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 .
Claims Accounting
Applicant's arguments, filed 08/03/2026, have been fully considered.
The following rejections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 08/03/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-5, 7-9, and 13 have been amended.
Claims 6, 10, and 11 have been canceled.
Claims 1-5, 7-9, and 12-13 are the current claims hereby under examination.
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 1-5, 7-9, and 12-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Step 1
Regarding claim 1, the claim recites a system comprising at least one camera and a processor configured to determine a correlation between detected pain and a pose of a patient, determining a mobility constraint of the patient and determining a positioning of the patient for a medical imaging procedure. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The functional limitations of determining a correlation between the detected pain experienced by the patient and the estimated pose of the patient, determining a mobility constraint of the patient, and determining a positioning of the patient for a medical imaging procedure set forth judicial exceptions. These steps describe concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exceptions in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites the functional limitation of displaying the determined correlation, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The display of the correlation does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the displayed correlation, nor does the method use a particular machine to perform the Abstract Idea. The system is comprised of at least one generic camera and processors configured to perform pre-solutional data gathering activity and perform the Abstract Ideas and the display is a generic display device configured to perform the displaying of the correlation. Therefore, the physical components of the system do not constitute a particular machine. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. It is noted that the system determines a mobility constraint a positioning of a patient, however these determinations are not relied upon in any way. The positioning or mobility constraint of the patient are not used in any way that provides an improvement to the technological field, effects a particular treatment or effects a particular change based on the determined positioning or mobility constraint.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites elements of the at least one camera configured to estimate the pose of the patient and to detect pain experienced by the patient using a machine learning algorithm, and a display unit configured to display the determined correlation. The functional limitations of estimating the pose of the patient and detecting pain experienced by the patient are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering steps necessary to perform the Abstract Idea. The details of using a machine learning algorithm to detect the pain experienced by the patient comprises using a machine learning algorithm to perform the data gathering step. Therefore, these functional limitations do not describe meaningful limitations, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the at least one camera or processor do not automatically confer eligibility on a claim directed to an Abstract Idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)). Further, the display of the determined correlation is recited at such a high level of generality that it amounts to insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exceptions itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claim 13.
The dependent claims also fail to add something more to the Abstract independent claims as they generally recite method steps pertaining to data gathering, data storage, and the further comparison (which constitutes a Mental Process and therefore an Abstract Idea) of data. Claims 2, 4-5, and 7-9 recite details pertaining to the collection of data for use in the Abstract Idea. It is noted that the use of machine learning algorithms and concepts do not confer subject matter eligibility to the limitations of claims 4, 7, and 8. Claim 3 recites the storing of the data of claim 1. Claim 12 recites details of a further observations, judgments, and comparisons capable of being performed in the human mind. The determining steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
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.
Claims 1-3, 5, 7, 9, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 2019/0050540 by Yoo et al. – previously cited, hereinafter “Yoo” in view of US Patent Publication 2019/0313966 by Lanzkowsky – previously cited, hereinafter “Lanzkowsky”.
