DETAILED ACTION
Notice of 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 .
Claims 1-20 are pending and are rejected.
Priority
Foreign priority:
Acknowledgment is made of applicant’s claim for foreign priority to application no. KR10-2022-0095642 filled on 08/01/2022 and application no. KR10-2022-0102519 filled on 08/17/2022. The certified copies has been received.
PCT:
The current application is a CON of the PCT application no. PCT/KR2023/010848 filled on 07/26/2023.
Information Disclosure Statement
The information disclosure statements (IDSs) filled on 07/27/2023, 02/13/2024 and 10/17/2024 are compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
Drawings filled on 08/16/2023 are acceptable for examination purpose.
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 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claims 1 and 11:
Claim 1 recites the limitation "one or more transformers" in line 18 and claim 11 recites the limitation "one or more transformers" in line 15. There is insufficient antecedent basis for this limitation in the claim.
For the examination purpose, in broadest reasonable interpretation, the limitation is construed as the one or more transformers.
Dependent claims 2-10 and 12-20:
Based on their dependencies in claim 1, claims 2-10 are also rejected under 35 U.S.C. 112(b) for the same reasons.
Based on their dependencies in claim 11, claims 12-20 are also rejected under 35 U.S.C. 112(b) for the same reasons.
Allowable Subject Matter
Claims 1 and 11 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) set forth in this Office action.
Reasons for indicating Allowable subject matter
Claims 1 and 11 include allowable subject matter. The following is an examiner’s statement of reasons for indicating the allowable subject matter:
Claims 1-10:
Regarding Claim 1:
Baughman et al. (US20230306238A1) discloses, An electronic device comprising: an interface; a memory configured to store a learning model; and
a processor configured to provide a control recommendation for an external electronic device by using the learning model stored in the memory, [¶38: “FIG. 1 depicts” “computer system 100 (alternatively, computer)” “may include a processor 110, memory 120, an input/output interface (herein I/O or I/O interface) 130, and a main bus 140. The main bus 140 may provide communication pathways for the other components of the computer system 100.”… ¶61: “FIG. 3A depicts a system 300 for performing artificial intelligence in an IoT ecosystem, consistent with some embodiments of the disclosure. System 300 may include the following: a communications network 310 for communicatively coupling the various components; a plurality of first IoT devices 320-1, 320-2, and 320-3 (collectively, first IoT devices 320); a plurality of second IoT devices 322-1, 322-2, 322-3, and 322-4 (collectively, second IoT devices 322), and a Multi-Level Coordinated Internet of Things Artificial Intelligence (“MLC”) 330.”];
wherein the learning model comprises:
an input layer that generates a plurality of first embedding vectors corresponding to an input sequence including a series of control….on a plurality of external electronic devices by applying embedding weights to the input sequence, [¶73: “the first plurality of ML operations 340, including NNs 340-1, 340-2, and 340-3, may perform embedding operations. For example, NN 340-1 may be configured as an auto-encoder NN that performs a training by filtering input data into a vector in a hidden layer that is smaller than the number of input neurons. Further, NN 340-1 may also be configured to take the small number of neurons in the hidden layer and be trained to output as an auto-decoder a signal that is similar to the output neurons. Upon successfully training, NN 340-1 may be altered, by removing the decoder portion, to leave only an embedding.”… ¶47: “The input connections 214 represent the output of the input neurons 212 to the hidden section 220. Each of the input connections 214 varies depending on the value of each input neuron 212 and based upon a plurality of weights (not depicted). For example, the first input connection 214-1 has a value that is provided to the hidden section 220 based on the input neuron 212-1 and a first weight. Continuing the example, the second input connection 214-2 has a value that is provided to the hidden section 220 based on the input neuron 212-1 and a second weight.”];
