Detailed Action
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The Preliminary Amendment filed 12/13/22 has been entered. Claims 1-19 are pending.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-12, 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
and thus is directed to non-patentable subject matter. Specifically, the claims are directed toward the judicial exception of an abstract idea without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts.
STEP 1:
Per Step 1 of the two-step analysis, the claims are determined to include a method (independent claim 1), an apparatus (independent claim 12) respectively and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
Step 2A, Prong 1:
The independent claim 1 recites “determining a level of certainty of a patient's response to a stimulus perception of a subjective medical test” (A person can mentally evaluate a stimulus perception of a subjective medical test and make a judgement to determine a level of certainty of patient’s response)
“detecting at least one physiological signal from the patient while the patient is providing a response to the stimulus perception” (A person can mentally evaluate a patient’s response to the stimulus and detect a physiological signal from the patient)
“determining the level of certainty of the patient's response to the stimulus perception from the at least one physiological signal” (A person can mentally evaluate a the stimulus perception of a patient and determine the level of certainty of the patient's response)
Furthermore, the signal being input data to machine learning model, set of training data including a signal associated with a level of certainty, and a determined level of certainty being output of the model are all rearrangements of data which may be mental processes.
Similarly, independent claim 12 recites “control unit circuitry configured to determine the a level of certainty of a patient's response to a stimulus perception of the subjective medical test” (A person can mentally evaluate a stimulus perception of a subjective medical test and make a judgement to determine a level of certainty of patient’s response)
“the level of certainty being determining from at least one physiological signal of the patient while the patient is providing the response to the stimulus perception” (A person can mentally evaluate a patient’s response to the stimulus and detect a physiological signal from the patient)
Furthermore, the at least one physiological signal being as an input data to a trained machine learning model, and the determined level of certainty being as an output of the trained machine learning model are all rearrangements of data which may be mental processes.
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls under the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Regarding dependent claim 2, personal homogenizing the input or output data may be a mental process to organize and manipulate the data and may be performed via mathematical statistical formulas.
Regarding claim 3, standardizing the signal data may be a mental process to organize and manipulate the data and may be performed via mathematical statistical formulas (note that the claim defines the physiological signal as input data).
Regarding claims 4 and 16, a person can mentally detect a physiological signal from the patient and determine a level of certainty (note the claim defines a reference signal and level of certainty as a set of reference data).
Regarding claim 5, the signal is simply defined as input data to the trained machine learning model which may simply be a set of instructions to mentally manipulate the data.
Regarding claim 6 and 17, the reference signal is merely a piece of data used as a threshold in a mental process.
Regarding claim 7, the formatting of signal data may be mental manipulation of data.
Regarding claim 8, classifying input data using the trained machine learning model may simply be using a set of instructions to mentally arrange data and determine certainty.
Regarding claim 9, determining a score by regressing data using the trained machine learning model to determine a level of certainty may simply be using a set of instructions to mentally manipulate and arrange data such as by using statistical formulas.
Regarding claim 10 and 15, defining the test and perception may be mental labeling of data.
Regarding claim 11, note the alternative language and weighting a result may be mentally manipulating and arranging data such as by using a mathematical formula.
All these claim features may be accomplished by applying particular calculations, groupings, inspection, and general manipulation of data. The invention is thus directed to mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Step 2A, Prong 2
This judicial exception is not integrated into a practical application. This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
As explained in Step 2A Prong 1, “the at least one physiological signal being an input data to a machine learning model” and “the determined level of certainty being the output of the trained machine learning model” are insignificant extra solution activities.
“a machine learning model trained based on a set of training data” and “the set of training data including at least one physiological signal associated to a level of certainty of a patient's response” are mere instructions to apply the judicial exception using generic computer.
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application. Therefore, the claim is directed to an abstract idea.
In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations of control circuitry amount to necessary data gathering and outputting. See MPEP 2106.05. It is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using any generic computer. See MPEP 2106.05(f). The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Thus, under Step 2A, the Examiner holds that the claims are directed to concepts identified as abstract ideas.
