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 Amendment
In response to the amendment filed 6/22/2026; claims 1, 6 - 10 are pending; claims 2-5 have been cancelled.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6 - 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy et al. (US 2023/0008323 A1) in view of Burwinkel et al. (US 2020/0205746 A1) and Publicover et al. (US 2015/0338915 A1).
Re claims 1, 10:
Reddy teaches 1. A system for predicting the likelihood of falling or the degree of anesthesia recovery (Reddy, Abstract; [0119]), the system comprising:
at least one camera installed at a predetermined location in a hospital to capture an image (Reddy, [0091]; Abstract);
a motion detector configured to detect the motion of a patient in the image (Reddy, [0005], “monitoring movement of the patient using the parts identified for the patient”);
a face recognizer configured to recognize the face of the patient in the image to determine the identity of the patient, and recognize the expression of the patient (Reddy, [0005]; [0012], “the method further includes identifying facial parts of the patient using the data from the camera”; fig. 11, Left eye and right eye); and
a monitor configured to predict the likelihood of falling or the degree of anesthesia recovery by using an action of the patient detected by the motion detector, the identity of the patient determined by the face recognizer, and the expression (Reddy, [0069], “The present inventors have also recognized that the risk of patient departures further increases with the presence of various cognitive impairments. These cognitive impairments may be the result of a disease state, pre-operative medications, or post-operative care, for example. In addition to impairing cognition, the patient may also be less stable during these times, and/or have an increased state of agitation as a result of the impaired cognition, each of which may further increase the risks of falling”; [0119], “predict and/or prevent for preventing patient falls from a bed”)
wherein the monitor comprises:
an expression recognition module configured to recognize an expression correlating with a situation involving the likelihood of falling or with the degree of anesthesia recovery and the intensity of the expression (Reddy, [0012]; [0068]);
a gaze recognition module configured to distinguish the recognized gaze of the patient according to the eye movement, and to calculate a difference between the distinguished gaze of the patient and a normal gaze to detect an abnormal gaze movement (Reddy, [0097], “anatomical hotspots P are identified: … left eye 32L, right eye 32R”; [0108], “the agitation score is determined by deriving feature a vector of critical patient regions (e.g., the eyes, eye brows, or around the mouth)”); and
an action recognition module configured to detect a posture of the patient by detecting a patient area from the image captured by the camera and extracting a skeleton of the patient within the area (Reddy, fig. 13; Abstract; [0130]; [0156]).
Reddy teaches 10. A method for predicting the likelihood of falling or the degree of anesthesia recovery (Reddy, Abstract; [0119]), the method comprising:
capturing an image by at least one camera installed at a predetermined location in a hospital (Reddy, [0091]; Abstract);
detecting the motion of a patient in the image (Reddy, [0005], “monitoring movement of the patient using the parts identified for the patient”);
recognizing the face of the patient in the image to determine the identity of the patient, and recognizing the expression of the patient (Reddy, [0005]; [0012], “the method further includes identifying facial parts of the patient using the data from the camera”; fig. 11, Left eye and right eye); and
predicting the likelihood of falling or the degree of anesthesia recovery by using the action of the patient detected in the detecting of the motion of the patient, the identity of the patient determined in the recognizing of the expression and gaze of the patient, and the expression (Reddy, [0069], “The present inventors have also recognized that the risk of patient departures further increases with the presence of various cognitive impairments. These cognitive impairments may be the result of a disease state, pre-operative medications, or post-operative care, for example. In addition to impairing cognition, the patient may also be less stable during these times, and/or have an increased state of agitation as a result of the impaired cognition, each of which may further increase the risks of falling”; [0119], “predict and/or prevent for preventing patient falls from a bed”),
wherein the recognizing of the expression and gaze of the patient comprises:
recognizing an expression correlating with a situation involving the likelihood of falling or with the degree of anesthesia recovery and the intensity of the expression (Reddy, [0012]; [0068]);
distinguishing the recognized gaze of the patient according to the eye movement, and calculating a difference between the distinguished gaze of the patient and a normal gaze to detect an abnormal gaze movement (Reddy, [0097], “anatomical hotspots P are identified: … left eye 32L, right eye 32R”; [0108], “the agitation score is determined by deriving feature a vector of critical patient regions (e.g., the eyes, eye brows, or around the mouth)”); and
detecting a posture of the patient by detecting a patient area from the image captured by the camera and extracting a skeleton of the patient within the area (Reddy, fig. 13; Abstract; [0130]; [0156]).
Reddy does not explicitly a face recognizer configured to recognize gaze of the patient; and a monitor configured to predict the likelihood of falling or the degree of anesthesia recovery by using gaze of the patient. Burwinkel et al. (US 2020/0205746 A1) teaches a predictive fall event management system and a method. Burwinkel teaches face recognizer configured to recognize gaze of the patient; and a monitor configured to predict the likelihood of falling or the degree of anesthesia recovery by using gaze of the patient (Burwinkel, [0098], “Vestibular-ocular reflexes can also be measured as the eye will attempt to stabilize the individual's visual field with each step”; [0108], “sensor 104 of the predictive fall event management system 100 can include one or more eye movement sensors. In one or more embodiments, the system 100 can also include one or more sensors 104 that can measure head movement of the wearer. Data from such head movement sensors 104 can be utilized to correlate with eye movement sensor data to determine the risk of a fall. Any suitable fall prediction system or device can be utilized to measure eye movement of a wearer”). Therefore, in view of Burwinkel, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system and method described in Reddy, by predicting fall based on eye movement as taught by Burwinkel, Since Burwinkel suggests evaluate eye movement of the wearer to determine a fall risk value based on monitoring of the wearer. For example, the system can detect eye movements and compare such eye movements to a baseline to determine whether a vestibular event is occurring that can increase the risk of fall (Burwinkel, [0110]).