a first encoding layer that outputs a plurality of first output vectors by using one or more transformers to generate a plurality of respective first encoded vectors from the plurality of first embedding vectors,… [¶68: “the MLC may direct the first plurality of ML operations 340 to the second plurality of ML operations 350, at 370.”… ¶74: “The second plurality of ML operations 350 may include one or more ML models and/or NNs.”… ¶78: “the second plurality of ML operations 350, including first device/task NNs 351-1, 351-2, 351-3, 351-4,” “NN 351-1 may be configured as an auto-encoder NN that performs a training by filtering input data. Further, NN 351-1 may also be configured to be trained to output as an auto-decoder. Upon successfully training, NN 351-1 may be altered, by removing the decoder portion, to leave only an embedding.” “mask layers and/or transformer layers may be added to NN 351-1. The mask layers and/or transformer layers may be based on a particular IoT task,”];
a second encoding layer that outputs a second output vector…generate a plurality of second embedding vectors, using one or more transformers to generate a plurality of second encoded vectors from the plurality of second embedding vectors,… [¶79: “The third plurality of ML operations 360 may include one or more ML models and/or NNs. In detail, the third plurality of ML operations 360 may include first IoT task NN 360-1, second IoT task NN 360-2, third IoT task NN 360-3, and fourth IoT task NN 360-4.”… ¶85: “The instruction of running to NN 360-1 may be to use the output of NN 351-1 and the output of NN 352-1 as input. The third ML operations may yield a third ML output (e.g., a vector, a set of values) from NN 360-1. The third ML output may be considered an IoT output.”… ¶89: “MLC 330 may instruct third ML operations to be executed by NN 360-2. The instruction of running to NN 360-2 may be to use the output of NN 351-2, the output of NN 352-2, and the output of NN 353-1 as input. The third ML operations may yield a third ML output (e.g., a vector, a set of values) from NN 360-2. The third ML output may be considered an IoT output.”];
wherein the first trained parameters and the second trained parameters are learned such that a loss….function is used to improve layers/levels [¶78: “Each NN of the second plurality of ML operations 350 may have a similar loss function, such as a task learning loss score for updating of the weights of various layers of neurons.”… ¶81: “The shared loss function may be a multi-dimensional function. Upon training or updating of a NN of the second level, other NNs on the second level and other NNs on the third level may be improved or updated. Similarly, upon training or updating of a NN on the third level, other NNs on the second level and third level may be improved or updated. Consequently, operations related to one IoT device and/or IoT task may improve the performance and accuracy of other NNs as related to other devices and/or tasks.”], but doesn’t explicitly disclose and
Do et al. (US20230290337A1) discloses, a second encoding layer that outputs a second output vector by adding position information to the plurality of first output vectors to generate a plurality of second embedding vectors,… applying second weights to values of the plurality of second encoded vectors,… [¶90: “Referring to FIGS. 8 and 9 , a slot tagging model trained by the training module 120 may include an embedding layer 121, an encoding layer 122,”… ¶98: “The encoding layer 122 may include a first encoding layer 122 a and a second encoding layer 122 b.” “the second encoding layer 122 b may be for encoding a vector for a dictionary sequence, i.e., the second embedding vector.”… ¶102-¶103: “second encoding layer 122 b may encode the second embedding vector di using a one-stack dense layer, as shown in Equation 2 below.” “wei =Wi *di +bi Equation 2” “where wei denotes an encoded vector for the second embedding vector di, W denotes a weight matrix, and bi denotes a bias term.”];
trained parameters are learned such that a loss…is minimized [¶112: “a loss value calculator and a weight adjuster. The loss value calculator may calculate a loss value for the slot tagging result using a loss function. For example, the loss value calculator may use a cross-entropy as a loss function. The weight adjuster may adjust weights of hidden layers of the slot tagging model in a direction to minimize the calculated loss value.”], but doesn’t explicitly disclose and
Clement et al. (US20230222334A1) discloses, wherein the first trained parameters and the second trained parameters are learned such that a loss between training data for the learning model” “is minimized. [¶82: “During training, an error loss is computed which is used to optimize the weights of the model (block 608). The loss is then used to adjust the model weights during training in order to minimize the loss function. The backward pass backpropagates the loss to each layer where a gradient is computed (block 610) and used to update the weights of that layer (block 612). The process of adjusting or updating the weights is considered model training and as the model keeps training and the loss is getting minimized, the model is learning.”].
