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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Regarding Claim 1 – The claim recites “applying a fusion module learned to fuse data...” See MPEP 2181. The claim limitation uses the term fusion module. The “fusion module” is modified by functional language “learned to fuse data….” The fusion module is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0089] describes applying an algorithm to fuse the data and where the algorithm is under the control of a processor. For examination purposes the fusion module is construed to be an algorithm controlled by hardware, such as a processor.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites the limitation "the specific frame" in the second and third lines of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes this is construed to be “a specific frame.” Appropriate correction is required.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-8 are drawn to a device, which is within the four statutory categories (i.e. apparatus). Claims 9-15 are drawn to a method, which is within the four statutory categories (i.e. process).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 9) recites:
A device comprising:
a memory configured to store at least one process for recognizing a surgical stage based on visual multiple modality; and
a processor configured to perform an operation for recognizing the surgical stage as the process is executed,
wherein the processor is configured to:
extract a plurality of visual kinematics-based indices based on a surgical image including a plurality of frames corresponding to a plurality of surgical stages,
obtain first feature data for the surgical image, and obtain second feature data for the plurality of visual kinematics-based indices,
obtain third feature data by applying a fusion module learned to fuse data to the first feature data and the second feature data, and
train a first artificial intelligence (AI) model to recognize each of the plurality of surgical stages based on the third feature data.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., follow rules or instructions), but for the recitation of generic computer components. For example, but for the memory, processor, fusion module, AI model, the limitations in the context of this claim encompass a following rules related to surgical procedures to determine the surgical stage based on the featured surgical image data. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-8 and 10-15 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 9) recites:
A device comprising:
a memory configured to store (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) at least one process for recognizing a surgical stage based on visual multiple modality; and
a processor configured to (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))perform an operation for recognizing the surgical stage as the process is executed,
wherein the processor is configured to: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
extract a plurality of visual kinematics-based indices based on a surgical image including a plurality of frames corresponding to a plurality of surgical stages,
obtain first feature data for the surgical image, and obtain second feature data for the plurality of visual kinematics-based indices,
obtain third feature data by applying a fusion module (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) learned to fuse data to the first feature data and the second feature data, and
train a first artificial intelligence (AI) model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) to recognize each of the plurality of surgical stages based on the third feature data.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the memory, processor, fusion module, AI model, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0050], [0057]-[0058], [0079], [0089], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-8, 10-15 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-8, 10-15 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the memory, processor, fusion module, AI model, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0050], [0057]-[0058], [0079], [0089]); using the memory, processor, fusion module, AI model, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination 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. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GRAMMATIKOPOULOU (WO 2022/195305) “Gramma”.
Regarding claim 1, Gramma discloses a device comprising:
a memory configured to store at least one process for recognizing a surgical stage based on visual multiple modality; and ([0019] According to another aspect, a computer program product includes a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a method)
a processor configured to perform an operation for recognizing the surgical stage as the process is executed, wherein the processor is configured to: ([0019] a computer program product includes a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a method… identifying a proposed region of interest in an image from a video of a surgical procedure, synthesizing an image adjustment, by the one or more machine learning models, based on the proposed region of interest and the image, and generating a modified visualization of the surgical procedure [0046] The surgical data provided to train the machine-learning models can include data captured during a surgical procedure, as well as simulated data. The surgical data can include time-varying image data (e.g., a simulated/real video stream from different types of cameras) corresponding to a surgical environment. The surgical data can also include other types of data streams, such as audio, radio frequency identifier (RFID), text, robotic sensors, other signals, etc. The machine-learning models are trained to predict and identify, in the surgical data, “structures” including particular tools, anatomic objects, actions being performed in the simulated/real surgical stages. [0050] the structures can be used to identify a stage within a workflow (e.g., as represented via a surgical data structure), predict a future stage within a workflow, etc.)