STEP 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Regarding “the at least one physiological signal being an input data to a machine learning model” and “the determined level of certainty being the output of the trained machine learning model,” these insignificant extra solution activities are well understood routine and conventional activities. See
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
The limitations “a machine learning model trained based on a set of training data” and “the set of training data including at least one physiological signal associated to a level of certainty of a patient's response” remain mere instructions to apply the judicial exception using generic computer.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Please note though that claims 13-14 do recite a physical detector such as a microphone and are therefore they, as well as claims 18-19 depending therefrom, are not rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kon et al “Kon” (WO 2020039428 A1) and Shriberg et al “Shriberg” (US 11942194 B2).
(Please see the attached copies of Kon and Shriberg that number paragraphs in the same manner as that used in this Action).
7. Regarding claim 1, Kon shows a computer implemented method for determining a patient's response to a stimulus perception of a subjective medical test (para 8, 12, 32, 50 show determining the human subject’s cognitive or emotional response to a stimulus in a given subjective test. Para 49, 63 also show for example determining the human subject response in an eye blink test to a visual stimulus. Para 43 show it is a psycho-physiological test which is a type of medical test and the human subject would thus be a patient), the method comprising: detecting at least one physiological signal from the patient while the patient is providing a response to the stimulus perception (para 32, 44-45 show detecting the physiological stress signal from the patient while the patient is responding to the stimulus) and determining the patient's response to the stimulus perception from the at least one physiological signal (para 31-32, 65-67 show determining a patient response to the stimulus based on the stress signal), the at least one physiological signal being an input data to a machine learning model trained based on a set of training data (para 20, 32, 34, 44 show the stress signals are input to the trained machine learning model), the set of training data including at least one physiological signal associated to a patient's response (para 20, 35, 60 show the training set includes the stress signal associated with the patient’s response). Kon does not explicitly show that the patent’s response is associated with a determined level of certainty such that output of the trained machine learning model is the determined level of certainty. Shriberg however does show associating a patient’s response with a determined level of certainty such that output of the trained machine learning model is the determined level of certainty (para 31, 33, 41, 70 show the audio response and other responses of a patient to a medical testing prompt and determining a confidence level to it. Para 31, 42, 227, 230, 270 show the output of a trained machine learning model is the determined level of certainty). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to associate a determined level of certainty to a patient’s response such that output of the trained machine learning model is the determined level of certainty as is shown in Shriberg, in the trained machine learning classifier of signal response determination of Kon, because it would provide an efficient way to use a trained machine learning model to determine a patient’s response based on the physiological signal. Determining the level of certainty would help determine what responses are reasonably indicated by the physiological signal.
8. Regarding claim 2, in addition to that mentioned for claim 1, note the alternative recitation. The input data includes the user response data. Kon para 14, 65 show calculating statistical values of the stress signal data including a standard deviation. Furthermore, Shriberg shows a step of inter and / or intra personal homogenizing the input data or the output of the trained machine learning (Shriberg para 70, 89, 212 show normalizing the user response data over various times and uses as well as among other patients). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to inter or intra personal normalize/homogenize the input data in Kon as is done in Shriberg, because it would provide an efficient way to provide accurate data and minimize anomalies with which to accurately determine the patient’s response.
9. Regarding claim 3, in addition to that mentioned for claim 2, the step of inter and/or intra personal homogenizing further comprises standardizing the at least one physiological signal, the at least one standardized physiological signal being the input data to the trained machine learning (as mentioned, Kon para 14, 65 show calculating statistical values of the stress signal data including a standard deviation).