Regarding claim 1, the following prior arts teach in the field of machine learning system for optimizing control settings for electronic devices:
Lee et al. (US20210034677A1, equivalent of foreign KR20210015524A listed in the IDS filed on 02/13/2024) describes, [¶12: “generate an embedding vector through text embedding by using first text data which is included in the user data and is related to content of the user data for each type of the user data; calculate a weight for the embedding vector using information which is included in the user data and is related to usability of the user data for each type of the user data; when a query is input, generate a query vector through the text embedding using second text data included in the query; and quantify user interest in the query for each type of the user data based on the embedding vector, the weight for the embedding vector, and the query vector.”];
ZHAO et al. (US20180285730A1, listed in the IDS filed on 02/13/2024) describes, [¶53: “Reference is now made to FIG. 3D, which illustrates a method of generating embedding features. As shown in FIG. 3D, after the training process completes, each element of hidden layer 186 can store a set of scaling parameters wni, each of which is associated with a particular dimension in the input vector (and a particular element in training data sequence 182). For example, element 186-1 of hidden layer 186 stores a set of scaling parameters including W1 1 associated with item 1 and W1 2 associated with content 1. Further, element 186-2 of hidden layer 186 also stores a set of scaling parameters including W2 1 associated with item 1 and W2 2 associated with content 1. These parameters can be stored in, for example, a table 190, where each column is associated with a particular element of hidden layer 186.”];
Choudhary et al. (US20210012770A1, equivalent of foreign KR20220031610A listed in the IDS filed on 02/13/2024) describes, [¶49: “A fusion embedding network 220 is configured to combine outputs of the embedding networks 202-206” “Each of the common embedding vectors 222-226 can be weighted with a corresponding weight W1, W2, and W3, respectively, and combined at the fusion embedding network 220. A mapping 230 is configured to select an output 232 and a confidence level 234 that correspond to the combined embedding vector 228.”];
However, none of the Baughman et al. (US20230306238A1), Do et al. (US20230290337A1), Clement et al. (US20230222334A1), Lee et al. (US20210034677A1), ZHAO et al. (US20180285730A1) and Choudhary et al. (US20210012770A1) taken either alone or in obvious combination disclose, An electronic device, specifically including:
wherein the learning model comprises:
an input layer that generates a plurality of first embedding vectors corresponding to an input sequence including a series of control histories of a user on a plurality of external electronic devices by applying embedding weights to the input sequence,
a first encoding layer that outputs a plurality of first output vectors by using one or more transformers to generate a plurality of respective first encoded vectors from the plurality of first embedding vectors, applying first weights to the plurality of first encoded vectors, and adding the plurality of first encoded vectors to which the first weights have been applied, and
a second encoding layer that outputs a second output vector by adding position information to the plurality of first output vectors to generate a plurality of second embedding vectors, using one or more transformers to generate a plurality of second encoded vectors from the plurality of second embedding vectors, applying second weights to values of the plurality of second encoded vectors, and adding the plurality of second encoded vectors to which the second weights have been applied,
wherein the first weights are based on a query vector and first trained parameters,
wherein the second weights are based on time information and second trained parameters, and
wherein the first trained parameters and the second trained parameters are learned such that a loss between training data for the learning model and the control recommendation based on the learning model is minimized.
(in combination with other elements of the claim)
having all the claimed features of applicant’s instant invention, including:
An electronic device comprising:
an interface;
a memory configured to store a learning model; and
a processor configured to provide a control recommendation for an external electronic device by using the learning model stored in the memory,
wherein the learning model comprises:
an input layer that generates a plurality of first embedding vectors corresponding to an input sequence including a series of control histories of a user on a plurality of external electronic devices by applying embedding weights to the input sequence,
a first encoding layer that outputs a plurality of first output vectors by using one or more transformers to generate a plurality of respective first encoded vectors from the plurality of first embedding vectors, applying first weights to the plurality of first encoded vectors, and adding the plurality of first encoded vectors to which the first weights have been applied, and
a second encoding layer that outputs a second output vector by adding position information to the plurality of first output vectors to generate a plurality of second embedding vectors, using one or more transformers to generate a plurality of second encoded vectors from the plurality of second embedding vectors, applying second weights to values of the plurality of second encoded vectors, and adding the plurality of second encoded vectors to which the second weights have been applied,
wherein the first weights are based on a query vector and first trained parameters,
wherein the second weights are based on time information and second trained parameters, and
wherein the first trained parameters and the second trained parameters are learned such that a loss between training data for the learning model and the control recommendation based on the learning model is minimized.
Claims 2-10 would be allowable based on their dependencies on claim 1.
Claims 11-20:
Regarding Claim 11:
The non-transitory computer readable storage medium of claim 11 includes similar limitations as the electronic device of claim 1. Therefore, the non-transitory computer readable storage medium of claim 11 also include allowable subject matter for the same reasons as described above in claim 1.
Claims 12-20 would be allowable based on their dependencies on claim 11.
It is for these reasons that applicant's invention defines over the prior art of the record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED SHAFAYET whose telephone number is (571)272-8239. 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, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2116
/M.S./
Patent Examiner,
Art Unit 2116