Regarding claims 1, 5, and 9, Yoo teaches a system for detecting pain experienced by a patient in relation to a pose of the patient (Fig. 3; joint examination system), the system comprising: at least one camera configured to estimate the pose of the patient ([0139-0142]; Movement execution photographing part 133 comprises a camera used to photograph the patient during a movement) and to detect pain experienced by the patient ([0142-0144]; The pain is determined by identifying the frame where the movement stops and the patient is determined to be experiencing pain. This is performed with a camera, as it relies on the photographs); a processor configured to determine a correlation between the detected pain experienced by the patient and the estimated pose of the patient ([0143, 0193]; movement stop recognition part 134 and pain verification part 135 determine pain frame identification information, where the pose of the patient correlates with the pain of the patient. When the photographed movement information contains the pain frame identification information, the pain time movement analysis information extraction part 5614 may extract a rotation value between segment coordinate systems as the pain time movement analysis information); and a display configured to display the determined correlation ([0143, 0225-0226]; The pain frame identification information is added to photographed movement information. Doctor terminal 7 includes examination information reception display part 71, where the examination information (including the photographed movement information) is displayed. Therefore the pain frame identification information is displayed.); wherein the at least one camera is configured to detect pain experienced by the patient based on a camera image (The pain detection unit is based on the stoppage of movement determined by the movement execution photographing part 133 (i.e., camera)); wherein the processor is further configured to determine a mobility constraint of the patient based on the correlation between the detected pain experienced by the patient and the estimated pose of the patient ([0193-0194]; The pain time movement analysis information extraction part 5614 extracts pain time movement analysis information at the time when the patient feel pain. The user stops moving when they feel pain, therefore the pain time movement analysis information contains joint angle information when the user can no longer move without pain. This defines a mobility constraint and is based on the correlation between the detected pain experienced by the patient and the estimated pose of the patient.), and to determine a positioning of the patient for a medical imaging procedure based on the mobility constraint ([0195-0196]; Implant alignment predication part 563 predicts an aligned state (i.e. positioning) of an implant transplanted into the joint related to the preset movement, based on the movement analysis information. This includes generating an implant alignment image based on the mobility constraint. [0210, 0212]; The image generated is identified in lieu of X-Ray or MRI/CT imaging, and therefore the prediction can be considered an imaging procedure. It is noted that the alignment features of the patient relative to the implant can be considered the positioning for a medical imaging procedure, as this defines the position of the patient in a procedure comprising a medical image. An example of this medical image is shown in Fig. 23.) in combination with a predefined requirement regarding the positioning of the patient for the medical imaging procedure ([0214-0215]; The implant alignment image may utilize the implant product information containing information on the manufacturer, the size, the surgery department, etc. of the implant transplanted into the patient. This defines a predefined requirement, as the implant has requirements based on location (i.e., a knee implant has positioning requirements with regards to the tibia and femur).).
Yoo does not teach but does not teach wherein the at least one camera is configured to detect pain experienced by the patient based on an audio signal, or wherein the at least one camera is configured to detect pain experienced by the patient based on the camera image using a machine learning algorithm trained to recognize a facial expression of the patient on the camera image corresponding to pain experienced by the patient, or wherein the at least one camera is configured to detect pain experienced by the patient based on a physical and/or linguistic input of the patient.
Lanzkowsky teaches a system and method for determining the pain level state of a patient. Fig. 1 shows this method, including collecting facial expression and/or body language data in operation 101 and collecting speech data (i.e., audio signal/linguistic input) in operation 102. Operation 104 then combines the data and analyzes it in order to determine pain state information ([0033]). It is noted that the detection and processing of facial expressions are achieved through methods such as neural network processing, which is a machine learning algorithm ([0006, 0011]). The methods use a combination of techniques, such as facial expressions, speech, and gestures provide a more robust determination of the patient's pain level state ([0011]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system taught by Yoo wherein the at least one camera is configured to detect pain experienced by the patient based on an audio signal; and wherein the at least one camera is configured to detect pain experienced by the patient based on the camera image using a machine learning algorithm trained to recognize a facial expression of the patient on the camera image corresponding to pain experienced by the patient; and wherein the at least one camera is configured to detect pain experienced by the patient based on a physical and/or linguistic input of the patient, in order to combine data indicative of pain, such as facial expressions, speech, and gestures to provide a more robust determination of the patient's pain level state, as taught by Lanzkowsky ([0011]).
It is noted that the user completing the preset movement and stopping when feeling pain as taught by Yoo is considered a gesture, as it is a motion completed to convey information. Therefore, using facial expressions, speech, and this preset movement is akin to using facial expressions, speech, and gestures, as taught by Lanzkowsky. It is further noted that the combination of Yoo and Lanzkowsky would use a combination of methods to convey pain information together to determine the patient’s pain level state, as taught by Lanzkowsky ([0011]).
