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 Nonfinal Office Action is in response to the RCE filed 03/09/2026, wherein claims 21, 23, 24, 31, 32, 35, 38 and 39 are amended. Claims 21-40 are currently pending and considered herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/09/2026 has been entered.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 21-24, 27, 28, 31, 32, 34, 35, 38 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2012/0154582 A1 to Johnson et al., hereinafter “Johnson,” in view of U.S. 2024/0282303 A1 to Shenkan, hereinafter “Shenkan.”
Regarding claim 21, Johnson discloses A system comprising: a non-transitory memory storing a dynamic database of healthcare protocol data (See Johnson at least at Paras. [0005], [0039] (“Examples set forth herein focus on the monitoring of tasks, which are specified a-priory, that comprise a protocol using information from databases, sensor systems, motion and optical shape recognition in the healthcare clinical services delivery venue. This system captures inputs or state changes from the art of computer vision object, movement and persons identification, telemetry signal processing, sensor systems and electronic records in order to uniquely reason or identify the state of activities being monitored relative to prescribed protocols. “), [0105]-[0109], [0119]-[0120], [0128], [0132]-[0135], [0140]-[0144]; Claim 7; Figs. 5, 7, 16-21) the healthcare protocol data defining an expected optical sensor reading for a healthcare interaction (See id. at least at Paras. [0037]-[0039] (“An optically based sensor system is deployed that determines the location and trajectory of people as well as the presence of certain objects and settings or status of configured apparatus, which singularly or in conjunction with other analog and digital data, informs a reasoning engine that calculates the state of the monitored people and objects.”), and a corresponding expected audio sensor reading for a response to the healthcare interaction by a healthcare agent (See id. at least at Paras. [0042]-[0045] (“a combination of multi-modal sensors and computer vision is used to identify motion, objects, and people alone or in combination with data and traditional sensor inputs and analyze activity analysis (i.e., through reasoning over 1 or more sensor signals). A reasoning engine is then used to determine the state of the systems and tasks related to the protocols. Such combination may include the use of telemetry, computer vision, RFID, audio analysis, commercial sensor technology, and the like, as described in detail below.”), [0062]; Claims 16, 22, 23; Figs. 1-3); a comparator circuit communicatively coupled to an optical sensor, an audio sensor, and the non-transitory memory, wherein the comparator circuit determines a deviation of a second reading of the optical sensor from the expected optical sensor reading (See id. at least at Paras. [0037]-[0039] (“An optically based sensor system is deployed that determines the location and trajectory of people as well as the presence of certain objects and settings or status of configured apparatus, which singularly or in conjunction with other analog and digital data, informs a reasoning engine that calculates the state of the monitored people and objects […] object, movement and persons identification, telemetry signal processing, sensor systems and electronic records in order to uniquely reason or identify the state of activities being monitored relative to prescribed protocols.), [0042]-[0045] (“a combination of multi-modal sensors and computer vision is used to identify motion, objects, and people alone or in combination with data and traditional sensor inputs and analyze activity analysis (i.e., through reasoning over 1 or more sensor signals). A reasoning engine is then used to determine the state of the systems and tasks related to the protocols. Such combination may include the use of telemetry, computer vision, RFID, audio analysis, commercial sensor technology, and the like, as described in detail below.”), [0061] (multi-modal sensing including audio analysis), [0062], [0065] (microphone), [0091]; Figs. 1-5 (sensor-based systems A…N), 6 (sound capture device 685), 7); and an alert circuit to transmit a real-time alert signal to a hardware speaker to provide an updated instruction to the healthcare agent based on the modified healthcare protocol data (See id. at least at Paras. [0045] (“embodiments present fact-based feedback to humans for contextual feedback for the purposes of professional development and behavioral change influencing (e.g., real-time audible notification, post-event aggregation, analysis, and reporting, etc.).”), [0061], [0081]-[0084] (audible feedback and instruction based on protocol), [0090]-[0097]; Figs. 1-7).
Johnson may not specifically describe but Shenkan teaches at least one machine-learning model, wherein the at least one machine-learning model comprises a dynamic configuration module (See Shenkan at least at Abstract; Paras. [0003]-[0012] (“The audio content can include read content. The operations can further include comparing the received audio content to expected audio content via a machine learning algorithm. The operations can include determining, based on an output of the machine learning algorithm, that a portion of the received audio content deviates from a portion of the expected audio content by greater than a threshold value.”), [0093] (real-time configuration), [0120]-[0128]; Claims 1-4; Figs. 1-8); and a configuration circuit that, responsive to the deviation exceeding a threshold, causes the dynamic configuration module to update the non-transitory memory in real time by modifying the dynamic database of healthcare protocol data based on a reading of the audio sensor (See Shenkan at least at Abstract; Paras. [0003]-[0012] (“The audio content can include read content. The operations can further include comparing the received audio content to expected audio content via a machine learning algorithm. The operations can include determining, based on an output of the machine learning algorithm, that a portion of the received audio content deviates from a portion of the expected audio content by greater than a threshold value.”), [0061], [0073]-[0075] (adjusting content and resources updated), [0093], [0120]-[0128] (expected audio and comparisons as healthcare protocol data are the expected audio sensor readings); Claims 1-4; Figs. 1-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Johnson and incorporate the teachings of Shenkan and provide machine learning and modifying databases based on audio sensor readings and being different from expected (protocol). Shenkan is directed to an automated customization engine for receiving audio content and storing and generating feedback. Incorporating the automated feedback and audio sensor and machine learning of Shenkan with the system and method for protocol adherence as in Johnson would thereby improve the applicability, efficacy, and accuracy of the claimed system and method for healthcare compliance.
