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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2023-0177494, filed on 12/08/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/26/2024 is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: input unit, classification model, in claim 1, and a first single model or a first ensemble model, and a second ensemble model in claims 2 and 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 5, 6, 15 and 16 contains the trademark/trade name DSM-5. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe DSM-5 and, accordingly, the identification/description is indefinite.
Claim 6, 7 and 16 and 17 contains the trademark/trade name ADOS-2 and SRS-2. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe ADOS-2 and SRS-2 and, accordingly, the identification/description is indefinite.
Claim 9 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 9 uses the word ROC, which the examiners defines as Receiver Operating Characteristics.
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.
Claim(s) 1-3, 5-6, and 11-13, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierce (US 20260213012 A1) and further in view of Ressemann (US 20240398302 A1).
Regarding claims 1 and 11, Pierce discloses an input unit configured to receive the retina image ([0026] The instant method uses an eye tracking device that leverages multiple cameras and measures actual point of gaze at anywhere from 120 to 600 HZ, though this is only one example of how the methods described herein can be implemented.);
a classification model configured to classify whether there is the ASD, and the ASD symptom severity based on the ("[0032] In another example, the disclosure relates to a method for determining if a child has autism, the method comprising: [0033] applying a trained machine learning model using eye tracking metrics from at least two eye tracking tests; and [0034] obtaining an autism risk score.
[0036] Further, those of skill in the art would recognize that each of the at least two eye tracking tests can be used in combination with other metrics such as metrics that measure autonomic nervous system arousal. Examples of such additional metrics include, but are not limited to, pupillometry, level of parent concern, and a combination thereof. Pupillometry relates to measuring the child's pupil size to establish a level of arousal.
[0057] Methods supporting diagnostic classification accuracy occurred across several stages."); and
at least one processor configured to control the input unit and the classification model, wherein the at least one processor is configured to ([0040] Making reference to FIG. 3, to generate an ARS/APS (Autism Risk Score/Autism Probability Score), a server (not shown) or software implemented on a processing circuitry (not shown) may store one or more machine learning models.):
preprocess the received retina image (fig. 3, ref 308 (preprocessing));
train the classification model such that the classification model classifies the ASD and typical development (TD) by using the preprocessed retina image, and classifies the ASD symptom severity ("[0057] Methods supporting diagnostic classification accuracy occurred across several stages. The results presented herein are based on eye tracking data for over 2,637 toddlers with and without autism. Given that toddlers with autism spectrum disorder may appear similar clinically to a toddler that has some other non-ASD delay (e.g., language delay), it was critical that the initial research, as illustrated here, contains toddlers from multiple non-ASD contrast groups. The non-ASD contrast groups contained toddlers with a language delay (LD), global developmental delay (DD), other delay (e.g., motor delay), typical development (TD), sibling of an ASD proband (Typ Sib), as well as those that show some autism characteristics but not enough to receive a formal ASD diagnosis (ASD Features).
[0058] An autism probability score 734 greater than 50 (e.g., 50 or higher or 51 or higher) is interpreted as autism and the higher the score the higher the autism probability and symptom severity."); and
allow the trained classification model to screen whether there is the ASD and the ASD symptom severity depending on the input retina image ([0058] The extracted data is fed to the “GET SET EARLY” software application as a numerical feature vector. According to the number successful test that the toddler takes, the corresponding trained machine learning model is selected and an autism probability score 734. The “GET SET EARLY” software application generates a report 732 for all successful tests and autism probability score 734. An autism probability score 734 greater than 50 (e.g., 50 or higher or 51 or higher) is interpreted as autism and the higher the score the higher the autism probability and symptom severity.). Pierce does not explicitly state that the imaging is done on a retina image. Pierce does disclose the images are of an eye or a pair of eyes.
In a similar field of endeavor of eye tracking ASD patients, Ressemann explicitly teaches a classification model configured to classify whether there is the ASD, and the ASD symptom severity based on the retina image by using a deep learning algorithm ([0163] According to certain aspects, changes in visual fixation of a patient over time with respect to certain dynamic stimuli provides a marker of possible developmental, cognitive, social, or mental abilities or disorders (such as ASD) of the patient. A visual fixation is a type of eye movement used to stabilize visual information on the retina, and generally coincides with a person looking at or “fixating” upon a point or region on a display plane. In some embodiments, the visual fixation of the patient is identified, monitored, and tracked over time through repeated eye-tracking sessions and/or through comparison with model data based on a large number of patients in similar ages and/or backgrounds.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection as disclose by Pierce, with the known method of retina observation, as taught by Ressemann, in order to yield the predictable results of earlier detection for ASD by spotting microscopic patterns or layer thinning that human eyes cannot see, since a retina shares an embryonic and anatomical connection with the brain.