Regarding claim 2, Yoo in view of Lanzkowsky teaches the system according to claim 1, wherein the at least one camera is configured to estimate the pose of the patient in a time-resolved manner; and wherein the at least one camera is configured to detect pain experienced by the patient in a time-resolved manner (Yoo, [0142]; Pain is detected during the preset movement by the movement execution photographing part 133 over a preset period of time. Therefore, the estimation of the pose and the detection of the pain are done in a time-resolved manner.).
Regarding claim 3, Yoo in view of Lanzkowsky teaches the system according to claim 1, further comprising a storage (Yoo; examination server 5) configured to store data indicative of the detected pain experienced by the patient, data indicative of the estimated pose of the patient and/or the correlation (Yoo, [0148]; The photographed movement information, including the pain frame identification information is transmitted by the photographed movement information transmission part 136 to the examination server is received and stored by the examination server 5. In the combination of Yoo and Lanzkowsky, all data used to identify pain would be stored in the server.).
Regarding claim 7, at least one camera taught by Yoo in view of Lanzkowsky (See rejection of claims 1, 5, and 9 above) uses facial expressions to detect pain. The facial expressions, such as brow furrows, are motions with a beginning and end, and therefore contain speeds of the facial motions. A brow furrow would contain the speed of the brow furrowing (i.e., speed of a motion) of the patient (Lanzkowsky, [0060]). Therefore, the pain detection unit taught by Yoo in view of Lanzkowsky teaches wherein the pain detection unit is configured to detect pain experienced by the patient based on the camera image using a machine learning algorithm trained to recognize a fluidity and/or speed of a motion of the patient.
Regarding claim 13, Yoo in view of Lanzkowsky teaches a computer implemented method for detecting pain experienced by a patient in relation to a pose of the patient (Yoo, [0124]; The method of Yoo is carried out with patient terminal 1, which can comprise a PC, in conjunction with other components such as servers.) the method comprising: estimating the pose of the patient ([0139-0142]; Movement execution photographing part 133 comprises a camera used to photograph the patient during a movement.); detecting pain experienced by the patient based on a camera image of a facial expression of the patient using a machine learning algorithm trained to recognize the facial expression of the patient on the camera image corresponding to pain experienced by the patient (Yoo, [0142-0144]; The pain is determined by identifying the frame where the movement stops and the patient is determined to be experiencing pain. The pain detection is based on the stoppage of movement determined by the movement execution photographing part 133 (i.e., camera) and other inputs such as gestures and facial expressions using machine learning algorithms as taught by Lanzkowsky (See the rejection of claim 1).); determining a correlation between the detected pain experienced by the patient and the estimated pose of the patient (Yoo, [0143, 0193]; movement stop recognition part 134 and pain verification part 135 determine pain frame identification information, where the pose of the patient correlates with the pain of the patient. When the photographed movement information contains the pain frame identification information, the pain time movement analysis information extraction part 5614 may extract a rotation value between segment coordinate systems as the pain time movement analysis information); and displaying the determined correlation ([0143, 0225-0226]; The pain frame identification information is added to photographed movement information. Doctor terminal 7 includes examination information reception display part 71, where the examination information, including the photographed movement information is displayed. Therefore, the pain frame identification information is displayed.), determining a mobility constraint of the patient based on the correlation between the detected pain experienced by the patient and the estimated pose of the patient (Yoo, [0193-0194]; The pain time movement analysis information extraction part 5614 extracts pain time movement analysis information at the time when the patient feel pain. The user stops moving when they feel pain, in combination with inputs as taught by Lanzkowsky (i.e., facial expression), therefore the pain time movement analysis information contains joint angle information when the user can no longer move without pain. This defines a mobility constraint and is based on the correlation between the detected pain experienced by the patient and the estimated pose of the patient.), and determining a positioning of the patient for a medical imaging procedure based on the mobility constraint (Yoo, [0195-0196]; Implant alignment predication part 563 predicts an aligned state (i.e. positioning) of an implant transplanted into the joint related to the preset movement, based on the movement analysis information. This includes generating an implant alignment image based on the mobility constraint. [0210, 0212]; The image generated is identified in lieu of X-Ray or MRI/CT imaging, and therefore the prediction can be considered an imaging procedure. It is noted that the alignment features of the patient relative to the implant can be considered the positioning for a medical imaging procedure, as this defines the position of the patient in a procedure comprising a medical image. An example of this medical image is shown in Fig. 23.) in combination with a predefined requirement regarding the positioning of the patient for the medical imaging procedure (Yoo, [0214-0215]; The implant alignment image may utilize the implant product information containing information on the manufacturer, the size, the surgery department, etc. of the implant transplanted into the patient. This defines a predefined requirement, as the implant has requirements based on location (i.e., a knee implant has positioning requirements with regards to the tibia and femur).).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Lanzkowsky in view of US Patent Publication 2021/0338110 by Gingrich et al. – previously cited, hereinafter “Gingrich”.
