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 .
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more.
Step 1
The claimed invention in claims 1-15 are directed to statutory subject matter as the claims recite a method, system, and non-transitory computer readable medium for determining a PTSD likelihood in a patient.
Step 2A, Prong One
Regarding claims 1, 14, and 15, the recited steps are directed to a mathematical concept and mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsections (I) and (III)).
Regarding claims 1, 14, and 15, the limitations of “receiving, at an electronic device, audio input data from a patient” and “receiving, at the electronic device, clinical assessment data from the patient, wherein the clinical assessment data comprises one or more of social support data, suicide ideation and attempts data, depression severity data, or self-assessment data” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional writing down or evaluating a patient’s speech content or vocal cues and clinical assessment report.
Regarding claims 1, 14, and 15, the limitations of “determining one or more audio input indicators based on the audio input data, wherein each audio input indicator of the one or more audio input indicators represents a likelihood of a positive PTSD diagnosis based on the audio input data” and “determining one or more clinical assessment indicators based on the clinical assessment data, wherein each clinical assessment indicator of the one or more clinical assessment indicators represents a likelihood of a positive PTSD diagnosis based on the clinical assessment data” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgement, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional making a diagnosis of PTSD based on evaluations of t a patient’s speech content or vocal cues and clinical assessment report.
Regarding claims 1, 14, and 15, the limitations of “combining the one or more audio input indicators and the one or more clinical assessment indicators using at least one machine learning model; and determining the PTSD diagnosis likelihood in the patient based on the audio input data and the clinical assessment data” is a mental process of combining the two types of data to determine a PTSD diagnosis likelihood.
Step 2A, Prong Two
For claims 1, 14, and 15, the judicial exception is not integrated into a practical application. In particular, the claims recite “a memory” and “one or more processors”. The receiving of the audio input data and clinical assessment data amount to nothing more than pre-solution activity of data gathering. The memory and processor are recited at a high-level of generality and amount to nothing more than parts of a generic computer. Additionally, applicant includes “a machine learning model”, which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of receiving of the audio input data and clinical assessment data amount to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Further, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). In this case, elements of general computer are being used to implement the abstract idea of determining a PTSD likelihood in a patient. Dependent claims 2-13 further limit the abstract idea and thus are also directed towards the abstract idea.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang et al. (Pub. No.: US 2016/0078771 A1); hereinafter referred to as “Zhuang” and further in view of Moturu et al. (Pub. No.: US 2015/0370993 A1); hereinafter referred to as “Moturu”.
Regarding claims 1, 14, and 15, Zhuang discloses a method for determining a PTSD likelihood in a patient (e.g. see abstract, [0006], [0007]) comprising: receiving, at an electronic device, audio input data from a patient (e.g. see figure 5 step 502, [0044], “speech response to open-ended questions”); determining one or more audio input indicators based on the audio input data, wherein each audio input indicator of the one or more audio input indicators represents a likelihood of a positive PTSD diagnosis based on the audio input data (e.g. see figure 5 step 504); receiving, at the electronic device, clinical assessment data from the patient (e.g. see figure 5 step 506, [0044], “neurophysiological signals (e.g., EEG signals) obtained from potentially aversive collection protocols (e.g., responses to negative image stimuli)” will read on “clinical assessment data”); determining one or more clinical assessment indicators based on the clinical assessment data, wherein each clinical assessment indicator of the one or more clinical assessment indicators represents a likelihood of a positive PTSD diagnosis based on the clinical assessment data (e.g. see figure 5 step 508); combining the one or more audio input indicators and the one or more clinical assessment indicators (e.g. see figure 5 steps 510-514) using at least one machine learning model (e.g. see [0026], [0047]); and determining, by the at least one machine learning model, the PTSD diagnosis likelihood in the patient based on the audio input data and the clinical assessment data (e.g. see [0063], figure 2 element 218, “FIGS. 5 and 6 are flowcharts corresponding to the below contemplated techniques which could be implemented in the training system 200 (FIG. 2) and the detection system 300 (FIG. 3)”. Thus figure 5 will ultimately feed into the Detection Model 218).
Zhuang discloses the claimed invention except for the clinical assessment data comprises one or more of social support data, suicide ideation and attempts data, depression severity data, or self-assessment data. Moturu teaches that it is known to use such a modification as set forth in figure 1 elements s120, s130, figures 5 and 6 (this will read on depression severity data and self-assessment data) to provide new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use depression severity data and self-assessment data as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]).
Regarding claim 2, Zhuang discloses using a machine learning model (e.g. see [0026], [0047]) but is silent as to the at least one machine learning model comprises an ensemble model. Moturu teaches that it is known to use such a modification as set forth in [0046] to provide a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use an ensemble model as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]).
Regarding claim 3, Zhuang discloses using a machine learning model (e.g. see [0026], [0047]) but is silent as to the ensemble model determines the PTSD diagnosis likelihood in the patient based on the audio input data and the clinical assessment data using a plurality of machine learning algorithms. Moturu teaches that it is known to use such a modification as set forth in [0046] to provide a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use an ensemble model as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]).
