DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission of 08/03/2024. Claims 1-11 are currently pending and examined below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
2. The drawings filed on 08/03/2024 have been accepted and considered by the Examiner.
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.
3. Claims 1-11 are rejected under 35 U.S.C. 101 as being nothing more than an abstract idea. As an example, regarding claim 1, the limitations of converting sign language into spoken words can be accomplished by a human being using their mind and at most pen/paper. Hence, all these steps fall under the category of mental processes. These steps are drafted at a high level of generality without tying it to a specific technological improvement and the computing device recited herein can be a general-purpose computing device. Accordingly, this claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the
recitation of a software system, software tools, software frameworks, software libraries, software web technologies, camera device, computing device and/or a general-purpose machine learning model merely read to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using the specification. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to generate, extract, determine, and generate, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is therefore not patent eligible.
Claims 2-11, merely provide certain details of the mental processes outlined above, such as data manipulation, training a machine learning model, labeling images, calculation of loss functions, calculations of performance metrics, calculation of hyperparameter optimization, camera calibration etc. These are all steps which themselves can also be accomplished by a human being with at most the aid of a pen/paper and hence also do not amount to significantly more than the judicial exception.
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.
4. 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. These claims are: claims 1-11, with the following limitations: a data collection module, a pre-processing module, a training module, a testing module, a hyperparameter tuning module and a deployment module.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. These claims use the generic term “module” without reciting structural details in the claim language. Wording that recites a function but does not disclose sufficient structure may be treated as invoking 112(f) despite not using the words “means for.”
If applicant does not intend to have this/these limitation(s) 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. The Examiner would like to reiterate that this is in fact an interpretation and not a rejection.
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.
5. Regarding claims 2-9 and 11, the phrases "such as”, “may” and “like” render the claims indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 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.
6. Claims 1, 5, 9 and 11 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Rahmani (U.S. Patent Application Publication # 2023/0085161 A1).
With regards to claim 1, Rahmani teaches a software system for converting sign language gestures into spoken words in real-time, comprising a data collection module, a pre-processing module, a machine learning model, a training module, a testing module, a hyperparameter tuning module, and a deployment module (Para 17, teaches a method of automatic translation between sign language and spoken language. Para 55, teaches techniques for capturing and labeling images from video or data collection. Para 40, teaches pre-processing. Para 34, teaches machine learning models. Para 35, teaches training of said models. Para 39, teaches testing of said models and hyperparameter tuning. Para 41, teaches deployment).
With regards to claim 5, Rahmani teaches the software system of claim 1, wherein the training module uses a loss function, such as cross- entropy loss, to measure the model's accuracy and guide the optimization process (Para 67, teaches training this model with single sign data so the loss used is a combination of classification loss such as cross-entropy, CTC loss, etc. and Triplet/Contrastive loss such as Euclidean distance, cosine similarity, etc. with hyperparameters controlling their contribution to total loss).
With regards to claim 9, Rahmani teaches the software system of claim 1, wherein the user interface is developed using web technologies such as HTML, CSS, and JavaScript, and may utilize front-end frameworks like React, Angular, and Vue.js. (Para 88, teaches that the machine-readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.).
With regards to claim 11, Rahmani teaches the software system of claim 1, wherein the system is designed to handle a large number of concurrent users and includes continuous monitoring to maintain high accuracy and reliability, potentially utilizing cloud services such as AWS, Azure, and Google Cloud (Para 119, teaches that there may be translations among multiple speakers and/or multiple signers. Para 81, teaches that some or all of the circuitry may be instantiated, for example, in one or more threads executing concurrently on hardware and/or in series on hardware. Para 86, teaches use of cloud-based services, while figure 5, teaches use of AWS).
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.
7. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Rahmani in view of Chandler (U.S. Patent Application Publication # 2019/0251702 A1).