Regarding claim 4, Yoo in view of Lanzkowsky teaches the system according to claim 1, but does not teach wherein the at least one camera is configured to estimate the pose of the patient using a convolutional neural network for estimating a position of a plurality of key points of the patient on a camera image.
Gingrich teaches a system configured to receive image frames of a human body captured at a specific time. A pose estimator including probability distribution models generated by neural networks 158 are applied to identify body parts, such as skeletal point (i.e., key points) to define a two-dimensional dataset. The key points are then used in conjunction with assumptions about the human body to derive joint parameters, such as orientations (i.e., angles) in three dimensions ([0024-0028]). Using the pose estimator provides enhanced goniometry while minimizing the subjectivity during a musculoskeletal analysis ([0005]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system of Yoo in view of Lanzkowsky such that the pose estimation unit is configured to estimate the pose of the patient using a convolutional neural network for estimating a position of a plurality of key points of the patient on a camera image, in order to obtain enhanced goniometry while minimizing the subjectivity during the musculoskeletal analysis, as taught by Gingrich ([0005]).
It is noted that Yoo teaches a musculoskeletal analysis to obtain the joint angles of the knee and relies on joint angles in three dimensions (such as that shown in Fig. 18). This analysis would be substituted for the analysis of Gingrich to achieve the benefits taught by Gingrich ([0005]).
Claims 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Lanzkowsky, as applied to claim 5, in view of US Patent Publication 2023/0065759 by Bacarella et al. – previously cited, hereinafter “Bacarella”.
Regarding claim 8, Yoo in view of Lanzkowsky teaches the system according to claim 5, but does not teach wherein the at least one camera is configured to detect pain experienced by the patient based on the audio signal using a machine learning algorithm trained to recognize sounds associated with pain.
Bacarella teaches a method of capturing biometric data from a patient and determining a severity level of a known problem ([0018]; i.e., adverse event). One way of determining the level of the severity of the patient is by capturing audio to determine the emotional state of the patient. Tone of voice or inflection are useful in determining the emotion that may indicate severe pain of the patient and can be predicted by using trained machine learning algorithms ([0038]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system taught by Yoo in view of Lanzkowsky wherein the at least one camera is configured to detect pain experienced by the patient based on the audio signal using a machine learning algorithm trained to recognize sounds associated with pain, in order to determine emotion that may indicate severe pain of the patient, as taught by Bacarella ([0038]).
It is noted that the combination of Yoo in view of Lanzkowsky teaches a system where pain is determined based on multiple responses from the patient in order to determine a more robust determination of the pain. The modification in view of Bacarella includes emotion derived from audio, which is another indicator of pain that can contribute to a more robust determination of pain.
Regarding claim 12, Yoo in view of Lanzkowsky teaches the system according to claim 1, but does not teach wherein the system is configured to compare the determined correlation with a record of an earlier correlation of the same patient.
