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 .
Status of Claims
Claims 1-20 are pending.
Drawings
The 4-page drawings have been considered and placed on record in the file.
Specification
The specification is objected to because of the following informalities:
In paragraph 44 "Next at operation 516…" should be "Next at operation 518…"
According to 37 CFR 1.71, MPEP §§ 608.01, 2161, and 2162, the specification must be in such particularity as to enable any person skilled in the pertinent art or science to make and use the invention without involving extensive experimentation and must clearly convey enough information about the invention to show that applicant invented the subject matter that is claimed. An applicant is ordinarily permitted to use his or her own terminology, as long as it can be understood. Necessary grammatical corrections, are required. Reference characters must be properly applied, no single reference character being used for two different parts or for a given part and a modification of such part. See 37 CFR 1.84(p). Every feature specified in the claims must be illustrated, but there should be no superfluous illustrations.
A substitute specification in proper idiomatic English and in compliance with 37 CFR 1.52(a) and (b) is required. The substitute specification filed must be accompanied by a statement that it contains no new matter.
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. Such claim limitation is: “processing unit” in claims 9, 12, and 15-16.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, these are being interpreted to cover the corresponding structures described in the applicant’s specification: ¶51: “Processing unit(s), such as processing unit(s) 602, can represent, for example, a CPU type processing unit, a GPU-type processing unit, a neural processing unit, a field-programmable gate array (FPGA), another class of digital signal processor (DSP), or other hardware logic components that may, in some instances, be driven by a CPU” as performing the claimed functions, and equivalents thereof.
If applicant does not intend to have these limitations 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.
Claim 2 recite the limitation "the representation". There is insufficient antecedent basis for this limitation in the claim because it is unclear if "the representation" is the same as “input mesh of a subject” in claim 1, or “model-generated mesh” in claim 1 or if it is something different. For examination purposes, the representation is considered to be the model-generated mesh.
Claim 7 (similarly claim 8) recites the limitation "model-generated representation". There is insufficient antecedent basis for this limitation in the claim because it is unclear if "model-generated representation" is the same as “input mesh of a subject” in claim 1, or “model-generated mesh” in claim 1 or if it is something different. For examination purposes, the representation is considered to be the model-generated mesh.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9-10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Saric et al. ("Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation" - Published 2023) in view of Bustany et al. (US 12,548,276 B2).
Regarding claim 1, Saric teaches “A method comprising: receiving an input mesh of a subject (Saric page 6 paragraph 3 "Three-dimensional pose estimation determines the position of body joints in three-dimensional space. It is usually realized using a combination of 2D image information and depth information");
identifying a pose of the subject from the input mesh (Saric page 6 paragraph 3 "This method involves the use of a 3D pose estimation system for the body, face, and hands using only monocular images. It outputs in the form of the SMPL-X model [18], which combines the skinned multi-person linear (SMPL) model [19], which represents the human body as a collection of interconnected joints, and the linear blend skinning model with the face and hands model");
generating a parameterized model of the subject from the pose (Saric page 6 paragraph 3 "Pose parameters 0 are divided into 21 body parameters, 15 parameters for the left hand, and 15 parameters for the right hand. Pose parameters 0 are defined with the angle-axis notation that defines relative rotation to the parent joint");
receiving a model-generated mesh of the subject from the parameterized model (Saric page 6 paragraph 3 "a low-dimensional representation of the human body, hands, and face is obtained. The SMPL-X model is able to model shape variations and body deformations with low-dimensional shape and pose parameters");
generating an input-model vertex mapping from vertices of the model-generated mesh to corresponding vertices of the input mesh (Saric page 7 paragraph 1 "Firstly, 2D joint localization is performed on input using a pretrained model, such as OpenPose. After that, the model utilizes a deep neural network to perform regression of the SMPL-X parameters. The regressed parameter values are further refined via an iterative optimization routine, where the model is aligned with the image based on 2D keypoints. This step minimizes the mismatch between the projected 3D joints in the SMPL-X model and the 20 joint positions in the image")”.
However, Saric is not relied on to teach “receiving a region name of a region of the subject; identifying a plurality of model-generated vertices of the model-generated mesh corresponding to the region name; identifying, from the input-model vertex mapping, a plurality of input mesh vertices that correspond to the plurality of model-generated vertices; and highlighting the region of the subject by modifying how the plurality of input mesh vertices appear in a rendering of the input mesh of the subject.”
