DETAILED ACTION
Claims 1, 8, 10 – 12 and 14 have been amended.
Claims 1 – 20 have been examined and are pending.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 5, 7 – 12, 14 – 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2020/0045163 to Hwang et al. (hereinafter Hwang) and in view of US Patent Application Publication No. 2020/0401886 to Deng et al. (hereinafter Deng).
Claim 1, Hwang discloses (¶14) providing an apparatus control method and an electronic device for recommending an application or a function of an application based on a situation of a user who uses the electronic device, which further includes:
communication method using artificial intelligence (AI) (Figs. 2 and 3, artificial neural network) at a first communication apparatus (terminal 100), the method comprising:
receiving information (¶215, learning device 200 transmit learning model to equipment 100) about an AI model (¶60-¶72) from a second communication apparatus (learning device 200), wherein the AI model (¶22, ¶26, 1st and 2nd learning model) comprises N sub-network models, the N sub-network models respectively correspond (¶77, multi-layer neural network) to N model identifiers (IDs) (¶201,225 model divides into a plurality of versions and these versions can be directed to identifiers for the sub-networks), the information about the AI model comprises model files of X sub-network models (¶90 training files of the AI model and the files are a label) in the N sub-network models, N and X are positive integers, N is greater than 1, and X is less than or equal to N (¶130, learning algorithms; ¶225 dividing the model into a plurality of versions, where each version is positive integer) and executing an AI service based on the information about the AI model (¶35, ¶249, model recommends application or function of an application)
Hwang does not explicitly disclose sending, by the first communication apparatus, service requirement information of an AI service to a second communication apparatus, where the service requirement information of the AI service is used to request the AI service, based on the service requirement information of the AI service, receiving information about an AI model of the AI service from the second communication apparatus, forming the AI model based on the model files of the X sub-network models corresponding to the N model identifiers. However, in an analogous art, Deng teaches:
sending, by the first communication apparatus, service requirement information of an AI service to a second communication apparatus, where the service requirement information of the AI service is used to request the AI service (Deng teaches ¶57 sending a request from the first computing system (i.e. consumer system) to the second communication apparatus (i.e. provider system). The purpose of service request includes the service requirement information of the AI service (e.g., for training model, or for making prediction, or the service may be an Ad impression bid request for advertising cloud clients’ campaigns)
based on the service requirement information of the AI service, receiving information about an AI model of the AI service from the second communication apparatus (Deng teaches ¶58 the provider generate predictions using the provider machine learning model. The provider machine learning model output (e.g., predictions) is then passed to the consumer system)
forming the AI model based on the model files of the X sub-network models corresponding to the N model identifiers (Deng teaches (¶41-¶42, ¶62 and Fig. 6) the integration component 630 may be configured to join the model output 622, 622-N with the consumer private data to form input data 632, 632-N for training the consumer machine learning model and for making predictions. Deng teaches ¶76 the integration component 630 may be configured to select one or more consumer input features to be joined with the data transferred from the provider system. Also, these data may be joined by (N model) common feature identifiers IDs.)
It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the communication method using artificial intelligence (AI) at a first communication apparatus, the method comprising: receiving information about an AI model from a second communication apparatus, wherein the AI model comprises N sub-network models, the N sub-network models respectively correspond to N model identifiers (IDs), the information about the AI model comprises model files of X sub-network models in the N sub-network models, N and X are positive integers, N is greater than 1, and X is less than or equal to N and executing an AI service based on the information about the AI model, as disclosed by Hwang, and sending, by the first communication apparatus, service requirement information of an AI service to a second communication apparatus, where the service requirement information of the AI service is used to request the AI service, based on the service requirement information of the AI service, receiving information about an AI model of the AI service from the second communication apparatus, forming the AI model based on the model files of the X sub-network models corresponding to the N model identifiers, as taught by Deng, for the purpose of implementing methods and systems allowing individuals/consumers to build, develop and implement machine learning models capable of generating predictions using both in-the-field data and data transferred from another model (provider model) while having the in-the-field data, local model and provider model remain private and secured (¶4).
