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 .
Application Status
This office action is responsive to Application No.:18/998,381 filed on 01/24/2025 (Foreign Priority Date: 08/09/2022).
Claims 1-14 are pending and presented for examination.
This action has been made NON-FINAL.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 (Foreign Priority Date: 08/09/2022).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/09/2026; 06/05/2025; 03/06/2025; 01/24/2025 is being considered by the examiner. A signed IDS is hereby attached.
Claim Objections
Claim 12 is objected to because of the following informalities:
Claim 12 failed to include a transitional phrase.
Appropriate correction is required.
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 12 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.
Referring to dependent claim 12, the claim language failed to include technical steps to further limit claim 1. Therefore, this claim is rendered as indefinite.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of shape learning1, without significantly more.
After Subject Matter Eligibility (SME) analysis, the examiner conclude that,
Step 1:
The claimed invention is a process which falls within one of the four statutory categories of invention (process, machine, manufacture or composition of matter). The claim(s) goes on to recite(s) the following:
extracting geodesic features and geometric features from three-dimensional shape data;
and classifying an object through a convolution operation on the extracted geodesic features and the extracted geometric features with respect to faces constituting the three-dimensional shape data.
After Subject Matter Eligibility (SME) analysis, the examiner conclude that,
Step 2A Prong One:
The idea of extracting geodesic features and geometric features from three-dimensional shape data is an idea having no particular concrete or tangible form. This concept fall within one of the three grouping mention in the 2019 PEG guidance. This is directed to a mental process.
The limitation of extracting geodesic features and geometric features from three-dimensional shape data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
Nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, the claims are directed to an abstract idea. (Step 2A Prong One: Yes)
After Subject Matter Eligibility (SME) analysis, the examiner conclude that,
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites one additional elements, such as classifying an object through a convolution operation on the extracted geodesic features and the extracted geometric features with respect to faces constituting the three-dimensional shape data.
The “computer generated” in both steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply/organize the exception. (Step 2A Prong Two: No)
After Subject Matter Eligibility (SME) analysis, the examiner conclude that,
Step 2B:
The claim(s) does/do not include additional elements, taken individually and as a combination, that are sufficient to amount to significantly more than the judicial exception because the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. When examining the limitations, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. (Step 2B: No) Accordingly, the claims are directed to an abstract idea and are rejected as ineligible for patenting under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by LIAO, US 20190026917.
Claim 1:
LIAO discloses a method for shape learning performed by a shape learning system (See LIAO Abstract; Paragraphs 0046-00482), the method comprising:
extracting geodesic features and geometric features from three-dimensional shape data (See LIAO Paragraphs 0052-0053; 00733-0074);
and classifying an object through a convolution operation (See LIAO Paragraphs 0064-0067) on the extracted geodesic features and the extracted geometric features (See LIAO Paragraphs 0052-00534; 0073-0074) with respect to faces (See LIAO Paragraph 0115) constituting the three-dimensional shape data (See LIAO Abstract; Paragraphs 0046-0048; 0052-0053; 0073-0074).
Claim 2:
LIAO discloses visualizing a result of object classification (See LIAO Paragraph 0067) from the three-dimensional shape data (See LIAO Paragraphs 0046-0048) by using a convolution-based neural network model (See LIAO Paragraphs 0064-0067) for explainable three-dimensional shape learning (See LIAO Paragraphs 0046-0048).
Claim 3:
LIAO discloses wherein the convolution-based neural network model (See LIAO Paragraphs 0064-0067) for explainable three-dimensional shape learning (See LIAO Paragraphs 0046-0048) includes: a descriptor layer that extracts geodetic features (See LIAO Paragraphs 0052-0053; 0073-0074) and geometric features using a target face and adjacent faces (See LIAO Paragraph 0115) from the three-dimensional shape data (See LIAO Paragraphs 0046-0048); and a convolutional layer (See LIAO Paragraphs 0064-0067) that performs a convolution operation (See LIAO Paragraphs 0064-0067) using the extracted geodetic features and geometric features (See LIAO Paragraphs 0052-0053; 0073-0074).
Claim 4:
LIAO discloses wherein the three-dimensional shape data (See LIAO Paragraphs 0046-0048) has a structure in which nodes, edges, and faces (See LIAO Paragraph 0115) constituting the three-dimensional shape data are irregular (See LIAO Paragraphs 0046-0048).
