classifying in the memory or a database that can be accessed by the electronic device. In the field of endeavor, PAVLOV teaches a method for providing recommendations for maintaining a healthy lifestyle basing on daily activity. He goes on to teach when unhealthy lifestyle habits are detected, these habits are correlated to categories of unhealthy habits, such as: low physical activity 1097, emotional overeating 1098, insufficient sleep time, etc., at operation 109, under the control of a processing unit (e.g., a processor), the system may predefine the categories of unhealthy habits and store the categories of unhealthy habits in the storage module. The system may combine the categories of unhealthy habits, with which the detected habits that were not conducive to maintaining a healthy lifestyle were correlated, to form a personalized profile of unhealthy habits, which is used to further analysis and generation of an appropriate recommendation for a healthy lifestyle and a program regarding the user's nutrition and physical activity (par. 133-135). It would have been obvious to one of ordinary skill in the art at the time of filing to modify NARAYANAN’s system to classify the action information into a positive pattern or a negative pattern as taught by PAVLOV for the purpose of further analysis and generation of an appropriate recommendation for a healthy lifestyle and a program regarding the user's nutrition and physical activity, a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (PAVLOV par. 134). Allowable Subject Matter Claims 6 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to An T Nguyen whose telephone number is (571)270-5167. The examiner can normally be reached Monday - Friday 9-5 ET. 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, Brian Zimmerman can be reached at 571-272-3059. 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. /AN T NGUYEN/Primary Examiner, Art Unit 2686
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Prosecution Timeline

Sep 08, 2023
Application Filed
Dec 10, 2025
Non-Final Rejection — §102, §103
Jan 30, 2026
Interview Requested
Feb 09, 2026
Interview Requested
Feb 18, 2026
Examiner Interview Summary
Feb 18, 2026
Applicant Interview (Telephonic)
Mar 27, 2026
Response Filed

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

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Prosecution Projections

1-2
Expected OA Rounds
68%
Grant Probability
95%
With Interview (+27.1%)
2y 10m
Median Time to Grant
Low
PTA Risk
Based on 596 resolved cases by this examiner. Grant probability derived from career allow rate.

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