Regarding claims 2 and 12, Pierce discloses a first single model or a first ensemble model configured to determine whether the ASD is present ([0058] The “GET SET EARLY” software application generates a report 732 for all successful tests and autism probability score 734. An autism probability score 734 greater than 50 (e.g., 50 or higher or 51 or higher) is interpreted as autism and the higher the score the higher the autism probability and symptom severity.); and
a second ensemble model configured to determine the ASD symptom severity ([0058] The “GET SET EARLY” software application generates a report 732 for all successful tests and autism probability score 734. An autism probability score 734 greater than 50 (e.g., 50 or higher or 51 or higher) is interpreted as autism and the higher the score the higher the autism probability and symptom severity.).
In a similar field of endeavor of eye tracking ASD patients, Ressemann teaches, in better detail, a second ensemble model configured to determine the ASD symptom severity ([0330] In some embodiments, processed session data are compared with corresponding data models to determine a level of a developmental, cognitive, social, or mental condition. A generated score is then compared to predetermined cutoff or other values to determine the patient's diagnosis of ASD, as well as a level of severity of the condition. In certain other embodiments, a patient's point-of-gaze data (e.g., visual fixation data) is analyzed over a predetermined time period (e.g., over multiple sessions spanning several months) to identify a decline, increase, or other salient change in visual fixation (e.g., point-of-gaze data that initially corresponds to that of typically-developing children changing to more erratic point-of-gaze data corresponding to that of children exhibiting ASD, or point-of-gaze data that becomes more similar to typically-developing children in response to targeted therapy).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection as disclose by Pierce, with the known method of retina observation, as taught by Ressemann, in order to yield the predictable results of earlier detection for ASD by spotting microscopic patterns or layer thinning that human eyes cannot see, since a retina shares an embryonic and anatomical connection with the brain.
Regarding claim 3 and 13, Pierce does not explicitly disclose but Ressemann teaches wherein the first ensemble model or the second ensemble model is a deep learning model based on a deep ensemble ([0223] The evaluation report can include patient information, session information, a summary of evaluation results (e.g., ASD or non-ASD). The evaluation results can also include assessment results that can include one or more index scores, e.g., social disability index score, verbal ability index score, and nonverbal learning index score, which can be obtained from an artificial intelligence (AI) model, such as a machine learning (ML) model, a single-layer neural network model, a multi-layer neural network model, or another trained AI model, in response to the input of the processed session data and the corresponding model data (described above) for a particular session.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection as disclose by Pierce, with the known method of retina observation, as taught by Ressemann, in order to yield the predictable results of earlier detection for ASD by spotting microscopic patterns or layer thinning that human eyes cannot see, since a retina shares an embryonic and anatomical connection with the brain.
Regarding claims 5 and 15, Pierce discloses classify training or verification data of the classification model into the ASD and the TD depending on only a diagnostic and statistical manual of mental disorders, fifth edition (DSM-5) criterion ([0019] In stark contrast, one way for a clinician to diagnose autism is based on clinical judgement and diagnostic criteria from the Diagnostic and Statistical Manual 5th Edition and/or the Autism Diagnostic Observation Schedule, which is a standardized instrument designed to assist clinicians in diagnosing autism. It will usually take a clinician between 2-3 hours to make a clinical diagnosis of autism.
[0036] Further, those of skill in the art would recognize that each of the at least two eye tracking tests can be used in combination with other metrics such as metrics that measure autonomic nervous system arousal. Examples of such additional metrics include, but are not limited to, pupillometry, level of parent concern, and a combination thereof. Pupillometry relates to measuring the child's pupil size to establish a level of arousal.).
Regarding claims 6 and 16, Pierce discloses classify the training or verification data of the classification model into the ASD and the TD depending on the DSM-5 criterion and an ADOS-2 score ([0019] In stark contrast, one way for a clinician to diagnose autism is based on clinical judgement and diagnostic criteria from the Diagnostic and Statistical Manual 5th Edition and/or the Autism Diagnostic Observation Schedule, which is a standardized instrument designed to assist clinicians in diagnosing autism. It will usually take a clinician between 2-3 hours to make a clinical diagnosis of autism.).
Claim(s) 4, 7 and 14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierce (US 20260213012 A1) in view of Ressemann (US 20240398302 A1), and further in view of Hoffman (US 20210338619 A1).
Regarding claims 4 and 14, Pierce and Ressemann do not disclose but in a similar field of endeavor of autism treatment, Hoffman teaches wherein the classification model is a convolutional neural network that uses ResNeXt-50 network as a backbone (fig. 1, (resnet50 deep network)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection using retina observation as disclosed by Pierce and Ressemann, with the known method of deep networks using resnet-50, as taught by Hoffman, in order to yield the predictable results of providing high-accuracy automated screening, efficient feature extraction from complex medical images, and robust support for early clinical intervention.
Regarding claims 7 and 17, Pierce and Ressemann do not disclose but in a similar field of endeavor of autism treatment, Hoffman teaches classify the training or verification data of the classification model depending on the ASD symptom severity based on a score calculated by calculating an ADOS-2 calibrated severity score and an SRS-2 T score ("[0033] FIG. 17 shows the change in ADOS-2 Total Score from Screening to Week 4 by Patient (ITT Population, Draft).