Claim 5:
LIAO discloses wherein the extracting includes constructing, in list form (See LIAO Figure 3D; Paragraphs 0053-0054 Note: The “list form” is the same as “mapping”), identification information for a plurality of faces (See LIAO Paragraph 0115) adjacent to each face with respect to the faces (See LIAO Paragraph 0115) constituting the three-dimensional shape data (See LIAO Paragraphs 0046-0048), and creating a data structure so as to obtain information about the nodes and edges of each face through the identification information constructed in list form (See LIAO Figure 3D; Paragraphs 0053-0054 Note: The “list form” is the same as “mapping”).
Claim 6:
LIAO discloses wherein the extracting includes extracting geodesic features (See LIAO Paragraphs 0052-0053; 0073-0074) from three-dimensional shape data (See LIAO Paragraphs 0046-0048) using order-invariant kernel mapping (See LIAO Paragraphs 0053; 0100).
Claim 7:
LIAO discloses wherein the extracting includes extracting geometric features (See LIAO Paragraphs 0052-0053; 0073-0074) based on the positions and orientations of a target face and surrounding faces (See LIAO Paragraph 0115).
Claim 8:
LIAO discloses wherein the extracting includes extracting geometric features (See LIAO Paragraphs 0052-0053; 0073-0074) based on information on the distance ratios and angles (See LIAO Paragraph 0070) between the target surface and the surrounding faces (See LIAO Paragraph 0115).
Claim 9:
LIAO discloses wherein the classifying includes feeding (See LIAO Paragraph 0067), into a convolutional layer (See LIAO Paragraphs 0064-0067), a feature vector for each face (See LIAO Paragraphs 0046; 0055) that integrates the geodesic features and the geometric features (See LIAO Paragraphs 0052-0053; 0073-0074).
Claim 10:
LIAO discloses wherein the classifying (See LIAO Paragraph 0067) includes performing a one-dimensional convolution (See LIAO Paragraphs 0064-0067) by using a feature vector (See LIAO Paragraphs 0046; 0055) that inherits a particular face based on the feature vector of each face (See LIAO Paragraphs 0046; 0055; 0115) and a feature vector (See LIAO Paragraphs 0046; 0055) that aggregates the relationships between the particular face and faces adjacent to the particular face (See LIAO Paragraphs 0046; 0055; 0115).
Claim 11:
LIAO discloses wherein the extracting includes performing a GAP (global average pooling) operation (See LIAO Paragraphs 0064-0067; 0100) to obtain a score for each class (See LIAO Paragraph 0067) since the one-dimensional convolution produces (See LIAO Paragraphs 0064-0067) an output with the number of faces remaining the same (See LIAO Paragraphs 0046; 0055; 0115), and performing object classification based on the obtained score (See LIAO Paragraph 00675).
Claim 12:
LIAO discloses computer program stored in a non-transitory computer- readable recording medium in order to execute the method for shape learning according to claim 1 by a shape learning system (See LIAO Abstract; Figure 1; Paragraphs 0046-0048).
Claims 13 and 14:
Claims 13 and 14 are rejected on the same basis as claims 1 and 2.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10733797 discloses a method for extracting information about a body's skeleton from a 3D body mesh includes measuring the closest distance from every vertex of the 3-D body mesh to the centroid of the 3-D body mesh and using these measurements to compute a geodesic map of these distances. Key anchors are then extracted from the geodesic map.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEREE N BROWN whose telephone number is (571)272-4229. The examiner can normally be reached M-F 5:30-2:00 PM EST.
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, SAID BROOME can be reached at (571) 272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHEREE N BROWN/Primary Examiner, Art Unit 2612 September 1, 2026
1 NOTE: Independent claim 1 and 13 body of the claims contains absolutely no hardware/computing components.
2 LIAO Paragraphs 0046-0048 of LIAO recites “higher layers may learn to represent complex shapes in visual data or words in auditory data. Still higher layers may learn to recognize common visual objects or spoken phrases.”
3 LIAO Paragraph 0073 recites “a geometric differential module computes the geodesic distance (e.g., geodesic distance value) between the 3D model and the object. After traversing each 3D model, the geometric differential module selects the 3D model (e.g., rendered image) corresponding to a smallest geodesic distance.”
4 LIAO Paragraphs 0052-0053 recites “the DCN 300 may include a feature extraction section and a classification section.”
5 LIAO Paragraph 0067 recites “the output of the deep convolutional network 350 is a classification score 366 for the input data 352. The classification score 366 may be a set of probabilities, where each probability is the probability of the input data including a feature from a set of features.”
Prosecution Insights