extract a plurality of visual kinematics-based indices based on a surgical image including a plurality of frames corresponding to a plurality of surgical stages, ([0053] data can include image-segmentation data that identifies and/or characterizes one or more objects (e.g., tools, anatomical objects, etc.) that are depicted in the image or video… the characterization can indicate a set of pixels that correspond to the object and/or a state of the object resulting from a past or current user handling [0084] track one or more surgical instruments at least partially depicted in one or more images 302 of a video stream from the detection input 426, e.g., from input window 320. The structure 436 may be defined with respect to identifying one or more surgical instruments being present along with position, orientation, and/or movement. )
obtain first feature data for the surgical image, and obtain second feature data for the plurality of visual kinematics-based indices, ([0010] In one or more examples, the first machine-learning model can be trained based on a training dataset of a plurality of temporally aligned annotated data streams including temporal annotations, spatial annotations, and sensor annotations)
obtain third feature data by applying a fusion module learned to fuse data to the first feature data and the second feature data, and ([0011] In one or more examples, the computer-implemented method can include performing feature fusion to combine one or more task-specific features of the surgical procedure with one or more temporally aligned features spanning two or more frames [0087] Temporal information that is provided by phase information can be used to refine confidence of the anatomy prediction in one or more aspects. In one or more aspects, the temporal information can be fused with a feature space, and the resulting fused information can be used by a decoder to output anatomical localization)
train a first artificial intelligence (AI) model to recognize each of the plurality of surgical stages based on the third feature data. ([0084] The phase 434 can identify a phase of a surgical procedure based on the detection input 426 and relationships learned over a training period. In one or more aspects, the machine-learning is further enhanced by establishing relationships between the feature spaces 404, 405, 414. For example, phase and structural detection features in the feature space 432 can be fused with temporal features from feature space 404 and image-based features from feature spaces 405 and 414. In some aspects, computer vision models can be used to label data in the input data 420 and/or detection input 426. The proposed region of interest 440 can be identified as contours or heatmaps in a current field of view that is likely to be of interest for the current surgical phase, objectives, structures, and instrument position)
Regarding claim 2, Gramma discloses the device according to claim 1, wherein the processor is configured to: when extracting the plurality of visual kinematics-based indices, obtain semantic segmentation mask data by inputting the surgical image including the plurality of frames into a second Al model learned to perform a semantic segmentation algorithm, and extract the plurality of visual kinematics-based indices from semantic segmentation mask data corresponding to one or more surgical instruments included in the surgical image among the semantic segmentation mask data. ([0145] segmentation models directly estimate the probability of each pixel to belong to a specific instrument type by relying on fine-grained pixel-wise segmentation mask annotations. While masks solve the aforementioned technical challenge faced by bounding boxes, the annotation cost significantly grows up to almost two orders of magnitude for annotating masks with respect to only annotating frame-level labels or bounding boxes. In practice, the annotation of datasets with masks at scale can be unfeasible, which can prevent models from achieving the generalization and robustness required to be applied in real-world applications [0146] use a multi-task machine learning model (“model”) that jointly learns to estimate bounding boxes and masks for surgical instruments. The model aggregates information from the multiple tasks by using a shared backbone as an encoder, while having a head for each individual task: instrument classification, bounding box regression and segmentation. While the classification and regression heads allow the model to localize and classify surgical instruments using scalable annotations, the segmentation head achieves the detailed pixel-wise annotations. To alleviate the burden of expensive pixel-wise annotation on large datasets, one or more aspects of technical solutions described herein use a training framework that accounts for missing masks and uses weakly-supervised loss computed on frame-level labels, which can be freely obtained from the bounding box annotations).
Regarding claim 3, Gramma discloses the device according to claim 2, wherein the plurality of visual kinematics- based indices includes movement and interrelationship information of the one or more surgical instruments. ([0053] data can include image-segmentation data that identifies and/or characterizes one or more objects (e.g., tools, anatomical objects, etc.) that are depicted in the image or video… the characterization can indicate a set of pixels that correspond to the object and/or a state of the object resulting from a past or current user handling [0084] track one or more surgical instruments at least partially depicted in one or more images 302 of a video stream from the detection input 426, e.g., from input window 320. The structure 436 may be defined with respect to identifying one or more surgical instruments being present along with position, orientation, and/or movement.)