Bustany teaches “receiving a region name of a region of the subject; identifying a plurality of model-generated vertices of the model-generated mesh corresponding to the region name (Bustany column 6 lines 10-15 "The locations of the anatomically desired target treatment points are typically annotated on the template 3D mesh models an expert clinician/therapist (image shown at the right in FIG. 3). The coordinate locations of the registered points are expressed locally through the coordinate locations of the vertices of the mesh elements in which they are located");
identifying, from the input-model vertex mapping, a plurality of input mesh vertices that correspond to the plurality of model-generated vertices (Bustany column 6 lines 22-36 "rigid body transformation is used to align the pose in the template 3D mesh model with that in in the patient's individual 3D mesh model with the that. Each 3D mesh model is segmented into body parts joined by virtual joint "handles." The body parts connected by these virtual joint handles represent the rigid body parts that will be translated and rotated for the purposes of alignment. Similar to the hierarchical mesh deformation (HMD) model described in Zhu et. al.: Detailed human shape estimation from a single image by hierarchical mesh deformation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019), joints are selected for the head crown, neck, waist, left/right shoulders, left/right elbows, left/right wrists, left/right finger joints, left/right hip joints, left/right knees, left/right ankles, and left/right toes"); and
highlighting the region of the subject by modifying how the plurality of input mesh vertices (Bustany column 8 lines 21-24 "The application program can be further configured to allow the clinician to visualize the target treatment points directly on the patient's custom 3D mesh model on the device display, e.g., using Unity Pro") appear in a rendering of the input mesh of the subject (Bustany column 8 lines 9-14 "The non-rigid body motion matrix transforms is determined by solving the respective optimization problem as that prescribed in the deformed template 3D mesh model as the patient's custom 3D model annotated with the target treatment points (image shown at the right in FIG. 7)").“
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine a method for generating a 3d parameterized mesh model of a person as taught by Saric to include identification of specific body parts within the model as taught by Bustany.
The suggestion/motivation for doing so would have been that there is a need in
the field of medical imaging to accurately determine location for treatment, "In addition to displaying the marked images to therapists for real time positioning during therapy, the methods and aids of the present invention may be used for augmented reality visualization of these treatment locations onto the patient's body thereby to further aid a clinician in accurately treating these target locations" as noted by Bustany disclosure in column 1 lines 62-52. Accurately visualizing treatment location also assists patients in better understanding their treatment plan.
Therefore, it would have been obvious to combine the disclosure of Saric with
the Bustany disclosure to obtain the invention as specified in claim 1 as there is a
reasonable expectation of success and/or because doing so merely combines prior art
elements according to known methods to yield predictable results.
Regarding claim 2, the combination of Saric and Bustany teaches “The method of claim 1, wherein the representation comprises a real-time 3D representation of the subject (Saric Figure 4 and page 7 paragraph 2 "The final output of the described model is formed by combining outputs from the face, hands, and body modules into the SMPL-X representation").”
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Saric Figure 4
Regarding claim 3, the combination of Saric and Bustany teaches “The method of claim 1, wherein the subject comprises a medical patient (Saric Figure 4 and page 2 paragraph 1 "A 3D human avatar can be a valuable tool in telemedicine because it allows healthcare providers to remotely interact with patients in a more immersive and personalized way") and the region name is identified by a medial professional (Saric page 2 paragraph 1 "Annotations of 3D models (human avatars) in extended reality (XR) can provide a valuable tool for telemedicine. In XR, healthcare professionals can use virtual annotations to highlight specific areas of a patient's anatomy or medical imaging, which can be viewed and manipulated in real-time").”
Regarding claim 4, the combination of Saric and Bustany teaches “The method of claim 1, wherein the pose is defined by joint positions inferred from the input mesh of the subject (Saric page 6 paragraph 3 "Three-dimensional pose estimation determines the position of body joints in three-dimensional space. It is usually realized using a combination of 2D image information and depth information").”
