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 .
Response to Amendment
The amendment, filed 03/16/2026, has been entered. The examiner notes claims 1-4 are pending.
Response to Arguments
The objection to claim 6 has been rendered moot as the claim has been canceled. There are no further outstanding claim objections.
The 35 USC 112 rejection of claims 5-9 has been rendered moot as claims 5-9 have been canceled. There are no further outstanding 35 USC 112 rejections.
Applicant’s argument, see Remarks pages 7-8, filed 03/16/2026, with respect to the objection to the specification has been fully considered and are persuasive. The applicant has amended the specification to overcome the objection. The objection to the specification has been withdrawn.
Applicant's arguments, see Remarks pages 8-13, filed 03/16/2026, have been fully considered but they are not persuasive.
In response to the applicant’s argument that the claims should be drawn to eligible subject matter at Step 2A Prong 1 of the Alice/Mayo test as the operations cannot be performed in the human mind, the examiner respectfully disagrees. The examiner contends that the human mind is capable of mirroring the capabilities of a convolutional neural network (hereinafter CNN). The examiner notes that CNNs emulate the brain's hierarchical, spatially-aware, and adaptive information processing strategies. For example, reading text on a screen requires detecting local features (e.g., edges and contrasts), processing those signals into simple features (e.g., lines and orientations), and then integrating the features into shapes, objects, and semantic meaning. Thus, the processing described in the amended claims is capable of being performed by the human mind.
In response to the applicant’s argument that the steps of “establishing” a coordinate system, “extracting” joint image regions, “performing” convolution, pooling, dropout and processing radiograph pixel data, “detecting” anatomical keypoints, and “constructing” characteristic axes are incapable of being performed in the human mind and/or are not mathematical concepts, the examiner respectfully disagrees. The step of “establishing” a coordinate system is a mental process capable of being performed in the human mind. For example, the human mind is capable of spatial orientation, where the mind can determine an up and down direction as being along the Y-axis and a left and right direction along the X-axis. The step of “extracting” joint image regions is an example of a mental process capable of being performed in the human mind. For example, the human mind is capable of observing and identifying locations of a joint from a radiograph produced after X-ray imaging a patient. The steps of “performing” convolution, pooling, dropout and processing radiograph pixel data are mental processes capable of being performed in the human mind. As discussed above, the human mind is capable of performing local feature detection, spatial abstraction, and robust generalization that mirror the functions of convolution, pooling, and dropout used in CNNs. As also described above, the human mind is capable of processing a radiograph to, as one example, identify features and/or abnormalities from a patient’s X-ray. The step of “detecting” anatomical key points is capable of being performed in the human mind. For example, the human mind is capable of processing a radiograph to, as one example, identify features and/or abnormalities from a patient’s X-ray. The step of “constructing” characteristic axes is a mental process capable of being performed in the human mind. For example, the human mind is capable of spatial orientation, where the mind can determine an up and down direction as being along the Y-axis and a left and right direction along the X-axis. The examiner notes that the step of “generating” diagnostic radiographs is not being interpreted as part of the judicial exception as it is insignificant extra-solution activity (in particular, this step is outputting data). Further, the examiner notes the applicant’s analysis of the abstract idea being carried out by the additional elements of the CNN is not appropriate at Step 2A Prong 1 as additional elements are analyzed at Step 2A Prong 2 and Step 2B.
In response to the applicant’s argument that the claims integrate the abstract idea into a practical application, the examiner respectfully disagrees. The applicant alleges the claimed invention provides an improvement to radiographic diagnostic technology. However, it is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
In response to the applicant’s argument that the claims recite significantly more than the abstract idea, the examiner respectfully disagrees. As detailed in the previous office action (and detailed below in the 35 USC 101 rejection), the additional elements merely add insignificant extra-solution activities and/or are appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
In response to the applicant’s argument that the claims represent a technological improvement in medical image processing accuracy, the examiner notes an alleged more accurate calculation/analysis is still a calculation/analysis nonetheless.
