Patent Application 18638079 - ARTIFICIAL INTELLIGENCE AI AND MACHINE LEARNING

Title: ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) BASED GRAPHICAL USER INTERFACE (GUI) SYSTEM FOR EARLY DETECTION OF DEPRESSION SYMPTOMS USING FACIAL EXPRESSION RECOGNITION AND ELECTROENCEPHALOGRAM

Application Information

Invention Title: ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) BASED GRAPHICAL USER INTERFACE (GUI) SYSTEM FOR EARLY DETECTION OF DEPRESSION SYMPTOMS USING FACIAL EXPRESSION RECOGNITION AND ELECTROENCEPHALOGRAM
Application Number: 18638079
Submission Date: 2025-05-16T00:00:00.000Z
Effective Filing Date: 2024-04-17T00:00:00.000Z
Filing Date: 2024-04-17T00:00:00.000Z
National Class: 600
National Sub-Class: 408000
Examiner Employee Number: 86979
Art Unit: 3797
Tech Center: 3700

Rejection Summary

102 Rejections: 0
103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

US 0346552🔗

Office Action Text

DETAILED ACTION

Notice of 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 Objections
Claims 1, 9, and 15 are objected to because of the following informalities: “a emotion” should be an emotion”. Appropriate correction is required.

Claim Rejections - 35 USC § 112
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.

Claim(s) 1-20 is/are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 1, 9, and 15, the past tense term “requested” renders the claim unclear. It is unclear as to why in response to the identification, data is considered “requested”.

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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.

Claim(s) 1-4, 6-10, 12-14, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “EEG Signal and video Analysis Based Depression Indication” by Y. Katyal et al. IEEE Int. Conf. Adv. Comm. Ctrl. Comput. Tech. 2014 (Katyal), in view of, “Multimodel Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression” by Y. Sun et al. Brain Sciences. 10, 85, Feb 6 2020 (Sun).
Regarding claims 1, 9, and 15, Katyal discloses a method, system, and operations comprising: receiving image data of a biological subject under examination for depression; analyzing the image data, to identify an emotional state conveyed by a face of the biological subject; identifying the emotional state as an emotion indicative of depression in the biological subject, acquire electroencephalogram data for the biological subject using a machine learning model (“neural network relevant to the application being considered (i.e. classification of EEG data) will be employed for designing classifiers”); receiving, from the biological subject, the electroencephalogram data; analyzing the electroencephalogram data to identify depression in the biological subject; and providing the emotional state identified to a graphical user interface (Abstract: “combining both EEG signal analysis and facial emotion recognition through video analysis”; “Using both EEG and Facial results gives a higher percentage output than the individual results”). Katyal does not explicitly disclose that a facial emotional state is identified by way of a first machine learning model, that the electroencephalogram data is acquired in response to an indication of depression, nor that a severity level of depression is identified. However, Sun teaches using a trained Hidden Markov Model to identify a facial-based emotional state of a person (“It utilizes Regional Hidden Markov Model (RHMM) as its classifier to train the states of three face regions…”), measuring a severity level of depression (Fig. 7(e)), and that joint utilization of facial expression and wearable neuroimaging, which includes EEG, improved overall emotional analysis (Abstract, 5. Conclusions). Additionally, Katyal teaches that, “visual attributes work as a cross check in the study”, which suggests to one with ordinary skill that a sequential detection and verification of depression indicators may contribute to a more accurate detection of depression. Katyal also discloses a group consisting of “not depressed” (Fig. 16), which is a measure of severity. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the identification and classification of mental state as taught by Sun to the depression indication of Katyal, as to provide robust bimodal detection of depression.
Regarding claims 2 and 3, while Katyal does not explicitly disclose the use of a random forest model, a random forest model of machine learning is well-known and conventionally used in the art and thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply a random forest model of machine learning, so as to provide a robust and appropriate machine learning algorithm.
Regarding claims 4, 10, and 16, Katyal discloses that the emotional state is sadness (Abstract: “depression”).
Regarding claims 6, 12, and 18, Katyal discloses that the image data are provided as one of still images or video images (“detect the face in the given image or video sequence”).
Regarding claims 7, 13, and 19, Katyal does not explicitly disclose generating a treatment plan for the biological subject based on the severity level identified by the second machine learning model, however Katyal states, “Results shown can detect even a small change in the patient’s mind and his level of depression which can help in early medication and saving the patient, if at all, from having suicidal tendencies”, which suggests to one with ordinary skill that it would have been obvious to generate a treatment plan based on a measured severity level.
Regarding claims 8, 14, and 20, Katyal discloses a group consisting of “not depressed” (Fig. 16).

Claim(s) 5, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over “EEG Signal and video Analysis Based Depression Indication” by Y. Katyal et al. IEEE Int. Conf. Adv. Comm. Ctrl. Comput. Tech. 2014 (Katyal), in view of, “Multimodel Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression” by Y. Sun et al. Brain Sciences. 10, 85, Feb 6 2020 (Sun), as applied to claims 1, 9, and 15 above, in view of Khalid (US 2024/0346552).
Regarding claims 5, 11, and 17, neither Katyal nor Sun explicitly disclose that the emotional state identified by the first machine learning model and the severity level identified by the second machine learning model are provided to a graphical user interface in real-time. However, Khalid teaches performing expression analysis in real-time ([0040]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the real-time feed as taught by Khalid to the system of Katyal and Sun, as to provide robust real-time feedback.

Conclusion

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/JASON M IP/Primary Examiner, Art Unit 3793