Regarding claim 5, the combination of Saric and Bustany teaches “The method of claim 4, wherein the parameterized model represents the subject with a plurality of body parts connected by a plurality of joints, and wherein the parameterized model is parameterized with the joint positions and joint angles of the pose (Saric page 6 paragraph 3 "This method involves the use of a 3D pose estimation system for the body, face, and hands using only monocular images. It outputs in the form of the SMPL-X model [18], which combines the skinned multi-person linear (SMPL) model [19], which represents the human body as a collection of interconnected joints, and the linear blend skinning model with the face and hands model").”
Regarding claim 6, the combination of Saric and Bustany teaches “The method of claim 1, further comprising: moving one or more vertices of the model-generated mesh of the subject to align with one or more corresponding vertices of the input mesh of the subject (Saric page 7 paragraph 1 "Firstly, 2D joint localization is performed on input using a pretrained model, such as OpenPose. After that, the model utilizes a deep neural network to perform regression of the SMPL-X parameters. The regressed parameter values are further refined via an iterative optimization routine, where the model is aligned with the image based on 2D keypoints. This step minimizes the mismatch between the projected 3D joints in the SMPL-X model and the 20 joint positions in the image").”
Regarding claim 7, the combination of Saric and Bustany teaches “The method of claim 6, wherein the one or more vertices of the model-generated representation are moved independently of each other (Bustany column 7 lines 41-50 "Translation invariant representations are used for the mesh element transformations to account for rigid body motion and non-rigid body deformations. Let mesh element sk contain the points xk, 1, xk,2, ... , xk,q, k=l, ... , nm (e.g. q=3 for a triangular mesh element). These transformations/deformations are obtained by translating point xk,1 to the global origin in the mesh element's local coordinate system, obtained. Such transformations/deformations will be applied to the mesh element's edges, ek, j=x k, j-x k, 1, j=2, ... 'q").”
The proposed combination as well as the motivation for combining Saric and Bustany references presented in the rejection of claim 1, applies to claim 7. Finally the method recited in claim 7 is met by Saric and Bustany.
Claim 9 recites a system with elements corresponding to the steps of the method
recited in claim 1. Therefore, the recited elements of this claim are mapped to the
proposed combination in the same manner as the corresponding steps of method
claim 1. Additionally, the rationale and motivation to combine the Saric and Bustany
references, presented in rejection of claim 1 apply to this claim. Finally, the combination of Saric and Bustany teaches “a processing unit; and a non-transitory computer-readable storage medium having computer-executable instructions stored thereupon, which, when executed by the processing unit, cause the processing unit (Saric page 8 paragraph 2 "Frame grabbing, person detection, person identification, and 3D pose estimation services were run on the following server configurations: AMO CPU Ryzen 9 5900X, RAM 3200 Mhz 4 x 16 GB, GPU NVIDIA RTX A5000 24 GB, and SAMSUNG SSD 980 PRO 1 TB M.2.")”.
Regarding claim 10, the combination of Saric and Bustany teaches “The system of claim 9, wherein the input-model generated vertex mapping associates a vertex of the model-generated mesh at a location of the medical patient with a vertex of the input mesh that is closest to the location of the medical patient (Bustany column 6 lines 22-36 "Rigid body transformation is used to align the pose in the template 3D mesh model with that in in the patient's individual 3D mesh model with the that").”
The proposed combination as well as the motivation for combining Saric and Bustany references presented in the rejection of claim 1, applies to claim 10. Finally the system recited in claim 10 is met by Saric and Bustany.
Claim 16 recites a computer readable medium including computer executable instructions corresponding to the steps of the method recited in claim 1. Therefore, the recited instructions of the computer readable medium of claim 16 are mapped to the proposed combination in the same manner as the corresponding steps of the method claim 1. Additionally, the rationale and motivation to combine Saric and Bustany presented in rejection of claim 1, apply to this claim.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Saric and Bustany, in view of Bozic et al. ("Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction" - Published 2020).
Regarding claim 8, the combination of Saric and Bustany teaches the method of claim 7. However, the combination of Saric and Bustany is not relied on to teach “wherein the one or more vertices of the model-generated representation are moved according to a multi-layer perceptron trained to conform a pair of mesh representations.”