Claim 2, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose:
wherein the N sub-network models comprise Y backbone network models and (N—Y) functional network models, and Y is a positive integer (¶79, a multi-layer neural network may include an input layer, one or more hidden layers, and an output layer)
The motivation to combine the reference is similar to the reasons in Claim 1.
Claim 3, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose:
method according to claim 1, wherein before receiving the information about the AI model from the second communication apparatus (¶179, stored usage history and information), the method further comprises:
sending, to the second communication apparatus, model IDs of the X sub-network models or indexes corresponding to the model IDs of the X sub-network models, wherein the model files of the X sub-network models do not exist locally, or are damaged, in the first communication apparatus (¶30, transmitting information related to a difference (i.e., model does not exist locally) between the first learning model and the second learning model to a second server device),
or
sending, to the second communication apparatus, model IDs of (N—X) sub-network models other than the X sub-network models in the N sub-network models or indexes corresponding to the model IDs of the (N—X) sub-network models, wherein model files of the (N— X) sub-network models already exist locally in the first communication apparatus (¶32, after the generating of the first learning model, transmitting the first learning model to a second server device and requesting to store the first learning model as associated with the user)
The motivation to combine the reference is similar to the reasons in Claim 1.
Claim 4, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose: wherein the method further comprises:
receiving information about the N sub-network models from the second communication apparatus (¶215, learning device 200 transmit learning model to equipment 100), wherein the information about the N sub-network models comprises the model IDs of the N sub-network models or indexes corresponding to the model IDs of the N sub-network models AI model (¶225, the model storage 231 stores the trained model by dividing the model into a plurality of versions depending on a training timing or a training progress.),
or
receiving, from the second communication apparatus, the model IDs of the (N—X) sub- network models other than the X sub-network models in the N sub-network models or the indexes corresponding to the model IDs of the (N—X) sub-network models.
The motivation to combine the reference is similar to the reasons in Claim 1.
Claim 5, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose:
wherein the method further comprises: sending, to the second communication apparatus, at least one of a service type of the AI service, a dataset type, a data type, or a computing resource, wherein the at least one of the service type of the AI service, the dataset type, the data type, or the computing resource is used to determine the model IDs of the N sub-network models (¶269, processor 550 may apply at least one of the environmental information or the usage information to the learning model when receiving a preset input for operating the application recommendation from the user; ¶324, the electronic device 100a or 100b may transmit to the server devices 200a and 200b the difference (e.g., differences in parameters or node structures such as thresholds, weighting values, etc.) between the learning model generated after the training model and the training model before training.)
The motivation to combine the reference is similar to the reasons in Claim 1.
Claim 7, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose:
wherein the model ID of a sub-network model in the N sub-network models comprises or indicates at least one of: a model type of the sub-network model, a dataset type of the sub-network model, a data type of the sub-network model, a network layer number of the sub-network model, a backbone network type of the sub-network model, a backbone network dataset type of the sub- network model, a backbone network data type of the sub-network model, a backbone network layer number of the sub-network model, or a computing resource type of the sub-network model (¶314, the context information applied to the learning model identification, such as related to types of other devices, application types (e.g. a navigation device or a music playing etc.)
The motivation to combine the reference is similar to the reasons in Claim 1.
Claim 8, do not teach or further define over the limitations in Claim 1. Therefore, claim 8 is rejected for the same rationale of rejection as set forth in Claim 1.
Claim 9, do not teach or further define over the limitations in Claim 2. Therefore, claim 9 is rejected for the same rationale of rejection as set forth in Claim 2.
Claim 10, do not teach or further define over the limitations in Claim 3. Therefore, claim 10 is rejected for the same rationale of rejection as set forth in Claim 3.
Claim 11, do not teach or further define over the limitations in Claim 4. Therefore, claim 11 is rejected for the same rationale of rejection as set forth in Claim 4.
Claim 12, do not teach or further define over the limitations in Claim 5. Therefore, claim 12 is rejected for the same rationale of rejection as set forth in Claim 5.
Claim 14, do not teach or further define over the limitations in Claim 1. Therefore, claim 14 is rejected for the same rationale of rejection as set forth in Claim 1.
Claim 15, do not teach or further define over the limitations in Claim 2. Therefore, claim 15 is rejected for the same rationale of rejection as set forth in Claim 2.