[0035] FIG. 19 shows the change from Baseline in SRS-2 DSM-5 Social Communication and Interaction T-score, Week 0-Week 8 (ITT Population, Draft).
TABLE 30 Schedule of Events Treatment Period Follow-up Screening Visit 1 Visit 2 Visit 3 Visit 4 Visit 5 Visit 6 Evaluation Days −30 to −1 Day 0 Day 14 ± 3 Day 28 ± 3 Day 60 ± 3 Day 84 ± 3 Day 120 ± 3 ABC-C X X X X X RBS-R X X X X X PSI X X X SSP X X WASI-2 X DSM-5 criteria for ASD X Skin Reactivity Testing (SRT) X X X ABC-C = Aberrant Behavior Checklist-Community; ADOS-2 = Autism Diagnostic Observation Scale-2; ASD = autism spectrum disorder; CGI-C = Clinical Global Impression of Change; CGI-S = Clinical Global Impression of Severity; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders-5th edition; PSI = Parenting Stress Index; RBS-R = Repetitive Behavior Scale-Revised; SAS = Spence Anxiety Scale; SSP = Spence Anxiety Scale; SRS-2 = Social Responsiveness Scale-2;").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection using retina observation as disclosed by Pierce and Ressemann, with the known method of deep networks using srs-2 and ados-2 scores, as taught by Hoffman, in order to yield the predictable results of providing fast, high-accuracy automated screening, efficient feature extraction from complex medical images, and robust support for early clinical intervention.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierce (US 20260213012 A1) in view of Ressemann (US 20240398302 A1), in view of Hoffman (US 20210338619 A1) and further in view of Ding (US 20250349421 A1).
Regarding claims 8 and 18, Pierce, Ressemann and Hoffman do not disclose but in a similar field of endeavor of diabetic retinopathy, Ding teaches perform random undersampling on the retina image before the ASD and the ASD symptom severity are classified ([0049] In S2, a network model U-net is built, the network model U-net is divided into a compression path and an expansion path and includes four down-samplings and four up-samplings, and two convolutions and one maximum pooling are performed prior to each sampling. The retina image is subjected to feature compression by four down-samplings in the compression path, an effective feature layer obtained by the last down-sampling is subjected to four up-samplings in the expansion path, the corresponding feature layers in the down-sampling are connected, finally, a retinal feature map is normalized by 1*1 convolution, and the built model is trained with the enhanced image data to obtain an image segmentation model.); and
perform data segmentation depending on at least one segmentation ratio ([0049] In S2, a network model U-net is built, the network model U-net is divided into a compression path and an expansion path and includes four down-samplings and four up-samplings, and two convolutions and one maximum pooling are performed prior to each sampling. The retina image is subjected to feature compression by four down-samplings in the compression path, an effective feature layer obtained by the last down-sampling is subjected to four up-samplings in the expansion path, the corresponding feature layers in the down-sampling are connected, finally, a retinal feature map is normalized by 1*1 convolution, and the built model is trained with the enhanced image data to obtain an image segmentation model.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of ASD detection using retina observation and a deep network as disclosed by Pierce, Ressemann, and Hoffman with the known method of undersampling retina images, as taught by Ding, in order to yield the predictable results of balancing imbalanced clinical cohorts, and preventing machine learning models from becoming biased.
Allowable Subject Matter
Claims 9 and 19 (and dependent claims 10 and 20) objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: prior art does not disclose or teach the unique combinations of perform preprocessing of setting and labeling an alpha zone and a beta zone in the retina image as an ROC area before the ASD and the ASD symptom severity are classified.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240062897 A1: relating to claims 6 and 7: "[0031] For purposes of the disclosure herein, the terms “autistic disorder” and “autism” may be used interchangeably to refer to a disorder that meets the diagnostic criteria of the accepted or recognized standard for diagnosis of the relevant disorder, for example, the DSM-IV, the DSM-5, both the DSM-IV and DSM-5, or a later iteration thereof.
[0036] Also for example, the observational assessment and interview data may include Autism Diagnostic Instrument-Revised (ADI-R) data, Autism Diagnostic Observation Schedule (ADOS) 1.sup.st and/or 2.sup.nd Edition (ADOS and/or ADOS-2) data, Social Responsiveness Scale (SRS) data, Social Communication Questionnaire (SCQ) data, Autism Screening Questionnaire (ASQ) data, Vineland Adaptive Behavior Scale (VABS) data, Behavior Rating Inventory of Executive Function (BRIEF) data, or combinations thereof."
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED A NASHER whose telephone number is (571)272-1885. The examiner can normally be reached Mon - Fri 0800 - 1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AHMED A NASHER/ Examiner, Art Unit 2675
/EMILY C TERRELL/ Supervisory Patent Examiner, Art Unit 2666
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