Bozic teaches “wherein the one or more vertices of the model-generated representation are moved according to a multi-layer perceptron trained to conform a pair of mesh representations (Bozic page 4 left hand column paragraph 1 "Using the predicted graph node positions and orientations, we learn implicit functions to represent the shape of each graph node and, thus, the entire shape of the object. The implicit functions are represented as a multi-layer perceptron (MLP). These MLPs take a 3D point centered around the node position as input and predict its signed distance value, defining the local geometry around the node. Warping all node MLPs to every time step and interpolating their local part reconstruction results in an accurate implicit deformable shape reconstruction without the need for an explicit canonical pose. In addition to the sample point locations, the MLPs are conditioned on the predicted graph positions, which enables reconstruction of pose-dependent geometry detail").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine a method for generating a 3d parameterized mesh model of a person and identification of body part as taught by Saric and Bustany to include a mulit-layer perceptron for moving vertices as taught by Bozic.
The suggestion/motivation for doing so would have been "Since there exists no general canonical pose (like a T pose of a human [26]) that fits all deformable objects, we avoid modeling it explicitly. Instead, we propose to employ a set of implicit functions that are centered around the deformation graph nodes. Specifically, we model local signed distance functions (SDFs) using multi-layer perceptrons (MLPs) that can be deformed to fit any frame, without requiring an explicit canonical pose" as noted by Bozic disclosure in page 2 left hand column paragraph 2.
Therefore, it would have been obvious to combine the disclosure of Saric and Bustany with the Bozic disclosure to obtain the invention as specified in claim 8 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Saric and Bustany, in view of Somech et al. (US 2024/0311563 A1).
Regarding claim 11, the combination of Saric and Bustany teaches the system of claim 9. However, the combination of Saric and Bustany is not relied on to teach “wherein the region name is identified by extracting a noun phrase from text or speech produced by the medical practitioner, computing an embedding vector of the noun phrase, and determining that the embedding vector is within a defined distance of an embedding vector representation of the region of the medical patient.”
Somech teaches “wherein the region name is identified by extracting a noun phrase from text or speech (Somech paragraph [0030] "processor executing the corpus assembler 156 detects natural language via a speech-to-text service. For example, an activated microphone at a user device can pick up or capture near-real time utterances of a user and the user device may transmit, over the network(s) 110, the speech data to a speech-to-text service that encodes or converts the audio speech to text data (the corpus of text) using natural language processing") produced by the medical practitioner (Somech paragraph [0123] "Named Entity Recognition (NER) (also referred to as entity extraction). NER is an information extraction technique in NLP that identifies and classifies elements or "entities" (e.g., nouns) in natural language text into predefined categories. Such predefined categories may be indicated in corresponding tags or labels. Entities can be, for example, names of people, specific organizations, specific locations, specific times, specific quantities, specific monetary price values, specific percentages, specific pages, and the like"), computing an embedding vector of the noun phrase (Somech paragraph [0057] "Some embodiments alternatively or additionally locate semantically related terms to a query based on user word embedding models, such as WORD2VEC, or GloVE"), and determining that the embedding vector is within a defined distance of an embedding vector representation of the region of the medical patient (Somech paragraph 100 "learn an embedding of feature vectors based on learning (for example, deep learning) to detect similar features between training data input(s) 415 in feature space using distance measures, such as cosine ( or Euclidian) distance").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine a system for generating a 3d parameterized mesh model of a person and identification of body part as taught by Saric and Bustany to include a system to extract phrases from received information to determine which area is being referred to as taught by Somech.
The suggestion/motivation for doing so would have been "Existing NLP-based technologies are incomplete or inaccurate. One reason is because existing language models, such as Large Language Models (LLM), often make predictions without adequate contextual data. For example, when a user asks a question or otherwise provides a query to a model, and the model does not know the context of the question, it provides an answer that is inaccurate or may not provide an answer at all" as noted by Somech disclosure in paragraph 18.
Therefore, it would have been obvious to combine the disclosure of Saric and Bustany with the Somech disclosure to obtain the invention as specified in claim 11 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding claim 12, the combination of Saric, Bustany, and Somech teaches ”The system of claim 11, wherein the computer-executable instructions further cause the processing unit to: reproduce the text or speech received from the medical practitioner with a local display that is observable by the medical patient (Somech paragraph [0105] "FIG. 5 is a screenshot 500 of an example user interface for returning search results of a query based on reading a meeting transcript 502 that includes various corpus data supplements, according to some embodiments. The screenshot 500 ( or web/app page) includes a corpus of text, which is a meeting transcript 502").”