Reddy does not explicitly disclose a gaze recognition module configured to distinguish the recognized gaze of the patient as each of saccades, vergence movements, smooth pursuit movements, and vestibulo-ocular movements according to the eye movement, and to calculate a difference between the distinguished gaze of the patient and a normal gaze to detect an abnormal gaze movement
Publicover et al. (US 2015/0338915 A1) teaches systems and methods are provided for discerning the intent of a device wearer primarily based on movements of the eyes. Publicover teaches a gaze recognition module configured to distinguish the recognized gaze of the patient as each of saccades, vergence movements, smooth pursuit movements, and vestibulo-ocular movements according to the eye movement, and to calculate a difference between the distinguished gaze of the patient and a normal gaze to detect an abnormal gaze movement (Publicover, [0054], “FIG. 3 is a flowchart illustrating the classification of saccades, micro-saccades, smooth pursuit eye movements, and fixations”; [0089]; [0106]; [0115]; [0118]; [0087], “sets of eye movements that consider the physiology and anatomy of the eye as well as the cognitive properties of the visual cortex”; [0567]; from [0279], “Discerning a User's State of Mind”; [0280], “ different emotions can be discerned based on eye movements and changes in the geometry of different components of the eye”). Therefore, in view of Publicover, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system and method described in Reddy, by providing the gaze recognition including various gaze movements as taught by Publicover, in order to assess the cognitive (Publicover, [0093]), motor function (Pulblicover, [0009]), attention / focus level (Publicover, [0015]), state of mind (Publicover, [0600]) by monitoring the gaze movements of the user.
Re claims 6 – 7:
6. The system of claim 1, wherein the action recognition module recognizes the meaning of each action stage through a detected change in posture of the patient (Reddy, [0130]).
7. The system of claim 6, wherein the action recognition module calculates the difference between the detected posture of the patient and a normal posture according to the meaning of each action stage to detect an abnormal action (Reddy, [0156]).
Re claim 8:
8. The system of claim 1, wherein the monitor predicts the likelihood of falling or the degree of anesthesia recovery by using an artificial neural network trained with features of gaze directions and skeletal movements of the patient, extracted from images indicating the likelihood of falling or images indicating the occurrence of falling (Reddy, [0019]; [0085]; [0138]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Reddy, Burwinkel and Publicover as applied to claim 1 above, and further in view of Annegarn et al. (US 2016/0220153 A1).
Re claim 9:
Reddy doesn’t explicitly 9. The system of claim 1, wherein the monitor classifies whether or not the determined identity of the patient belongs to either a high-risk group for falling or a high-risk group for anesthesia recovery, and makes a prediction with increased sensitivity if the patient belongs to the high-risk group. Annegarn teaches the missing features (Annegarn, [0012], “the increase in sensitivity is temporary and only lasts while the higher risk of falling is present or detected”; [0099] – [0103]). Therefore, in view of Annegarn, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system described in Reddy, by increasing sensitivity as taught by Annegarn, by providing robust classification methods or processing algorithms for detecting falls accurately when the risk is higher (Annegarn, [0006]).
Response to Arguments
Applicant's arguments filed 6/22/2026 have been fully considered but they are not persuasive.
Applicant argues:
Reddy and Burwinkel fail to describe at least "a gaze recognition module". Amended claim 1 recites: "a gaze recognition module configured to distinguish the recognized gaze of the patient as each of saccades, vergence movements, smooth pursuit movements, and vestibulo-ocular movements according to the eye movement, and to calculate a difference between the distinguished gaze of the patient and a normal gaze to detect an abnormal gaze movement." The Office acknowledges that Reddy fails to describe a gaze recognition module but asserts that Burwinkel remedies this deficiency. (OA, p. 4-5.) However, Burwinkel merely describes that vestibulo-ocular reflexes may be measured because the eye attempts to stabilize the individual's visual field with each step during walking. (Burwinkel, para. [0098].)
The newly cited reference Publicover et al. (US 2015/0338915 A1) teaches the limitation: distinguishing the recognized gaze of the patient as each of saccades, vergence movements, smooth pursuit movements, and vestibulo-ocular movements according to the eye movement to assess the state of mind of the patient.
Applicant argues:
However, there is no teaching or suggestion in the cited references that describes this coordinated three-module architecture in combination with the recited strengthened gaze-recognition limitation requiring per-type classification and per-type abnormality detection.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The examiner submits that Reddy teaches the three module architectures: an expression recognition module, a gaze recognition module and an action recognition module. Reddy does not teach each of saccades, vergence movements, smooth pursuit movements, and vestibulo-ocular movements according to the eye movement. The newly cited Publicover et al. (US 2015/0338915 A1) remedies this deficiency.
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.
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/JACK YIP/Primary Examiner, Art Unit 3715
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