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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/1/2026 has been entered.
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.
Claim 1-4, 6-10, 12-16, 18-22, 24-28, 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-4, 6-10, 12-16, 18-22, 24-28, 30 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node.
The apparatus and the method claim 1, 7, 11, 19, and 25 recites limitation, “use one or more neural networks to classify one or more game-specific performance anomalies into one or more clusters of performance anomaly types based, at least in part, on feedback of one or more users pertaining to the one or more game-specific performance anomalies; and reduce the one or more clusters of performance anomaly types by at least reducing a dimensionality of data from the feedback or removing feedback that does not satisfy a minimum confidence threshold for a respective cluster of a performance anomaly type”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites use one or more neural networks to classify one or more game-specific performance anomalies into one or more clusters of performance anomaly types based, at least in part, on feedback of one or more users pertaining to the one or more game-specific performance anomalies; and reduce the one or more clusters of performance anomaly types by at least reducing a dimensionality of data from the feedback or removing feedback that does not satisfy a minimum confidence threshold for a respective cluster of a performance anomaly type. The classifying and clustering data (performance anomalies) using neural networks, based on user feedback and reducing clusters via dimensionality reduction or filtering by confidence threshold recited in the claim is no more than an abstract idea i.e., data analysis and organization activities specifically collecting, analyzing and classifying information, which are generally considered abstract idea under USPTO guidance (see MPEP 2106.04 (a) etc. (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements or specific technological improvement that would integrate the abstract idea into a practical application. (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, neural network, dimensional reduction and filtering recited in the claims are well-known techniques in data science and machine learning and are recited at a high level of generality. Therefore, there are no additional elements that would amount to significantly more than the abstract idea (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims does not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
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 (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 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.
Claims 1-2, 7-8, 13-14, 19-20, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Borovikov et al. (US 2021/0346798 A1) in view of Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS)
Regarding claim 1, Borovikov teaches one or more processors, comprising circuitry to: (see paragraph [0144], processor including electrical circuitry reads on one or more processors comprising circuitry) to
use one or more neural networks (see paragraphs [0054] & [0112] and Fig. 5, Machine learning algorithms can be used to generate prediction models and can include artificial neural network algorithms. This reads on one or more neural networks) to characterize one or more game-specific performance anomalies (e.g. paused game) into one or more groups (user tagged) of performance anomaly types based, at least in part, on feedback of one or more users pertaining to the one or more game-specific performance anomalies (see paragraph [0112] and Fig. 5, The model generation system 146, may receive feedback data 554. The feedback data may be received as part of a model generation process that enables a user interacting with the video game to generate an Imitation Learning (IL) model. For example, if an anomaly exists in the training data (e.g. selection of a pause button by the user) the user may tag the anomalous data enabling the model generation system to handle the tagged data differently by applying a different weight to the data or excluding the data from the model generation process. This reads on characterize one or more game-specific performance anomalies into one or more groups of performance anomaly types based, at least in part, on feedback of one or more users pertaining to the one or more game-specific performance anomalies).
Borovikov does not specifically teach reduce the one or more clusters of performance anomaly types by at least reducing a dimensionality of data from the feedback or removing feedback that does not satisfy a minimum confidence threshold for a respective cluster of a performance anomaly type.
However, in the same field of endeavor, Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS) teaches from Page 1 and 2 X1 that experiment trains a deep autoencoder network structure with four hidden layers, with input layer dimensions of 784, and hidden layer dimension of 1000, 500, 250 and 30 respectively. The process of obtaining the network weight is as follow: 1. Train the first RBM network, consisting of the input layer of 784 dimensions and the first hidden layer of 1000 dimensions, using RBM optimization with training samples. After optimization, calculate the output values of the training samples in the hidden layer. 2. Use the result from step 1 as input values for training the second RBM network, optimize it using the RBM network and calculate the network’s output values. Similarly train the third and fourth RBM network. 3. Connect the four RBM networks into a new network, divided into encoder and decoder parts, and initialize the new network with the values obtained in steps 1 and 2. 4. Since the final output and initial input node numbers of the new network are the same, the initial input values can be used as the output labels of the network theory. Use the BP algorithm to calculate the cost function and its derivatives. 5. Use the initial values from step 3 and the cost and derivative values from step 4 to optimize the entire new network using the conjugate gradient descent method, obtaining the final network weights i.e., reduce the one or more clusters of performance anomaly types by at least reducing a dimensionality of data from the feedback.
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Borovikov with the method of Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS) so as to reduce error rate and fine tune part uses the actual labels of the training samples for classification (See Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS) (See Page 2 Experiment Results and Experiment Summary).