Therefore, for the reasons provided above, the 35 USC 101 rejection of claims 1-4 is maintained.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
A measurement and diagnostic system for weight-bearing lines of both lower limbs, comprising:
a data loading module, configured to load and read a data file (.mat) of keypoint position information of training sets and radiographs to be measured, and select joint image positions in each radiograph by establishing a two-dimensional coordinate system for each radiograph and extracting joint image regions including a left hip joint area, a right hip joint area, a left knee joint area, a right knee joint area, a left ankle joint area and a right ankle joint area; wherein the radiographs comprise full-length weight-bearing radiographs of human lower limbs;
wherein the eight-characteristic axis keypoints correspond to a center of a left femoral head, a center of a right femoral head, a center of a left femoral knee joint, a center of a left tibial knee joint, a center of a right femoral knee joint, a center of a right tibial knee joint, a center of a left ankle joint and a center of a right ankle joint;
a keypoint selection module, configured to classify and identify the joint image positions selected by the data loading module through a convolutional neural network by taking obtained joint images as input layers and performing convolution, pooling, dropout, and fully-connected layer operations for processing pixel data of the radiographs to determine coordinates of anatomical keypoints, to obtain coordinates of corresponding anatomical keypoints on the radiographs, comprising coordinates of eight characteristic axis keypoints and coordinates of a plurality of joint line keypoints;
wherein the joint line keypoints comprise lowest points of medial and lateral femoral condyles of distal femurs and lowest points of medial and lateral tibial plateaus of proximal tibias, and the joint orientation lines are generated by connecting the corresponding medial and lateral condyle points and tibial plateau points;
connecting the obtained coordinates of the eight keypoints to draw characteristic axes, thereby forming four characteristic axes extending along left and right lower limbs, comprising a first characteristic axis (9), a second characteristic axis (10), a third characteristic axis (11) and a fourth characteristic axis (12); and
connecting the coordinates of the joint line keypoints to draw form six joint lines, thereby forming left and right knee joint upper orientation lines (13), left and right knee joint lower orientation lines (14), and left and right ankle joint orientation lines (15), wherein the first and second characteristic axes correspond to the left and right knee joint upper orientation lines (13) respectively, and the third and fourth characteristic axes correspond to the left and right knee joint lower orientation lines (14) and the left and right ankle joint orientation lines (15) respectively, wherein the convolutional neural network comprises:
-convolution layers performing convolution using 3x3x8 kernels;
- pooling layers generating reduced-dimension feature maps;
-dropout layers applied after convolution operations; and
- a fully connected layer configured to output coordinates of the anatomical keypoints;
an angle calculation module, configured to calculate slopes among the keypoints by using the obtained keypoints, characteristic axes and joint lines, and calculate mechanical lateral distal femoral angles, joint line convergence angles, medial proximal tibial angles and lateral distal tibial angles by using inverse trigonometric functions applied to slopes derived from the keypoint coordinates; and
a mechanical axis diagnosis and output module, configured to compare the obtained angle values with preset reference thresholds of the mechanical lateral distal femoral angles, the joint line convergence angles, the medial proximal tibial angles and the lateral distal tibial angles according to the angles obtained by the angle calculation module, wherein if the obtained angle values are not within the reference thresholds, the system generates a diagnostic radiograph image displaying the characteristic axes, joint orientation lines and calculated orthopedic angles overlaid on the radiograph image, thereby indicating that the angles are abnormal, and there is possibly a lower limb deformity, and if all the angle values are within the reference thresholds, it indicates that there is no lower limb deformity; and
then, output the obtained angle values, show the plurality of characteristic axes and joint lines on the radiographs, and represent measurement results in a picture output form.
Step 1:
The examiner determines that the claim 1 is drawn to a machine.
Step 2A Prong 1:
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018).
The claimed steps of loading, reading, classifying, identifying, calculating, comparing, importing, extracting, outputting, and storing recite mental processes and mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The step of “loading and reading” a data file in independent Claim is a mental process capable of being performed in the human mind. For example, the human mind is capable of “loading” a memory and “reading” what the memory contains. The steps of “classifying and identifying” the joint image positions in independent Claim 1 are mental processes capable of being performed in the human mind. For example, the human mind is capable of looking at an image of a human body and identifying and classifying various joints of the body (i.e., differentiating between knee and elbow joints). The step of “calculating” slopes among keypoints is an example of a mathematical concept of finding the steepness and direction of a line. The step of “comparing” the obtained angle values is example of a mental process capable of being performed in the human mind. For example, the human mind is capable of comparing angles and giving insight as to whether one angle is larger/smaller than another. The step of “extracting” keypoints may be classified as both a mental process and mathematical concept. As a mental concept, the human mind is capable of extracting data from a memory. As a mathematical concept, extraction (i.e., feature extraction) is the process of transforming raw data into smaller, meaningful sets of features for analysis. The step of “storing” results is an example of a mental process capable of being performed by the human mind. For example, the human mind is capable of storing data as memories.
The claimed steps of loading, reading, classifying, identifying, calculating, comparing, extracting, and storing can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for estimation that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind,
Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind,
University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims 2-4, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1-4 is not integrated into a practical application because:
• The abstract idea amounts to simply implementing the abstract idea on a computing device. For example, the recitations regarding the generic computing components for loading, reading, classifying, identifying, calculating, comparing, extracting, and storing merely invoke a computer as a tool.
• The data-gathering step (loading) and the data-output step (outputting and generating) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
• There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computing device that is used as a tool for loading, reading, classifying, identifying, calculating, comparing, extracting, and storing.
• The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to estimate bio-information.
• The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computing device for loading, reading, classifying, identifying, calculating, comparing, extracting, and storing. The claims do not apply the obtained prediction to a particular machine. Rather, the data is merely output in a post-solution step.
Step 2B:
The additional elements are identified as follows: data loading module, keypoint selection module, angle calculation module, mechanical axis diagnosis module, output module, a convolutional neural network.
The examiner notes prior art reference Vdovjak (US 20220076078 A1) discloses the use of convolutional neural networks as a well-understood, routine, and conventional technique in radiograph processing [0005 “The known model is a feed-forward 169-layer convolutional neural network that outputs a probability of abnormality when provided with a radiograph as input”].
Those in the relevant field of art would recognize the above-identified additional elements as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer (see MPEP 2106.05(f)). It is unclear from the applicant’s specification how the general modules used are different from generic computer processing modules and thus cannot reasonably be said to improve the performance of a computer/technical field.
Thus, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Conclusion
THIS ACTION IS MADE FINAL. 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 JONATHAN M HANEY whose telephone number is (571)272-0985. The examiner can normally be reached Monday through Friday, 0730-1630 ET.
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, Alexander Valvis can be reached at (571)272-4233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN M HANEY/Examiner, Art Unit 3791
/JUSTIN XU/Primary Examiner, Art Unit 3791
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