Regarding claim 4, Gramma discloses the device according to claim 3, wherein the processor is configured to: when obtaining the first feature data and the second feature data, obtain the first feature data and the second feature data by inputting each of the surgical image and the plurality of visual kinematics-based indices into a third Al model, and wherein the third Al model includes at least one of a transformer, a convolutional neural network (CNN) model, and a long short term memory (LSTM) model. ([0085] Training of the machine-learning model 400 in combination with the detection model 430 can include using computer vision modeling in combination with one or more artificial neural networks, such as encoders, Recurrent Neural Networks (RNN, e.g. LSTM, GRU, etc.), CNNs, Temporal Convolutional Neural Networks (TCNs), decoders, Transformers, other deep neural networks, etc. For example, an encoder can be trained using weak labels (such as lines, ellipses, local heatmaps or rectangles) or full labels (segmentation masks, heatmaps) to predict (i.e., detect and identify) features in surgical data. In some cases, full labels can be automatically generated from weak labels by using trained machine-learning models). Encoders can be implemented using architectures, such as ResNet, VGG, or other such neural network architectures. During training, encoders can be trained using input windows 320 that includes images 302 that are annotated with the labels (weak or full)).
Regarding claim 5, Gramma discloses the device according to claim 1, wherein the processor is configured to: when obtaining the third feature data, concatenate the first feature data and the second feature data, and obtain the third feature data by applying the fusion module to the concatenated first feature data and the second feature data, and wherein the fusion module includes a multi-layer perceptron-based fusion module. ([0084] The phase 434 can identify a phase of a surgical procedure based on the detection input 426 and relationships learned over a training period. In one or more aspects, the machine-learning is further enhanced by establishing relationships between the feature spaces 404, 405, 414. For example, phase and structural detection features in the feature space 432 can be fused with temporal features from feature space 404 and image-based features from feature spaces 405 and 414. In some aspects, computer vision models can be used to label data in the input data 420 and/or detection input 426. The proposed region of interest 440 can be identified as contours or heatmaps in a current field of view that is likely to be of interest for the current surgical phase, objectives, structures, and instrument position [0085] Training of the machine-learning model 400 in combination with the detection model 430 can include using computer vision modeling in combination with one or more artificial neural networks, such as encoders, Recurrent Neural Networks (RNN, e.g. LSTM, GRU, etc.), CNNs, Temporal Convolutional Neural Networks (TCNs), decoders, Transformers, other deep neural networks, etc. For example, an encoder can be trained using weak labels (such as lines, ellipses, local heatmaps or rectangles) or full labels (segmentation masks, heatmaps) to predict (i.e., detect and identify) features in surgical data [0088] Feature fusion 408 can be based on transform-domain image fusion algorithms to implement an image fusion neural network (IFNN). For example, an initial number of layers in the IFNN extract salient features from the temporal information output by the first model and the feature space).
Regarding claim 8, Gramma discloses the device according to claim 1, wherein the first model learned based on the third feature data outputs information for the surgical stage indicated by the specific frame based on the specific frame of another surgical image being input by the device. ([0084] The phase 434 can identify a phase of a surgical procedure based on the detection input 426 and relationships learned over a training period. In one or more aspects, the machine-learning is further enhanced by establishing relationships between the feature spaces 404, 405, 414. For example, phase and structural detection features in the feature space 432 can be fused with temporal features from feature space 404 and image-based features from feature spaces 405 and 414. In some aspects, computer vision models can be used to label data in the input data 420 and/or detection input 426. The proposed region of interest 440 can be identified as contours or heatmaps in a current field of view that is likely to be of interest for the current surgical phase, objectives, structures, and instrument position).
