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 .
Amendment
This action is in response to the Amendment filed on 6/17/2026.
Claims 1, 4, 7, 8, 11, 14, 15, 18 and 21-32 are pending.
Response to Arguments
Applicant's arguments with respect to claims 1, 4, 7, 8, 11, 14, 15, 18 and 21-32 have been considered.
Rejections under 35 U.S.C. 101
In view of the amendments to claims 15 and its dependents that now recite a non-transitory computer readable medium, the rejection of claims 15 and its dependent claims has been withdrawn.
Independent claims 1,8 and 15 as amended now recite the steps of:
collecting manual input from an input interface of a pain assessment regularizing system;
collecting image capture input or video capture input from an image capture device of the pain assessment regularizing system directed upon an expressive location on a patient's body;
collecting physiological measurement input from a physiological sensor of the pain assessment regularizing system affixed to an operative position on a patient's body, wherein the expressive location and the operative position do not correspond to a location where the patient is experiencing or reporting pain;
weighing the manual input, the image capture input or video capture input, and the physiological measurement input using a trained weight set that differentially weights the image capture input or video capture input and the physiological measurement input over the manual input; and
converting each of the manual input, the image capture input or video capture input, and the physiological measurement input into a respective regularized pain assessment factor, wherein converting comprises mapping each input to a value on a common normalized scale;
deriving a combined regularized pain assessment factor collectively from each regularized pain assessment factor;
recording each combined regularized pain assessment factor in a time series; and
forecasting a trend of the time series of combined regularized pain assessment factors by fitting a regression model to the time series.
Applicant’s arguments with respect to the rejection under 35 U.S.C. 101 on the grounds of the claims being directed to an abstract idea have been fully considered. Applicant’s arguments are not persuasive for the following reasons.
In Independent claims 1,8 and 15, the newly added limitations of
weighing the manual input, the image capture input or video capture input, and the physiological measurement input using a trained weight set that differentially weights the image capture input or video capture input and the physiological measurement input over the manual input; and
converting each of the manual input, the image capture input or video capture input, and the physiological measurement input into a respective regularized pain assessment factor, wherein converting comprises mapping each input to a value on a common normalized scale;
deriving a combined regularized pain assessment factor collectively from each regularized pain assessment factor;
recording each combined regularized pain assessment factor in a time series; and
forecasting a trend of the time series of combined regularized pain assessment factors by fitting a regression model to the time series.
do not improve the technology or the processing of the computer and instead improve the abstract idea of the pain assessment itself. the steps of weighing inputs using a trained weight set that uses differential weights for the inputs, mapping to a normalized scale, deriving a combined regularized pain assessment factor, recording each factor in a time series and forecasting are steps that involve data manipulation and recording and prediction and that can be done manually by a clinician. The learning model that has been trained is a generic learning model and is used for data manipulation. The alleged improvement appears to reside in the abstract idea itself, not in the additional elements recited in the claim. Appellant does not point to, nor is there any evidence that a human mind is incapable of executing the claimed steps.
The steps of collecting data using manual input, an image capture device and a physiological sensor are data collection steps using well known generic data collection devices. Applicant’s originally filed specifications discuss the processors as being generic well known processors including laptop devices (e.g. [0131], [0132]). Each step does no more than require a generic computer to perform generic computer functions. The claims do not, for example, purport to improve the functioning of the computer itself.
In short, the claims do not recite any additional elements that:(1) improve the functioning of a computer or other technology, (2) are applied with any particular machine, (3) effect a transformation of a particular article to a different state, and (4) are applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.05(a){c), (e)-(h). Therefore, the amended claims are rejected under 35 U.S.C. 101.
Rejections under 35 U.S.C. 102 and 103 - Annoni et al (U.S. Patent Application Publication No. 2020/0359960A1 hereinafter “Annoni”), Annoni et al (U.S. Patent Application Publication No. 2018/0193644A1 hereinafter “Annoni’644)
Applicant argues that Annoni does not teach at least the steps of: (i) weighing the inputs using a trained weight set that differentially weights the objective image-capture and physiological inputs over the subjective manual input; (ii) converting that comprises mapping each input to a value on a common normalized scale; and (iii) forecasting a trend of a time series of combined regularized pain assessment factors by fitting a regression model to the time series.
Applicant’s arguments are persuasive and upon further search and consideration, the rejection is withdrawn.
New Claims
New claims 21-32 have been addressed in the current office action as discussed below.
