DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to application filed on 12/5/2023. Claims 1-5 are currently pending and have been considered below.
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.
Claims 2 and 5 are 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 2 and 5 recites “determine outputs of which a number is a largest among different numbers of outputs with the same value as the final result when the final result is determinable by a majority vote”. The limitation is vague and unclear since “a largest among different numbers of outputs with the same value” does not make sense – is it the largest or is it the same value with others. Accordingly, the claims are indefinite.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Poecher et al (US 20240265268 A1).
As per Claim 1, Poecher teaches a method of determining a final result using federated learning (Abstract, para. 0022-0023) the method comprising:
based on performance of artificial intelligence (AI) models of each of federated learning devices, determining weights of each of the AI models (para. 0022-0024, 0031-0032, 0050, 0064, 0075, 0086-0088, 0108-0110, regarding model weights);
inputting input data to each of the AI models by each of the federated learning devices (para. 0022, 0029-0035, 0048-0077, regarding training each model);
determining a final result for the input data by a final result determination server based on outputs and the weights of the AI models (para. 0022-0024, 0031-0032, 0050, 0064, 0075, 0086-0088, 0108-0110, regarding model weights; para. 0022, 0029-0035, 0048-0077, regarding training each model; abstract, para. 0022-0027, 0050, regarding results/benefits of federated learning; para. 0004, 0042-0043, regarding the server(s)).
As per Claim 2 (while the claim is not fully interpretable due to the deficiencies listed in 112(b) rejection above), Poecher teaches a method as recited above for Claim 1. Poecher further teaches wherein the determining of the final result comprises: determining whether the final result is determinable by a majority vote from the outputs of the AI models (para. 0015, 0023, 0050, 0051, 0054, 0071, 0083, 0088, 0115, regarding majority vote);
determine outputs of which a number is a largest among different numbers of outputs with the same value as the final result when the final result is determinable by a majority vote (para. 0015, 0023, 0050, 0051, 0054, 0071, 0083, 0088, 0115, regarding majority vote);
determining an output with a largest value obtained by applying the weights to each of the outputs of the AI models as the final result when the final result is not determinable by the majority vote (para. 0050, regarding confidence filtering as an alternative to majority voting; also para 0116, regarding confidence filtering).
As per Claim 3, Poecher teaches a method as recited above for Claim 1. Poecher further teaches learning the AI models by inputting learning data to the AI models; and determining the performance of the AI models by evaluating the learned AI models (para. 0022, 0029-0035, 0048-0077, regarding training each model).
As per claims 4-5, claims 4-5 recite substantially similar limitations as claim 1-2, respectively; therefore, claims 4-5 are rejected with the same reasoning, rationale and motivation as recited above for claims 1-2, respectively.
Conclusion
Additional relevant art not relied upon includes:
Wang et al (US 20240062072 A1), regarding “A federated learning method according to a seventeenth invention is a federated learning method in which a global model is communicated between a plurality of local servers and repeatedly learned cooperatively. The global model is a decision tree or a decision tree group including a shape of a tree indicating a relation between local training data and a weight of the relation. The federated learning method includes: a model generation step of generating a current local model via at least one of the local servers based on a global model generated by past learning and current local training data used for current learning; a gradient calculation step of calculating gradient values for the respective two or more local servers based on the current local model generated by the model generation step, the global model, and the current local training data, the gradient value being based on a function indicating an error between a predicted value and a measured value of an output result of the current local model; a calculation step of calculating the weight based on the gradient values calculated for the respective two or more local servers by the gradient calculation step; and a global model updating step of updating the global model based on the current local model generated by the model generation step and the weight calculated by the calculation step.”
Cirillo et al (US 20230237321 A1), regarding “As mentioned previously, federated learning techniques, such as the federal learning technique of FIG. 1, can be categorized as horizontal federated learning and vertical federated learning. FIG. 1 schematically illustrates an exemplary federated learning system 100. The federated learning system includes two local training nodes or systems 102, 104. A training node (e.g., a training device or system) may be configured to determine ML weights for a ML model based on data collected located. For instance, the training node 102 may be associated with a first local environment—such as a first smart building or a first industrial environment (e.g., a first factory). The training node 104 may be associated with a second local environment—such as a second smart building or a second industrial environment (e.g., a second factory).”
Wang et al (US 20230214676 A1), regarding “According to an aspect of the disclosure, there is provided a prediction model training method, which is performed by a server, including: transmitting, to a plurality of training devices, a model to be trained by the plurality of training devices, wherein the model to be trained includes feature extraction layers configured to extract user features and prediction layers configured to perform information prediction; classifying the plurality of training devices into at least one group based on the user features extracted by the training devices; receiving, from the plurality of training devices, model parameters obtained by the respective training devices training the model to be trained, wherein the model parameters include first parameters corresponding to the feature extraction layers and second parameters corresponding to the prediction layers; performing global federated aggregation based on the first parameters to obtain a global federated aggregation result; performing intra-group federated aggregation for each of the at least one group, based on the second parameters of one or more of the plurality of training devices in a respective group, among each of the at least one group, to obtain an intra-group federated aggregation result; and transmitting, to each of the plurality of training devices, the global federated aggregation result and the intra-group federated aggregation result associated the respective group of the respective training device, so that the plurality of training devices update the first parameters of the feature extraction layers based on the global federated aggregation result and update the second parameters of the prediction layers based on the intra-group federated aggregation result.”
Golden et al (US 11551353 B2), regarding “For example, the classifier may classify the malignancy, lesion type, cancer subtype or prognosis of the query lesion. The classifier may be a K-nearest-neighbors algorithm that generates a result based on majority voting of the returned results, or it may be a more sophisticated algorithm, such as a random forest or gradient boosted decision trees.”
Schmidt et al (US 11538152 B2), regarding “ It is e.g. possible that the aggregate algorithm comprises multiple of the local algorithms, wherein the outputs of the local algorithms are averaged and/or a majority voting for the output is performed. In this case the quality measure can be used to determine a weight for the different local algorithms. E.g. a higher weight can be used when the local algorithm performs well on the initial training data or on a certain part of the initial training data.”
FEDULOVA et al (US 20220391760 A1), regarding “Optionally, the determined model outputs may be combined into the combined model output by applying a trainable combination model to the determined correspondence scores and model outputs. The trained combination model is typically comprised in the combined model and as such may be distributed along with the trained models and their fingerprints for applying the combined model. As discussed, various non-trainable techniques may be used to combine the determined model outputs into the combined model, e.g., an average or a majority vote. However, as the inventors realized, by training a combination model, the accuracy of the determined model outputs can be further improved. For example, the combination model can be a decision tree for selecting one or more particular model outputs to use in the combination, a linear regression model for computing the combined model output from the determined model outputs, etcetera. The combination model can be trained by the party constructing the combined model, for example, but it is also possible to train the combined model using distributed learning techniques on the training datasets of the respective trained models, which may be a lot more feasible than an overall distributed learning approach since the dataset used to train the combination model can be relatively small and can use smaller inputs.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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