With regards to claim 2, Rahmani teaches the software system of claim 1, wherein the data collection module captures and labels images (Para 55, teaches techniques for capturing and labeling images from video or data collection);
Although Rahmani teaches use of Python (Para 88), it does not explicitly detail use of Python or OpenCV for capturing or labeling of images. This is taught by Chandler (See paragraphs 294-298);
Rahmani and Chandler are analogous art as they belong to a similar field of endeavor in speech/gesture processing. A person of ordinary skill in the art would have combined these teachings because it is a straightforward application of Chandler’s Python-based image capturing and labeling process to Rahmani’s image capturing and labeling process.
8. Claims 3-4 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Rahmani in view of Hadad (U.S. Patent Application Publication # 2025/0350584 A1).
With regards to claim 3, Rahmani teaches the software system of claim 1, wit pre-processing module (See para 40), however, it may not explicitly detail the limitation wherein the pre-processing module utilizes libraries such as TensorFlow, Keras, PyTorch, OpenCV, NumPy, and Pandas for data manipulation and augmentation. This is taught by Hadad (Paragraphs 31-33, teach TensorFlow libraries used for training, testing and deployment of machine learning models);
Rahmani and Hadad are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine design choice a person of ordinary skill in the art would make.
With regards to claim 4, Rahmani teaches the software system of claim 1, wherein the training module trains the machine learning model (Paragraphs 34-39, teaches training, testing and deployment of machine learning models);
However, Rahmani may not explicitly detail using machine learning frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn to train the machine learning model. This is taught by Hadad (Paragraphs 31-33, teach TensorFlow used for training, testing and deployment of machine learning models);
Rahmani and Hadad are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine design choice a person of ordinary skill in the art would make.
With regards to claim 6, Rahmani teaches the software system of claim 1, wherein the testing module tests the machine learning model (Paragraphs 34-39, teaches training, testing and deployment of machine learning models);
However, Rahmani may not explicitly detail using libraries such as Scikit-learn, TensorFlow, and PyTorch to calculate performance metrics. This is taught by Hadad (Paragraphs 31-33, teach that TensorFlow used for training, testing and deployment of machine learning models);
Rahmani and Hadad are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine design choice a person of ordinary skill in the art would make.
With regards to claim 7, Rahmani teaches the software system of claim 1, with a hyperparameter tuning module (See para 39);
However, Rahmani may not explicitly detail the limitation wherein the hyperparameter tuning module uses tools such as Scikit-learn, Optuna, and Hyperopt for hyperparameter optimization. This is taught by Hadad (Paragraphs 31-33, teach that Scikit-learn is tuning machine learning models);
Rahmani and Hadad are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine design choice a person of ordinary skill in the art would make.
With regards to claim 8, Rahmani teaches the software system of claim 1, wherein the deployment module deploys the machine learning model (Paragraphs 34-39, teaches training, testing and deployment of machine learning models);
However, Rahmani may not explicitly detail using tools such as TensorFlow Serving, Flask, FastAPI, Docker, and Kubernetes to ensure scalability and ease of deployment. This is taught by Hadad (Paragraphs 31-33, teach that TensorFlow used for training, testing, scalability and deployment of machine learning models);
Rahmani and Hadad are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine design choice a person of ordinary skill in the art would make.
9. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rahmani in view of Aull (U.S. Patent Application Publication # 2009/0115721 A1).
With regards to claim 10, although Rahmani teaches use of a camera (See figure 3). It may not explicitly detail the limitation further comprising a user setup process that includes camera calibration and initial gesture recognition tests guided by the user interface. This is taught by Aull (Paragraphs 27-31, teach initial gesture recognition using the camera along with camera calibration);
Rahmani and Aull are analogous art as they belong to a similar field of endeavor in speech/gesture processing. Combining these teachings for the above claimed application is a routine set-up process for any person of ordinary skill in the art.
Conclusion
10. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Jadhav (U.S. Patent Application Publication # 2026/0080802 A1), Shankar (U.S. Patent Application Publication # 2025/0292191 A1). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)