Claim 16, do not teach or further define over the limitations in Claim 3. Therefore, claim 16 is rejected for the same rationale of rejection as set forth in Claim 3.
Claim 17, do not teach or further define over the limitations in Claim 4. Therefore, claim 17 is rejected for the same rationale of rejection as set forth in Claim 4.
Claim 18, do not teach or further define over the limitations in Claim 5. Therefore, claim 18 is rejected for the same rationale of rejection as set forth in Claim 5.
Claim 20, do not teach or further define over the limitations in Claim 7. Therefore, claim 20 is rejected for the same rationale of rejection as set forth in Claim 7.
Claims 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2020/0045163 to Hwang, in view of US Patent Application Publication No. 2020/0401886 to Deng and in view of US Patent Application Publication No. 2021/0019151 to Pudipeddi et al. (hereinafter Pudipeddi).
Claim 6, Hwang in view of Deng discloses all the elements of claim 1. Further, Hwang disclose:
wherein the method further comprises: determining a model ID group of a first sub-network model in the N sub-network models based on a communication scenario of the AI service (¶84, artificial neural network trained using training data can classify in groups or cluster inputted data according to a pattern within the inputted data); and
determining a model ID of the first sub-network model (¶91, the label may refer to a target answer to be guessed (determined) by the artificial neural network when the training data is inputted to the artificial neural network), wherein the model ID of the first sub-network model is any model ID in the model ID group (¶120, index (reference) the optimal model parameters).
Hwang in view of Deng does not explicitly disclose a difference between accuracy rates of two models corresponding to any two model IDs in the model ID group is less than a specified threshold or the accuracy rates are both within a specified range. However, in an analogous art, Pudipeddi teaches:
a difference between accuracy rates of two models corresponding to any two model IDs in the model ID group is less than a specified threshold or the accuracy rates are both within a specified range (¶112, model accessor 130 may halt execution of the AI model when the accuracy of the AI model exceeds a predetermined threshold. ¶113, other metrics may be used in assessing AI model, for example, logarithmic loss, metrics derived from a confusion matrix, area under curve, F1 score, mean absolute error, mean squared error. When other metrics are used, the appropriate threshold for each metric may be determined and applied in the assessment of AI model.)
It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the method further comprises: determining a model ID group of a first sub-network model in the N sub-network models based on a communication scenario of the AI service, and determining a model ID of the first sub-network model, wherein the model ID of the first sub-network model is any model ID in the model ID group, as disclosed by Hwang in view of Deng, and a difference between accuracy rates of two models corresponding to any two model IDs in the model ID group is less than a specified threshold or the accuracy rates are both within a specified range, as taught by Pudipeddi, for the purpose of enabling the execution of arbitrarily large AI models on a memory-constrained target device that is communicatively connected to a parameter server (¶6).
Claim 13, do not teach or further define over the limitations in Claim 6. Therefore, claim 13 is rejected for the same rationale of rejection as set forth in Claim 6.
Claim 19, do not teach or further define over the limitations in Claim 6. Therefore, claim 19 is rejected for the same rationale of rejection as set forth in Claim 6.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments and amendments, filed on 11/07/2025 with respect to the Claims 1 – 20 have been fully considered and they are persuasive. Hence, the 35 USC § 101 rejection is withdrawn.
Claim Rejections - 35 USC § 102
Applicant’s arguments and amendments, filed on 11/07/2025 with respect to the Claims 1 – 5, 7 – 12, 14 – 18 and 20 have been fully considered and they are persuasive. Hence, the 35 USC § 102 rejection is withdrawn. However, based on the claim amendments and the newly introduced limitations, the search is updated and a new reference (US Patent Application Publication No. 2020/0401886 to Deng) is being introduced for the 35 USC § 103 rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASSAN ABDUR-RAHMAN KHAN whose telephone number is (313)446-6574. The examiner can normally be reached TEAPP - (M-Sa) 9/30/17-9/30/18, 6am-10pm IFP.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Parry can be reached at (571) 272-8328. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/H. A. K./
Examiner, Art Unit 2451
/Chris Parry/Supervisory Patent Examiner, Art Unit 2451
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