Regarding claim 22, Johnson as modified by Shenkan discloses the limitations of claim 21, and Johnson further discloses wherein the alert circuit transmits one or more reports associated with the deviation to a healthcare dashboard (See id. at least at Abstract; Paras. [0036]-[0039] “The system provides real-time alerts to medical personnel in the actual processes of care, thereby reducing the number of negative patient events and ultimately improving staff behavior with respect to protocol adherence […] An optically based sensor system is deployed that determines the location and trajectory of people as well as the presence of certain objects and settings or status of configured apparatus, which singularly or in conjunction with other analog and digital data, informs a reasoning engine that calculates the state of the monitored people and objects. Deviations from desired states are determined and appropriate reporting and alarming is made. The sensor and reasoning systems are hosted in a message brokered computing environment that may persist in one or more computers and locations.”); Figs. 1-7).
Regarding claims 23, Johnson as modified by Shenkan discloses the limitations of claim 21, and Johnson further discloses wherein the comparator circuit is configured to access the at least one machine-learning model stored in one or more databases (See id. at least at Paras. [0133]-[0153] (machine learning)).
Regarding claim 24, Johnson as modified by Shenkan discloses the limitations of claim 23, and Johnson further discloses wherein the comparator circuit is configured to load at least one machine-learning model, provide the loaded machine-learning model at least one of the optical sensor data or the audio sensor data, and execute the loaded machine-learning model (See id. at least at Paras. [0151]-[0154] (“From a computer vision and machine learning perspective, detecting the occurrences of such actions normally requires the discovery of a suit of related motion features in a fine granularity from imageries, and performs a classification/regression analysis on the features using an action specifically learned statistical model, trained over a representative set of sample data […] consider the motion features extracted in the space-time volumes of captured video imageries and potential other assisted sensors. The space-time motion features that can be used to characterize the training samples include, as examples, motion features from spatial-temporal filtering, motion features from sparse interest operators, occupancy measures from 3D image and depth sensors, positioning measures from bed load sensors.”).
Regarding claim 27, Johnson as modified by Shenkan discloses the limitations of claim 21, and Johnson further discloses wherein the non-transitory memory is to store one or more event streams (See id. at least at Paras. [0148]-[0152] (“The trajectory filtering/smoothing of each tracker is also performed on the ground plane in such a centralized fashion, enabling the system to provide continuous meta-data stream in the form of person locations as a function of time. According to one embodiment, up to five persons may be simultaneously tracked in a room.”).
Regarding claims 28 and 35, claims 28 and 35 recite substantially the same limitations as included in independent claim 21. Thus, claim 28 is rejected under the same grounds of rejection and for the same reasoning applied to claim 21, above.
Regarding claims 31 and 38, claims 31 and 38 recite substantially the same limitations as included in claim 23. Thus, claims 31 and 38 are rejected under the same grounds of rejection and for the same reasoning applied to claim 23, above.
Regarding claims 32 and 39, claims 32 and 39 recite substantially the same limitations as included in claim 24. Thus, claims 32 and 39 are rejected under the same grounds of rejection and for the same reasoning applied to claim 24, above.
Regarding claim 34, claim 34 recites substantially the same limitations as included in claim 27. Thus, claim 34 is rejected under the same grounds of rejection and for the same reasoning applied to claim 27, above.
Claims 25, 33 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Shenkan and further in view of U.S. 2008/0027499 A1 to Srivathsa et al., hereinafter “Srivathsa.”
Regarding claim 25, Johnson as modified by Shenkan discloses the limitations of claim 21, may not specifically describe but Srivathsa teaches wherein the alert circuit is to, based on the deviation exceeding the threshold, send an SMS alert to the caregiver (See Srivathsa at least at Paras. [0002], [0019], [0024], [0033]-[0035]; Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Johnson and Shenkan to incorporate the teachings of Srivathsa and provide a message after a certain threshold. Srivathsa is directed to an integrated health care communication and monitoring system. Incorporating the health communication systems as in Srivathsa with the automated feedback and audio sensor and machine learning of Shenkan and the system and method for protocol adherence as in Johnson would thereby improve the applicability, efficacy, and accuracy of the claimed system and method for healthcare compliance.
Regarding claims 33 and 40, claims 33 and 40 recite substantially the same limitations as included in claim 25. Thus, claims 33 and 40 are rejected under the same grounds of rejection and for the same reasoning applied to claim 25, above.
Claims 26, 30 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Shenkan and further in view of U.S. 2020/0237291 A1 to Sundaram et al., hereinafter “Sundaram.”
Regarding claim 26, Johnson as modified by Shenkan discloses the limitations of claim 21, may not specifically describe but Sundaram teaches wherein the comparator circuit is to perform a gait analysis, predict a fall based on the analysis, and analyze the predicted fall (See Sundaram at least at Paras. [0003], [0006]-[0007], [0084], [0162], [0195], [0199]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Johnson and Shenkan to incorporate the teachings of Sundaram and provide particular fall detection techniques. Sundaram is directed to adaptive health monitoring for patients. Incorporating the adaptive health monitoring as in Sundaram with the automated feedback and audio sensor and machine learning of Shenkan and the system and method for protocol adherence as in Johnson would thereby improve the applicability, efficacy, and accuracy of the claimed system and method for healthcare compliance.
Regarding claims 30 and 37, claims 30 and 37 recite substantially the same limitations as included in claim 26. Thus, claims 30 and 37 are rejected under the same grounds of rejection and for the same reasoning applied to claim 26, above.
Response to Arguments
Applicant’s Amendment and Remarks filed March 9, 2026 have been fully considered, but they are not entirely persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The arguments at Pages 9-10 are not persuasive. The arguments are moot at least in light of new reference Shenkan, discussed above.
As such, it is submitted that the cited combination of prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for monitoring clinical administration and automated assessments, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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