10. Regarding claim 4, in addition to that mentioned for claim 2, the inter and/or intra personal homogenizing further comprises detecting at least one reference physiological signal associated to a reference level of certainty of the patient's response (Shriberg para 126, 350, 352, 425 show a response associated to a benchmark standard certainty level – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2. Shriberg para 119, 192, 485 further show a response associated with a reference threshold level of confidence – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2), the at least one reference physiological signal and the reference level of certainty of the patient's response being a set of reference data (Shriberg para 126, 350, 352 show a reference response and benchmark standard level are a set of reference data), and wherein the level of certainty of the patient's response to the stimulus perception is determined from the at least one physiological signal and from the set of reference data (Shriberg para 126, 350, 352 show the level of certainty of the response is determined from the response and the reference response and reference level of certainty, and Shriberg para 119, 192, 485 further show a response associated with a reference threshold level of confidence - motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2).
11. Regarding claim 5, in addition to that mentioned for claim 4, the at least one reference physiological signal is an input data to the trained machine learning model (Kon para 20, 32, 34, 44 show the stress signals are input to the trained machine learning model, and this would include any reference stress signal). The motivation to have the reference signal is the same as that mentioned for claim 2, from which claim 4 depends, and now from which claim 5 depends.
12. Regarding claim 6, in addition to that mentioned for claim 4, the at least one reference physiological signal is used to threshold the output data (as mentioned for claim 4, Shriberg para 119, 192, 485 show a response associated with a reference threshold level of confidence – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2).
13. Regarding claim 7, the physiological signals comprise signals having different modalities, and wherein the method further comprises formatting the physiological signals having different modalities (Kon para 20, 32, 34, 37 show the stress signals have different classes, and Kon para 45, 61, 62 show different format protocols for the different classes of signals).
14. Regarding claim 8, the level of certainty is a category, and wherein determining the category of certainty further comprises classifying the input data by way of the trained machine learning model to determine the level of certainty (Shriberg 227, 230, 311, 353 show the confidence level is a score category determined by classifying the response input data in the trained machine learning model to determine the confidence level). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have this in Kon, because it would provide an efficient way to use a trained machine learning model to determine a patient’s response based on the physiological signal. Classifying the input data by the trained machine learning model to determine the confidence level would help determine what responses are reasonably indicated by the physiological signal.
15. Regarding claim 9, the level of certainty is a score (Shriberg para 31, 55, 89 show the confidence level is a score) and determining the score of certainty further comprises regressing the input data by way of the trained machine learning model to determine the level of certainty (Shriberg para 256, 276, 346, 409 show the using the regression model on the response input data to the trained machine learning model). Motivation to use this in Kon is the same as that mentioned for claim 1.
16. Regarding claim 10, Kon show the subjective medical test is a subjective ophthalmic test (para 49, 63 show the eye blinking test), the stimulus perception is a visual stimulus perception (para 17, 62 show the visual stimulations). Shriberg para 208, 214, 276, 335 also show eye gazing tests in response to visual stimulations.
17. Regarding claim 11, in addition to determining a level of certainty according to claim 1 (see claim 1); please note the alternative recitation of: informing of the determined level of certainty, and/or weighting a result of the subjective medical test, and/or changing manually or automatically the stimulus perception by taking into account the determined level of certainty. Shriberg para 134, 141, 227 weights the result of the test, and para 250 for example informs the confidence level via a score. The motivation to use either of these in Kon is the same as that mentioned for claim 1, namely to provide an efficient way to use a trained machine learning model to determine a patient’s response based on the physiological signal.
18. Regarding claim 12, Kon shows a device for a subjective medical test of a patient (para 49, 63 show the device for measuring eye blinking, para 39 shows devices for other subjective medical tests of the human subject, and para 43 show it is a psycho-physiological test which is a type of medical test and the human subject would thus be a patient), comprising: control unit circuitry configured to determine the patient's response to a stimulus perception of the subjective medical test (para 42, 72 show the circuitry to control the medical measuring devices, para 32, 44-45 show detecting the physiological stress signal from the patient while the patient is responding to the stimulus), the response being determining from at least one physiological signal of the patient while the patient is providing the response to the stimulus perception (para 31-32, 65-67 show determining a patient response to the stimulus based on the stress signal), the at least one physiological signal being as an input data to a trained machine learning model (para 20, 32, 34, 44 show the stress signals are input to the trained machine learning model). Kon does not explicitly show that the patent’s response is associated with a determined level of certainty such that output of the trained machine learning model is the determined level of certainty. Shriberg however does show associating a patient’s response with a determined level of certainty such that output of the trained machine learning model is the determined level of certainty (para 31, 33, 41, 70 show the audio response and other responses of a patient to a medical testing prompt and determining a confidence level to it. Para 31, 42, 227, 230, 270 show the output of a trained machine learning model is the determined level of certainty). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to associate a determined level of certainty to a patient’s response such that output of the trained machine learning model is the determined level of certainty as is shown in Shriberg, in the trained machine learning classifier of signal response determination of Kon, because it would provide an efficient way to use a trained machine learning model to determine a patient’s response based on the physiological signal. Determining the level of certainty would help determine what responses are reasonably indicated by the physiological signal.