The proposed combination as well as the motivation for combining Saric, Bustany, and Somech references presented in the rejection of claim 11, applies to claim 12. Finally the system recited in claim 12 is met by Saric, Bustany, and Somech.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Saric, Bustany, and Somech, in view of Engelke et al. (US 2021/0266473 A1).
Regarding claim 13, the combination of Saric, Bustany, and Somech teaches the system of claim 12. However, the combination of Saric, Bustany, and Somech is not relied on to teach “wherein the text or speech received from the medical practitioner is processed by a language simplification engine to make the text or speech more understandable to the medical patient.”
Engelke teaches “wherein the text or speech received from the medical
practitioner is processed by a language simplification engine to make the text or speech more understandable to the medical patient (Engelke paragraph [0118] "For instance, in at least some cases the language simplification module and filler work removal module may use an IC associated with an utterance to assess the meanings of specific words within the utterance and may then identify simplified words or phrases for portions of the verbatim text and swap the simplified words into the verbatim text and also may remove filler words prior to presenting to the AU").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine a system for generating a 3d parameterized mesh model of a person and identification of body part based on received information as taught by Saric, Bustany, and Somech to include a language simplification model as taught by Engelke.
The suggestion/motivation for doing so would have been " Second, the second person has to understand the meaning of each separate word received either in text or in sound. Thus, if the first person uses one or more words that the second person is unfamiliar with, the second person will be unable to perceive the first person's thought even if the second person correctly hears all the words uttered by the first person" as noted by Engelke disclosure in paragraph 5. This is important in medical settings as a misunderstanding of communication can lead to anxiety and fear during medical procedures for a patient.
Therefore, it would have been obvious to combine the disclosure of Saric, Bustany, and Somech with the Engelke disclosure to obtain the invention as specified in claim 13 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Saric and Bustany, in view of Chae et al. (US 2017/0086791 A1).
Regarding claim 14, the combination of Saric and Bustany teaches the system of claim 9. However, the combination of Saric and Bustany is not relied on to teach “wherein the region is highlighted by changing a color of the plurality of input mesh vertices displayed by a local display.”
Chae teaches “wherein the region is highlighted by changing a color of the plurality of input mesh vertices displayed by a local display (Chae paragraph [0082] "referring to FIG. 6A, the 3D model which coincides with a body proportion may be displayed in a translucent form, and the area-of-interest may be displayed to be clearly distinguished from others in a visual manner by using a different color of the area-of interest. Alternatively, the area-of-interest may be displayed on the 3D model by changing a color, or a human body may be displayed by using an outline, and an area to be examined and the area-of-interest may be displayed in different colors").”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention of the instant application to combine a system for generating a 3d parameterized mesh model of a person and identification of body part based on received information as taught by Saric and Bustany with color coordinating regions of interest as taught by Chae because such features is the result of applying known techniques to a known device ready for improvement to yield predictable results. More specifically, color coordinating region of interests allows those viewing an image to easily “area-of-interest may be displayed to be clearly distinguished from others” (Chae paragraph 82). Therefore, it would have been recognized that modifying system for generating a 3d parameterized mesh model of a person and identification of body part based on received information to include all the features of a changing colors of region of interest for displaying would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the reference applied shows the ability to incorporate a 3d mesh model and changing colors of a region of interest and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Therefore, it would have been obvious to combine the disclosure of Saric and Bustany with the Chae disclosure to obtain the invention as specified in claim 14 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Saric and Bustany, in view of Jorasch et al. (US 11,128,636 B1).
Regarding claim 15, the combination of Saric and Bustany teaches the system of claim 9. However, the combination of Saric and Bustany is not relied on to teach “analyze facial expression and body language of the medical patient to detect that the medical patient is confused; and alert the medical practitioner that the medical patient is confused.”