Bacarella teaches a system that uses real-time biometric data of patients to determine a severity level of a known problem ([0018]; i.e., adverse event). The biometric data can be compared to the baseline data of the patient in order to determine the severity of the known problem. Based on the severity of the known problem, the problem may be ignored, a treatment may be proposed or a notification may be generated to alert a medical professional to determine the appropriate action ([0045]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system of Yoo in view of Lanzkowsky wherein the system is configured to compare the determined correlation with a record of an earlier correlation of the same patient, in order to determine the severity of the pain and generate an alert to a medical professional to determine the appropriate action if needed, as taught by Bacarella ([0045]).
It is noted that Yoo is directed towards a monitoring method for a known problem (joint pain). Incorporating baseline data to determine if the pain defines an emergency requiring immediate attention/treatment would be beneficial, as the known problem may have varying levels of severity, necessitating several levels of attention/treatment.
Response to Arguments
Applicant’s arguments, filed 08/03/2026 have been fully considered.
The amendments to the claim do not invoke claim interpretation under 35 U.S.C. 112(f) for any claim limitations.
Applicant’s arguments regarding the rejection of claim 1 under 35 U.S.C. 101 are acknowledged. Applicant argues that claim 1 recites a concrete medical measurement and positioning system is acknowledged. This argument is not found persuasive. The claimed invention is directed towards a system wherein a camera estimates the pose of a patient and detects pain experienced by the patient, then determines a correlation, a mobility constraint, and a positioning. The system does not provide an improvement to the technological field, effect a particular treatment or effect a particular change based on the determined correlation, positioning, or mobility constraint. The correlation is output on a display, but the determined positioning and mobility constraints are not relied upon in any way. In other words, the mobility constraint and the positioning are determined, but are not applied in any way.
Applicant’s arguments regarding the rejection of claim 1 under 35 U.S.C. 102(a)(1) and 103 are acknowledged. Amended claim 1 includes the limitations of previous claims 6, 10, and 11. Applicant’s arguments related to the limitations of “wherein at least one camera is configured to detect pain experienced by the patient based on a camera image of the face of the patient using a machine learning algorithm trained to recognize a facial expression of the patient on the camera image corresponding to pain experienced by the patient” are directed towards Yoo. These arguments are moot as the newly presented claims are rejected under 35 U.S.C. 103 as being obvious over Yoo in view of Lanzkowsky. Applicant further argues that Lanzkowsky teaches that pain state information can be determined by using neural network processing, and this is not analogous to a trained machine learning algorithm. This argument is not found persuasive. A neural network is a form of machine learning. Further, Lanzkowsky teaches “[a]nalysis of pain levels of patients as they interact with a diagnostic system may be performed by gathering data from measuring facial expressions, head and body gestures, speech analysis and physiological conditions. This is done using machine learning techniques” in par. [0006]. It is noted that an untrained machine learning algorithm would not be able to analyze pain levels, and therefore it is implied that the machine learning techniques of Lanzkowsky are trained to perform their functions.
Applicant argues that predicting an aligned state of an implant, deriving or displaying implant alignment information is not the same as “to determine a positioning of the patient for a medical imaging procedure based on the mobility constraint in combination with a predefined requirement regarding the positioning of the patient for the medical imaging procedure”. This argument is not found persuasive. The prediction of the aligned state of the implant is based on the movement information that comprises the pain frame information (i.e., mobility constraint). Further, the prediction of the aligned state of the implant comprises a medical image (See Fig. 23 of Yoo), and includes the positioning of the implant relative to the patient. This positioning of includes both the positioning of the implant, and the positioning of the patient, and is based on the implant product information (predefined requirements for this procedure). Applicant is invited to specify the type or types of medical imaging procedures to distinguish the claimed invention from the combination of Yoo and Lanzkowsky as applied above.
Conclusion
Applicant's amendment necessitated the new ground(s) 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.
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