19. Regarding claim 13, Kon shows a test unit circuitry configured to provide a subjective test associated to stimulus perceptions (para 42, 72 show circuitry for the medical test devices that provide the test measurement, para 39, 49, 63 show the subjective medical tests are associated to different stimulations), and a detector configured to detect at least one physiological signal from the patient while the patient is providing a response to the stimulus perception (para 39, 49, 63 show the various detectors which then produce some stress signal in response from the patient responding to the test, para 32, 44-45 show detecting the physiological stress signal from the patient while the patient is responding to the stimulus).
20. Regarding claim 14, please note the alternative recitation. Kon para 42 shows the microphone, para 48 shows the blood pressure detector, para 49 shows the temperature detector.
21. Regarding claim 15, Kon show the subjective medical test is a subjective ophthalmic test (para 49, 63 show the eye blinking test), the stimulus perception is a visual stimulus perception (para 17, 62 show the visual stimulations). Shriberg para 208, 214, 276, 335 also show eye gazing tests in response to visual stimulations.
22. Regarding claim 16, in addition to that mentioned for claim 3, the inter and/or intra personal homogenizing further comprises detecting at least one reference physiological signal associated to a reference level of certainty of the patient's response (Shriberg para 126, 350, 352, 425 show a response associated to a benchmark standard certainty level – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2. Shriberg para 119, 192, 485 further show a response associated with a reference threshold level of confidence – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2), the at least one reference physiological signal and the reference level of certainty of the patient's response being a set of reference data (Shriberg para 126, 350, 352 show a reference response and benchmark standard level are a set of reference data), and wherein the level of certainty of the patient's response to the stimulus perception is determined from the at least one physiological signal and from the set of reference data (Shriberg para 126, 350, 352 show the level of certainty of the response is determined from the response and the reference response and reference level of certainty, and Shriberg para 119, 192, 485 further show a response associated with a reference threshold level of confidence - motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2).
23. Regarding claim 17, in addition to that mentioned for claim 5, the at least one reference physiological signal is used to threshold the output data (as mentioned for claim 4, Shriberg para 119, 192, 485 show a response associated with a reference threshold level of confidence – motivation to combine this with Kon such that the response would be the physiological signal is the same as that mentioned for claim 2).
24. Regarding claim 18, Kon show the subjective medical test is a subjective ophthalmic test (para 49, 63 show the eye blinking test), the stimulus perception is a visual stimulus perception (para 17, 62 show the visual stimulations). Shriberg para 208, 214, 276, 335 also show eye gazing tests in response to visual stimulations.
25. Regarding claim 19, Kon show the subjective medical test is a subjective ophthalmic test (para 49, 63 show the eye blinking test), the stimulus perception is a visual stimulus perception (para 17, 62 show the visual stimulations). Shriberg para 208, 214, 276, 335 also show eye gazing tests in response to visual stimulations.
Conclusion
26. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Fink (US 11684256 B2) shows measuring a patient response in an ophthalmic test to a visual stimulus.
b) Bostoen (CA 3050225 A1) shows predictive modeling and training devices with biofeedback signal measuring.
27. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 Est.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at 571-272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN P SAX/ Primary Examiner, Art Unit 2146