Regarding claim 9, the claim recites substantially similar limitations as those recited in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 10, the claim recites substantially similar limitations as those recited in the rejection of claim 2, and, as such, is rejected for similar reasons as given above.
Regarding claim 11, the claim recites substantially similar limitations as those recited in the rejection of claim 3, and, as such, is rejected for similar reasons as given above.
Regarding claim 12, the claim recites substantially similar limitations as those recited in the rejection of claim 4, and, as such, is rejected for similar reasons as given above.
Regarding claim 13, the claim recites substantially similar limitations as those recited in the rejection of claim 5, and, as such, is rejected for similar reasons as given above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gramma in view of Sommerlade et al. (US 2022/0044071).
Regarding claim 6, Gramma discloses the device according to claim 1, but does not appear to disclose the following, however, Sommerlade teaches it is old and well known in the art of data processing wherein the fusion module is configured to: obtain enhanced data for enhancing an interaction between the first feature data and the second feature data by applying a stop-gradient algorithm to the first feature data and the second feature data, and obtain the third feature data by performing a convolution operation on the enhanced data. (Sommerlade [0023] where I is the input image 34 and v.sub.1 is the variation of training images. I′ and I.sub.g′ are the reconstructed image 38 and the first synthetic image 52. f is the real image features 36 and e is embedding vectors. sg represents the stop-gradient operator that is defined as an identity at the forward computation time and has zero partial derivatives).
Therefore, it would have been obvious to one of ordinary skill in the art of data processing, before the effective filing date of the claimed invention, to modify Gramma, to incorporate wherein the fusion module is configured to: obtain enhanced data for enhancing an interaction between the first feature data and the second feature data by applying a stop-gradient algorithm to the first feature data and the second feature data, and obtain the third feature data by performing a convolution operation on the enhanced data, as taught by Sommerlade, in order to optimize the first and second data features. See Sommerlade [0023].
Regarding claim 14, the claim recites substantially similar limitations as those recited in the rejection of claim 6, and, as such, is rejected for similar reasons as given above.
Claim 7, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gramma in view of Wolf et al. (US 2021/0307840).
Regarding claim 7, Gramma discloses the device according to claim 1, and tracking the surgical instrument path and movement patterns of the instrument related to the visual kinematics indices (see [0084]). Gramma does not appear to disclose the following, however, Wolf teaches it is old and well known in the art of healthcare data processing wherein the processor is configured to: calculates a surgical skill score of a user of the at least one surgical instrument based on a path and a movement pattern of the at least one surgical instrument related to the plurality of visual kinematics-based indices. ([0156] each surgical phase of a particular video footage may be associated with more than one event… skill level of a medical care giver involved in the event, etc.), time associated with the event (such as start time, end time, etc.), type of the event, information related to medical instruments involved in the event, information related to anatomical structures involved in the event, information related to medical outcome associated with the event, one or more amounts (such as an amount of leak, amount of medication, amount of fluids, etc.), one or more dimensions (such as dimensions of anatomical structures, dimensions of incision, etc.) [0157] In some embodiments, the stored data may characterize a surgical plane corresponding to the location of the interaction. Surgical planes corresponding to an interaction may be characterized by identified medical instruments, anatomical structures, interactions between medical instruments and anatomical structures, a measurement of force applied to a surgical instrument, a location within an anatomical structure, an interaction between adjacent tissues, a location between two organs, a curved area of an anatomical structure, or any other data characterizing an interface between anatomical structures or a predefined operating plane).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Gramma, to incorporate calculating a surgical skill score of a user of the at least one surgical instrument based on a path and a movement pattern of the at least one surgical instrument related to the plurality of visual kinematics-based indices, as taught by Wolf, in order to view surgical procedure video and analyze related statistical data about the medical professionals to facilitate future scheduling and patient-data collection. See Wolf [0003]-[0004].
Regarding claim 15, the claim recites substantially similar limitations as those recited in the rejection of claim 7, and, as such, is rejected for similar reasons as given above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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