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 § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations "…each occurrence intensity…" and “…each action unit…” in line 6. There is insufficient antecedent basis for this limitation in the claim. There is no prior mention to either an occurrence intensity or action unit, let alone mention that there is a plurality of either. As such, it is unclear as to what these limitations refer to.
The limitations "…each occurrence intensity…" and “…each action unit…” will be interpreted as "…each of a plurality of occurrence intensities…" and “…each of a plurality action units…”
Claims 4, 5, and 7 recites the limitation "…second machine learning model…" in line 3 of claims 4 and 5 (respectively),as well as line 4 of claim 7. There is insufficient antecedent basis for this limitation in the claim. Each respective claim is dependent on claim 1, however, there is no prior mention of a first machine learning model. As such, it is unclear how this is a second machine learning model. As such, it is further unclear if this indicates that there is a first machine learning model (necessitated by this being the second).
Fig. 7 does show that there is a first machine learning algorithm that corresponds to a second machine learning model, but this is not reflected in the claims. For the purpose of interpreting these dependent claims it is assumed that Claim 1 is amended to require a first model performing the steps shown in Fig. 7 to maintain the secondary nature of the aforementioned machine learning model.
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.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter, for the reasons as follows:
In re to claim 1, the claim is directed to a Non-transitory computer-readable medium, which falls within one of the four statutory categories. Additionally, due to reciting similar limitations, claims 8 and 9 are rejected for the same reasons provided below (as the method performed by claim 1 and system used to execute claim 1, respectively).
Claim 1 recites: “A non-transitory computer-readable recording medium storing a symptom detection program for causing a computer to execute processing comprising: acquiring video data that includes a face of a patient who is executing a specific task;
detecting each occurrence intensity of each action unit included in the face of the patient, by analyzing the acquired video data; and
detecting a symptom related to a major neurocognitive disorder of the patient, based on a temporal change in the occurrence intensity of each of a plurality of the detected action units.”
The limitations of claim 1, as drafted, are considered to fall under the category of an abstract concept.
An individual may look at a video of a patient’s face. Based on the activity displayed in the video, the individual may decide that the patient is experiencing a particular neurological issue with respect to the expression of related symptoms.
Thus, the claim recites an abstract idea. Additionally, the judicial exception is not integrated into a practical application.
Further, regarding the similarly written claim 9, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “…a memory…” and “…a processor…”
The additional elements do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the additional elements do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in
MPEP 2106).
Furthermore, the additional elements do not add significantly more to the judicial exception. Memory may be implemented by a generic memory component that performs functions that are well-understood, routine and conventional. Memory is a computer element which performs generic computer data storage. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
A processor may be implemented by a generic computer that performs functions that are well- understood, routine and conventional. It is a computer element which performs generic computer functions/computations. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
Dependent claims
Claim 2 (dependent on claim 1) further correlates the relation of the symptom to the major neurocognitive disorder by stating it belongs to said disorder or to a cognitive impairment. This does not add significantly more than the abstract idea, nor does it integrate it into a practical application. As such, it is a part of the abstract idea.
Claims 3 and 6 (dependent on claim 1), disclose additional details regarding data that processing occurs, adding usage of a first machine learning model and further detailing that the specific task applies a load on cognitive function (each respectively). These additional details only indicate that the system merely applies a machine learning model as some intermediate step and further indicate a circumstantial aspect to the term “specific task”. They do not add significantly more than the abstract idea, nor integrate it into a practical application. As such, they are a part of the abstract idea.
Claims 4, 5, and 7 (dependent on claim 1) disclose additional limitations regarding the training of a second machine learning model. They Detail the features that are considered during training. They do not add significantly more than the abstract idea, nor integrate it into a practical application due to the features considered for training not being directed to a particular development. As such, they are a part of the abstract idea.
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.
Claims 1-4, 6, 8, and 9 are rejected under 35 U.S.C. 102 (a)(1)/(a)(2) as being anticipated by Glasner et al. (US publication 20200251190 A1; hereinafter “Glasner”).
In re to claim 1, Glasner teaches wherein: a non-transitory computer-readable recording medium storing a symptom detection program for causing a computer to execute processing comprising: acquiring video data (video; [0035] discloses that the recorded subject response data may be video data) that includes a face (Fig. 1 shows the system recording a subject (exemplified with a face) using an imaging device (as is detailed in [0072], which states that the capture device 108 may be a camera). Additionally, [0070] discloses that the system records data of facial expressions, performing measurements in facial expression and movement) of a patient (subject; Fig. 1 and [0034]-[0035] indicate the recording of a subject, understood as a patient) who is executing a specific task ([0034]-[0035] discloses that the subject has their response recorded in relation to a given task, like answering a question. It is understood that the task being responded to by the subject is the specific task);
detecting each occurrence intensity of each action unit included in the face of the patient, by analyzing the acquired video data ([0070] discloses that the system records data of facial expressions, performing measurements in facial expression and movement. It is understood that the measured expressivity and movement over a period of time is an occurrence intensity. Additionally, the action unit is understood as the determined facial expression); and
detecting a symptom related to a major neurocognitive disorder of the patient ([0078] discloses the determination of a disease severity level based on subject answers and detected biomarkers. It is understood that the detected biomarker response is a symptom related to a neurocognitive disorder. See also in [0078] that the disease severity measured based on the biomarker response is for schizophrenia. Thus, the biomarker response is related to a neurocognitive disorder), based on a temporal change in the occurrence intensity of each of a plurality of the detected action units ([0070] discloses that the system records data of facial expressions, performing measurements of said facial expressions and movements over time. Further, as this is a response over time, this is understood to be a temporal change. Additionally, this is done for a plurality of action units, as [0070] also indicates that the measured stimuli include a variety of requested expressions).