Regarding claim 2 Borovikov teaches wherein the circuit is to determine the one or more game-specific performance anomalies using the feedback received from one or more player devices and relating to one or more games, the feedback being filtered to remove feedback unrelated to the one or more game- specific performance anomalies (see paragraph [0112] and Fig. 5, The model generation system 146, may receive feedback data 554. The feedback data may be received as part of a model generation process that enables a user interacting with the video game to generate an Imitation Learning (IL) model. For example, if an anomaly exists in the training data (e.g. selection of a pause button by the user) the user may tag the anomalous data enabling the model generation system to handle the tagged data differently by applying a different weight to the data or excluding the data from the model generation process. This reads on determine the one or more game-specific performance anomalies using the feedback received from one or more player devices and relating to one or more games, the feedback being filtered to remove feedback unrelated to the one or more game- specific performance anomalies).
Regarding claim 7, it has been rejected for the same reasons as claim 1.
Regarding claim 8, it has been rejected for the same reasons as claim 2.
Regarding claim 13, it has been rejected for the same reasons as claim 1.
Regarding claim 14, it has been rejected for the same reasons as claim 2.
Regarding claim 19, it has been rejected for the same reasons as claim 1.
Regarding claim 20, it has been rejected for the same reasons as claim 2.
Regarding claim 25, it has been rejected for the same reasons as claim 1 and further Borovikov teaches an anomaly classification system, comprising: one or more processors (see paragraph [0144], processor including electrical circuitry reads on one or more processors) to use one or more neural networks (see paragraphs [0054] and memory for storing network parameters for the one or more neural networks (see paragraphs [0138] - [0140]).
Regarding claim 26, it has been rejected for the same reasons as claim 2.
VII. Claims 3-4, 9-10, 15-16, 21-22, and 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Borovikov et al. (US 2021/0346798 A1) in view of Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS) and further in view of Phadke et al. (US 2019/0081969 A1).
Regarding claim 3, Borovikov Theory and Practice of Dimensionality Reduction Using Neural Network (Reference in IDS) does not specifically teach wherein feedback are classified using a trained classifier of the one or more neural networks, wherein classified feedback that is output from the trained classifier is to be generated using standardized language.
Phadke teaches wherein feedback are classified using a trained classifier of the one or more neural networks, wherein classified feedback that is output from the trained classifier is to be generated using standardized language (see paragraphs [0027] & [0038] – [0039], The machine learning unit can be implemented as a neural network. The machine learning unit classifies newly reported anomalies as True and False anomalies. For example, the machine learning model may be utilized to classify newly reported anomalies as True anomalies or False anomalies. The anomalies that classified as true anomalies may be displayed. This reads on feedback are classified using a trained classifier of the one or more neural networks, wherein classified feedback that is output from the trained classifier is to be generated using standardized language).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the feedback in Borovikov adapt to include being classified using a trained classifier of the one or more neural networks, wherein classified feedback that is output from the trained classifier is to be generated using standardized language because the machine learning model to classify reported anomalies in Phadke (see Phadke, Fig. 2) can be implemented into the system in Borovikov using well-known techniques and it would allow for an improved analysis of the reported anomalies (see Phadke, paragraph [0003]).
Regarding claim 4, Phadke teaches performing clustering of the classified output into a plurality of clusters relating to the performance anomaly types (see paragraph [0030], clustering techniques can be used to group similar anomalies together. This reads on performing clustering of the classified output into a plurality of clusters relating to the performance anomaly types)
Regarding claim 9, it has been rejected for the same reasons as claim 3.
Regarding claim 10, it has been rejected for the same reasons as claim 4.
Regarding claim 15, it has been rejected for the same reasons as claim 3.
Regarding claim 16, it has been rejected for the same reasons as claim 4.
Regarding claim 21, it has been rejected for the same reasons as claim 3.
Regarding claim 22, it has been rejected for the same reasons as claim 4.
Regarding claim 27, it has been rejected for the same reasons as claim 3.
Regarding claim 28, it has been rejected for the same reasons as claim 4.
Allowable Subject Matter
Claim 6, 12, 18, 24 and 30 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dimensionality reduction using neural network (Year: 2007)
Reducing the dimensionality of data with Neural Network (Year: 2006)
Applicant’s arguments with respect to claim(s) 1-4, 6-10, 12-16, 18-22, 24-28, 30 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIZAR N SIVJI whose telephone number is (571)270-7462. The examiner can normally be reached Monday-Friday 7-4.
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, Alison Slater can be reached at (571) 270-0375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/ Primary Examiner, Art Unit 2647
Prosecution Insights