Jorasch teaches “analyze facial expression and body language of the medical patient to detect that the medical patient is confused; and alert the medical practitioner that the medical patient is confused (Jorasch column 328 lines 4-11 "Having a camera on the headset could allow the user and central control AI system to collect and interpret facial visual information for feedback to the user and others. If the user looks confused, the facial expressions are interpreted by the central AI controller and the meeting owner alerted to help address the confusion").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine a system for generating a 3d parameterized mesh model of a person and identification of body part based on received information as taught by Saric and Bustany to include a facial expression recognition and alerting parties that am individual is confused as taught by Jorasch.
The suggestion/motivation for doing so would have been "The meaning of words, signs and symbols might be different from one person to another and the same word might have hundreds of meanings. Users of the headsets, when indicating confusion, could get a different representation to the comments" as noted by Jorasch disclosure in column 292 lines 34-38. This is important in medical settings as a misunderstanding of communication can lead to anxiety and fear during medical procedures for a patient.
Therefore, it would have been obvious to combine the disclosure of Sarica and Bustany with the Jorasch disclosure to obtain the invention as specified in claim 15 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Saric and Bustany, in view of Tripathi et al. ("SMPL made simple FAQs" - Published 2025).
Regarding claim 17, the combination of Saric and Bustany teaches the computer-readable storage medium of claim 16. However, the combination of Saric and Bustany is not relied on to teach “he plurality of model generated vertices that correspond to the region name are obtained from a predefined region-vertex mapping of parameterized model that maps regions to sets of vertices.”
Tripathi teaches “the plurality of model generated vertices that correspond to the region name are obtained from a predefined region-vertex mapping of parameterized model that maps regions to sets of vertices (Tripathi page 23 Figure and "The following is the mapping between SMPL joint ids to joint names").”
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Tripathi page 23
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention of the instant application to combine a system for generating a 3d parameterized mesh model of a person and identification of body part as taught by Saric and Bustany to include predefined vertices mapping as taught by Tripathi, because such features is the result of applying known techniques to a known device ready for improvement to yield predictable results. More specifically, predefined vertices mapping allows the generation of a model with accurate spatial information of regions. Therefore, it would have been recognized that modifying a system for generating a 3d parameterized mesh model of a person and identification of body part to include all the features of a predefined vertices mapping would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the reference applied shows the ability to incorporate system for generating a 3d parameterized mesh model of a person and identification of body part and predefined vertices mapping and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Therefore, it would have been obvious to combine the disclosure of Saric and Bustany with the Tripathi disclosure to obtain the invention as specified in claim 17 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding claim 18, the combination of Saric, Bustany, and Tripathi teaches “the plurality of model generated vertices that correspond to the region name are obtained from a predefined region-vertex mapping of parameterized model that maps regions to sets of vertices (Tripathi page 23 Figure and "The following is the mapping between SMPL joint ids to joint names").”
The proposed combination as well as the motivation for combining Saric, Bustany, and Tripathi references presented in the rejection of claim 17, applies to claim 18. Finally, the computer-readable storage medium recited in claim 18 is met by Saric, Bustany, and Tripathi.
Regarding claim 19, the combination of Saric, Bustany, and Tripathi teaches “the plurality of model generated vertices that correspond to the region name are obtained from a predefined region-vertex mapping of parameterized model that maps regions to sets of vertices (Tripathi page 23 Figure and "The following is the mapping between SMPL joint ids to joint names").”
The proposed combination as well as the motivation for combining Saric, Bustany, and Tripathi references presented in the rejection of claim 17, applies to claim 19. Finally, the computer-readable storage medium recited in claim 19 is met by Saric, Bustany, and Tripathi
Regarding claim 20, the combination of Saric, Bustany, and Tripathi teaches “the plurality of model generated vertices that correspond to the region name are obtained from a predefined region-vertex mapping of parameterized model that maps regions to sets of vertices (Tripathi page 23 Figure and "The following is the mapping between SMPL joint ids to joint names").”
The proposed combination as well as the motivation for combining Saric, Bustany, and Tripathi references presented in the rejection of claim 17, applies to claim 20. Finally, the computer-readable storage medium recited in claim 20 is met by Saric, Bustany, and Tripathi
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Publication 20200126297 A1 to Tian et al. discloses a method for generating a parameterized 3d mesh model of a person, which includes information of body shape, pose, and regions of interest for improved mapping of acupuncture points.
Conclusion
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/JASPREET KAUR/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