In re to claim 2 [dependent on claim 1], Glasner teaches wherein: the symptom related to the major neurocognitive disorder of the patient is one of a major neurocognitive disorder or a cognitive impairment ([0078] discloses the determination of a disease severity level based on subject answers and detected biomarkers. It is understood that the detected biomarker response is a symptom to a neurocognitive disorder. See also in [0078] that the disease severity measured based on the biomarker response is for schizophrenia. Thus, the biomarker response is a symptom (correspondent to the claims) of a neurocognitive disorder).
In re to claim 3 [dependent on claim 1], Glasner teaches wherein: detecting each occurrence intensity ([0070] discloses that the system records data of facial expressions, performing measurements in facial expression and movement) of each action unit included in the face of the patient ([0070] also indicates that the measured stimuli include a variety of requested expressions of the subject. It is further stated that the biomarkers measured are in relation to the expression of the various emotions (represented by facial expression, as per [0070])), by inputting the acquired video data ([0035] discloses that the recorded subject response data may be video data) into a first machine learning model ([0111] discloses the application of a machine learning model (understood as OpenFace’s facial analysis system in further combination with a machine learning method, as described in [0111])).
In re to claim 4 [dependent on claim 1], Glasner teaches wherein: generating a second ([0111] discloses the application of a machine learning model (understood as OpenFace’s facial analysis system in further combination with a machine learning method, as described in [0111]). It is understood that this constitutes a first machine learning model) machine learning model, by training presence or absence of occurrence of the symptom related to the major neurocognitive disorder of the patient, by using the temporal change in the occurrence intensity ([0070] discloses that the system records data of facial expressions, performing measurements of said facial expressions and movements over time. Further, as this is a response over time, this is understood to be a temporal change for the occurrence intensity (correspondent to the claims). Additionally, this measurement over time is performed using the biomarkers (as stated by [0070])) of each of the plurality of action units as a feature amount ([0152] discloses the usage of previous disease severity and biomarkers of other subjects in order to train a machine learning algorithm. It is understood that, following training, the machine learning model is the second machine learning model. Additionally, it is understood that a given value used to train the machine learning model is a given feature amount).
In re to claim 6 [dependent on claim 1], Glasner teaches wherein: the specific task is an application or an interactive application that applies a load on a cognitive function and examines the cognitive function ([0070] discloses that the system records data of facial expressions, performing measurements of said facial expressions and movements over time according to a variety of requested expressions. As this is reliant on subject response to a request to perform an action, it is understood to apply a load on cognitive function (as it requires subject thought). Additionally, this examines cognitive function due to its use to determine disease severity in relation to disorders like schizophrenia (as exemplified in [0078]).
As to claim 8, it is the method that is performed by the execution of claim 1. As such, it recites similar limitations and is rejected for the same reasons as provided above.
As to claim 9, by virtue of Glasner teaching the use of a processor (Fig. 1 (112) computational analysis unit, as indicated by [0063]) and a memory (as indicated by [0162]), it is the system that executes claim 1. As such, it recites similar limitations and is rejected for the same reasons as provided above.
Allowable Subject Matter
Claims 5 and 7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the 101 and 112(b) rejections above, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance for claim 5 (and 7 due to reciting similar claim limitations). The claimed features of claim 5 (as well as claim 7) are not anticipated nor obvious in view of prior art of record.
The closest known prior art Glasner discloses a system that performs a subject evaluation based on image data that can determine a neurological disorder, as suggested by [0078]. Additionally, it determines the detection of faces and further uses them to generate a second machine learning algorithm (as suggested by [0152]). It further performs model training with respect to temporal information (as suggested by the tracking of facial data over time mentioned in [0070]). However, it does not explicitly teach generating a machine learning algorithm according to, specifically, changing of a patient’s face direction while also using additional temporal changes as feature amounts.
Rogers et al. (US patent 9104908 B1; hereinafter “Rogers”) discloses a generated machine learning model that performs feature location as described in [0083] by virtue of a trained locator. This is further shown in Fig. 2 and [0084] to be applied to facial data of a subject. The system further indicates in [0146]-[0147] to perform adaptive orientation determination for a user’s head tracking. However, it is not explicitly indicated that the model is generated using the change in direction in combination with some form of occurrence intensity for a plurality of action units, instead basing training off a plurality of angles of face orientation. Nor does it explicitly indicate training on a plurality of temporal changes with respect to a subject’s face.
Anderson et al. (US publication 20200167949 A1; hereinafter “Anderson”) discloses a system that performs anatomical feature orientation determination. [0041] discloses the determination of different orientations in relation to the machine learning application’s training. Thus, disclosing that the system is generated with respect to a plurality of anatomical directions. It further suggests application to face recognition by virtue of face determination and vector determination as described in [0030]. However, it does not explicitly indicate the generation of a machine learning model using a change in face direction in combination with occurrence intensity for a plurality of action units. Rather, it generates its model for orientation determination on training data of an object in different orientations rather than explicitly the change in direction.
Neither the closest known prior art, nor any reasonable combination of known prior art teaches
dependent claim 5 (and 7 due to similar claim limitations) without use of impermissible hindsight bias.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN M COOMBER whose telephone number is (571)270-0950. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/KEVIN M COOMBER/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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