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, 4, 7, 8, 11, 14, 15, 18 and 21-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 8 and 15 recite a method, a system and a non- transitory computer-readable storage medium storing computer-readable instructions for performing operations comprising:
collecting manual input from an input interface of a pain assessment regularizing system;
collecting image capture input or video capture input from an image capture device of the pain assessment regularizing system directed upon an expressive location on a patient's body;
collecting physiological measurement input from a physiological sensor of the pain assessment regularizing system affixed to an operative position on a patient's body, wherein the expressive location and the operative position do not correspond to a location where the patient is experiencing or reporting pain;
weighing the manual input, the image capture input or video capture input, and the physiological measurement input using a trained weight set that differentially weights the image capture input or video capture input and the physiological measurement input over the manual input; and
converting each of the manual input, the image capture input or video capture input, and the physiological measurement input into a respective regularized pain assessment factor, wherein converting comprises mapping each input to a value on a common normalized scale;
deriving a combined regularized pain assessment factor collectively from each regularized pain assessment factor;
recording each combined regularized pain assessment factor in a time series; and
forecasting a trend of the time series of combined regularized pain assessment factors by fitting a regression model to the time series.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a method, claim 8 is directed to a system and claim 15 is directed to a non-transitory computer-readable storage medium storing computer-readable instructions for performing operations and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1, 8 and 15 recite a method, a system and a computer-readable storage medium storing computer-readable instructions for performing operations comprising:
collecting manual input from an input interface of a pain assessment regularizing system;
collecting image capture input or video capture input from an image capture device of the pain assessment regularizing system directed upon an expressive location on a patient's body;
collecting physiological measurement input from a physiological sensor of the pain assessment regularizing system affixed to an operative position on a patient's body, wherein the expressive location and the operative position do not correspond to a location where the patient is experiencing or reporting pain;
weighing the manual input, the image capture input or video capture input, and the physiological measurement input using a trained weight set that differentially weights the image capture input or video capture input and the physiological measurement input over the manual input; and
converting each of the manual input, the image capture input or video capture input, and the physiological measurement input into a respective regularized pain assessment factor, wherein converting comprises mapping each input to a value on a common normalized scale;
deriving a combined regularized pain assessment factor collectively from each regularized pain assessment factor;
recording each combined regularized pain assessment factor in a time series; and
forecasting a trend of the time series of combined regularized pain assessment factors by fitting a regression model to the time series.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1, 8, and 15 recite an abstract idea of a mental process.
Steps 1-3 are directed to a pre-solution activity of collecting data using generic data collection devices.
Seps 4-8 are limitations as drafted in the claims, under the broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting a generic physiological sensor worn on the patient’s body, an input interface, a generic processor, memory and a generic patient-controlled analgesia device, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example, a clinician can perform the steps of weighing inputs, converting the inputs to a regularized factor by mapping to a normalized scale, deriving a combined factor, recording the factors in a time series and forecasting a trend as claimed manually via pen and paper.
As discussed in the response to
The learning model that has been trained is a generic learning model and is used for data manipulation. The alleged improvement appears to reside in the abstract idea itself, not in the additional elements recited in the claim. Appellant does not point to, nor is there any evidence that a human mind is incapable of executing the claimed steps.
Further, dependent Claims 4,7,11,14,18, 21-32 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 8 and 15 recite the additional elements of a generic physiological sensor worn on the patient’s body, an input interface, a generic processor, memory. while claims 7 and 14 recite a generic patient-controlled analgesia device. The new dependent claims recite additional image capture device that comprise an infrared or depth sensor, sensors and well-known physiological sensors. However, these elements are recited at a high level of generality performing the function of generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements when considered individually and in combination are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, a generic physiological sensor worn on the patient’s body, an input interface, a generic processor, memory and a generic patient-controlled analgesia device as recited to perform the claimed steps and further claims 7 and 14 that recite collecting patient-controlled analgesia (PCA) input and the new dependent claims recite additional image capture device that comprise an infrared or depth sensor, sensors and well-known physiological sensors. However, these amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (For example Annoni et al (U.S. Patent Application Publication Number: US 2020/0359960A1, hereinafter “Annoni” - APPLICANT CITED), Zuckerman-Stark et al (U.S. Patent Application Publication Number: US 2020/0359914A1, hereinafter “Stark” - APPLICANT CITED), Thakur et al (U.S. Patent Application Publication Number: US 2020/0188673 A1, hereinafter “Thakur”- APPLICANT CITED) teach physiological sensor worn on the patient’s body, an input interface, a generic processor, memory and a generic patient-controlled analgesia device) and conventional limitations that amount to mere instructions or elements to implement the abstract idea. Osorio (U.S. Patent Application Publication Number: US 2014/0246549 A1, hereinafter “Osorio”) teaches infrared imaging with facial recognition software to detect changes in facial expression indicative of pain (e.g. [0105]). In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
No prior art was found teaching individually, or suggesting in combination, all of the features of the applicants' invention, specifically the steps of "weighing the manual input, the image capture input or video capture input, and the physiological measurement input using a trained weight set that differentially weights the image capture input or video capture input and the physiological measurement input over the manual input; and converting each of the manual input, the image capture input or video capture input, and the physiological measurement input into a respective regularized pain assessment factor, wherein converting comprises mapping each input to a value on a common normalized scale; deriving a combined regularized pain assessment factor collectively from each regularized pain assessment factor; recording each combined regularized pain assessment factor in a time series; and forecasting a trend of the time series of combined regularized pain assessment factors by fitting a regression model to the time series” in combination with the recited limitations of the claimed invention.
While no prior art rejections have been provided for the claims they cannot be indicated as allowable due to the rejection under 35 U.S.C. 101 discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin et al (U.S. Patent Application Publication Number: US 2019/0362843 A1, hereinafter “Lin”) teaches a system and method for pain monitoring and management comprising data collection using physiological data sensors, infrared image sensors and user input and using dynamic machine learning models to predict and treat pain (e.g. Abstract).
Platt et al (U.S. Patent Application Publication Number: US 2001/0037222 A1, hereinafter “Platt”) teaches a system and method for pain assessment comprising data collection patient pain episode and generating a multidimensional pain score by using weighting factors (e.g. Fig. 5A) to quantify a pain condition for the patient.
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MALLIKA DIPAYAN FAIRCHILD whose telephone number is (571)270-7043. The examiner can normally be reached Monday- Friday 8 am-5pm EST.
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/MALLIKA D FAIRCHILD/Primary Examiner, Art Unit 3792
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