Regarding claim 4, Zhuang discloses using a machine learning model (e.g. see [0026], [0047]) but is silent as to the at least one machine learning model comprises any one or more of a regression model, a decision tree model, a neural network model, a support vector machine model, a random forest model, and a weighted ensemble model. Moturu teaches that it is known to use such a modification as set forth in [0046] to provide a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use the machine learning models disclosed above as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of a variety of suitable machine learning algorithms to improve diagnostic predictions (e.g. see [0046]).
Regarding claim 5, Zhuang discloses each audio input indicator of the one or more audio input indicators represents speech emotion, vocal features, or lexical features of the audio input data from the patient (e.g. see [0044], [0061]).
Regarding claim 6, Zhuang discloses a first audio input indicator of the one or more audio input indicators comprises a speech emotion indicator, wherein the speech emotion indicator represents a likelihood of PTSD based on speech emotion extracted from the audio input data from the patient and compared to speech emotion training data at a machine-learned model trained on speech emotion data consistent with a positive PTSD diagnosis and speech emotion data consistent with a negative PTSD diagnosis (e.g. see [0044], [0061]).
Regarding claim 7, Zhuang discloses a second audio input indicator of the one or more audio input indicators comprises a vocal features indicator, wherein the vocal features indicator represents a likelihood of PTSD based on vocal features extracted from the audio input data from the patient and compared to vocal features training data at a machine-learned model trained on vocal feature data consistent with a positive PTSD diagnosis and vocal feature data consistent with a negative PTSD diagnosis (e.g. see [0044], [0061]. Note: The phrase “vocal feature” may be interpreted broadly. Vocabulary used by a patient will read on vocal feature as the words spoken are a feature of the patient’s voice in any given sentence).
Regarding claim 8, Zhuang discloses a third audio input indicator of the one or more audio input indicators comprises a lexical features indicator, wherein the lexical features indicator represents a likelihood of a positive PTSD diagnosis based on lexical features extracted from the audio input data from the patient and compared to lexical features training data at a machine-learned model trained on lexical feature data consistent with a positive PTSD diagnosis and lexical feature data consistent with a negative PTSD diagnosis (e.g. see [0044], [0061]).
Regarding claim 9, Zhuang discloses the claimed invention except for each clinical assessment indicator of the one or more clinical assessment indicators represents social support, suicide ideation and attempts, depression severity, or self-assessment of the clinical assessment data. Moturu teaches that it is known to use such a modification as set forth in figure 1 elements s120, s130, figures 5 and 6 (this will read on depression severity data and self-assessment data) to provide new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use depression severity data and self-assessment data as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]).
Regarding claim 10, Zhuang discloses a first clinical assessment indicator of the one or more clinical assessment indicators comprises a social support indicator, wherein the social support indicator represents a likelihood of a positive PTSD diagnosis based on social support features extracted from the audio input data from the patient and compared to social support training data at a machine-learned model trained on social support data consistent with a positive PTSD diagnosis and social support data consistent with a negative PTSD diagnosis (e.g. see [0061]. “social isolation” will read on “social support indicator”).
Regarding claim 11, Zhuang discloses the claimed invention except for a second clinical assessment indicator of the one or more clinical assessment indicators comprises a suicide ideation and attempts indicator, wherein the suicide ideation and attempts indicator represents a likelihood of a positive PTSD diagnosis based on suicide ideation and attempts features extracted from the audio input data from the patient and compared to suicide ideation and attempts training data at a machine-learned model trained on suicide ideation and attempts data consistent with a positive PTSD diagnosis and suicide ideation and attempts data consistent with a negative PTSD diagnosis. Moturu teaches that it is known to use such a modification as set forth in [0105] (“exhibition of suicide or death preoccupation thoughts”) to provide improved patient outcomes in terms of patient function and patient quality of life for PTSD patients (e.g. see [0017]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use depression severity data and self-assessment data as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results improved patient outcomes in terms of patient function and patient quality of life for PTSD patients.
Regarding claim 12, Zhuang discloses the claimed invention except for a third clinical assessment indicator of the one or more clinical assessment indicators comprises a depression severity indicator, wherein the depression severity indicator represents a likelihood of a positive PTSD diagnosis based on depression severity features extracted from the audio input data from the patient and compared to depression severity training data at a machine-learned model trained on depression severity data consistent with a positive PTSD diagnosis and depression severity data consistent with a negative PTSD diagnosis. Moturu teaches that it is known to use such a modification as set forth in figure 1 elements s120, s130, figures 5 and 6 (this will read on depression severity data and self-assessment data) to provide new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use depression severity data and self-assessment data as taught by Moturu in the system/method of Zhuang, since said modification would provide the predictable results of new and useful method for modeling behavior and states of depression (e.g. see [0006]) that improve upon current standards of detection, diagnosis and treatment of MDD, anxiety, and/or other depressive disorders (e.g. see [0005]).
Regarding claim 13, Zhuang discloses combining the one or more audio input indicators and the one or more clinical assessment indicators comprises the clinician adjusting the weight of a social support indicator (e.g. see figure 5 steps 510-514, [0061]. Note: Step 510 of learning the relationship between low and high-cost data will “weight” the inputs as learning the relationship will determine how the data will be projected into the matrix and “social isolation” will read on “social support indicator”).
Conclusion
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/P.C.E/Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792