DETAILED ACTION
Claims 21--40 are pending in the Instant Application.
Claims 21-26, 28-33 and 35-40 are rejected (Non-Final Office Action).
Claims 27 and 34 are objected to.
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 .
Priority
The Instant Application filed 06/19/2024 is a continuation of CT/CN2022/14022, filed 12/20/2022 claims foreign priority to 202111564623.8, filed 12/20/2021
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03 October 2024, 1 March 2025 and 21 August 2025 were considered by the examiner.
Claim Rejections - 35 USC § 102
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 21-25, 28-33 and 34-39 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Watanabe et al (“Watanabe”), United States Patent Application No. 20230050708..
As per claim 21, Watanabe discloses a communication method ([0041] wherein distributed computing nodes communicate), comprising:
obtaining first data and first information from a first node ([0053] additional training data (first data) is obtained from the first node (central training server in the prior art) along with first information, the number of data samples per label), wherein the first information indicates a data augmentation manner of the first data ([0053] wherein the additional information includes what specific examples of a label training data that is needed to be augmented, and [0045] wherein the data augmentation module employs either conventional or another manner (technique in the prior art) based on the existing labeled data); and determining a first training dataset of an artificial intelligence (Al) model based on the first data and the first information ([0061]-[0062] wherein training is performed using the training dataset, determined from first data and the first information.)
As per claim 22, Watanabe discloses the method according to claim 21, further comprising: sending second information to the first node ([0051] wherein statistical information is sent (statistical information being second information) to the first node, central training server)), wherein the second information indicates: a type of the first data, a scenario corresponding to the first training dataset, a data amount of the first data, or a data augmentation manner supported by a second node that trains the Al model ([0051] wherein Examiner notes the use of “or” and the number of data samples for each label is described, which is the data amount of the first data).
As per claim 23, Watanabe discloses the method according to claim 21, further comprising: when performance of the Al model does not meet a performance requirement ([0067] wherein the AI model ca be tested for performance (accuracy in the prior art)), obtaining second data from the first node ([0060] wherein statistical data is obtained where the number of local training data sets are insufficient with respect to the count of data samples), wherein the Al model is obtained through training based on the first training dataset ([0067] wherein the trained model is described), and update training on the Al model is performed based on the second data ([0060] wherein additional (updated) training on the AI model is performed with new samples).
As per claim 24, Watanabe discloses the method according to claim 23, wherein obtaining the second data from the first node comprises:
sending third information to the first node, wherein the third information indicates that the performance of the Al model does not meet the performance requirement or the third information requests the second data; and receiving the second data from the first node ([0060] and [0067] wherein if the performance fail, third data (in the form of additional samples) are received).
As per claim 25, Watanabe discloses the method according to claim 23, further comprising: obtaining, from the first node, information that indicates the performance requirement ([0070] wherein the first node, (central training server in the prior art) a threshold selection module).
As per claim 28, Watanabe discloses a communication method ([0041] wherein distributed computing nodes communicate), comprising: determining first data and first information ([0053] additional training data (first data) is obtained from the first node (central training server in the prior art) along with first information, the number of data samples per label), wherein the first information indicates a data augmentation manner of the first data ([0053] wherein the additional information includes what specific examples of a label training data that is needed to be augmented, wherein the manner of augmentation is determined by the input of the existing labeled training data i.e. the first information); and sending the first data and the first information to a second node , wherein a first training dataset of an artificial intelligence (AI) model is determined based on the first data and the first information ([0058]-[0060\wherein data is sent and received as first training data).
As per claim 29, Watanabe discloses the method according to claim 28, further comprising: obtaining second information ([0051] wherein statistical information is sent (statistical information being second information) to the first node, central training server)), wherein the second information indicates: a type of the first data, a scenario corresponding to the first training dataset, a data amount of the first data, or a data augmentation manner supported by the second node ([0051] wherein Examiner notes the use of “or” and the number of data samples for each label is described, which is the data amount of the first data).
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As per claim 30, Watanabe discloses the method according to claim 28, further comprising: sending second data to the second node ([0060] wherein statistical data is obtained where the number of local training data sets are insufficient with respect to the count of data samples), wherein training on the AI model is updated based on the second data ([0060] wherein additional (updated) training on the AI model is performed with new samples based on the statistics).
As per claim 31, Watanabe discloses the method according to claim 30, wherein before sending the second data to the second node, the method further comprises:
receiving third information from the second node ([0067] wherein an accuracy test is performed and the result is received), wherein the third information indicates that performance of the AI model does not meet a performance requirement or the third information requests the second data (Examiner notes the use of “or” where either can be the third information [0060] and [0067] wherein if performance criteria is not met, additional samples are provided).
As per claim 32, Watanabe discloses the method according to claim 31, further comprising: sending, to the second node, information that indicates the performance requirement ([0067] wherein the second note is sent information that indicates the performance requirement by sending additional samples.)
As per claim 33, Watanabe discloses the method according to claim 28, further comprising: receiving fourth information from the second node, wherein the fourth information requests the data augmentation manner of the first data ([0061] wherein the labeling information is the fourth information that determines the data augmentation manner (type of labeled data needed) see [0045] wherein either conventional or other data augmentation manners (techniques in the prior art) are used based on the labeled training data).
As per claim 35, Watanabe discloses a communication apparatus, comprising: at least one processor ([0025]), wherein the at least one processor is coupled to at least one memory([0025]), and when the at least one processor executes instructions stored in the at least one memory, the communications apparatus is caused to perform the method of claim 21. Thus, claim 35 is rejected for the same rationale and reasoning as claim 21.
As per claim 36, claim 36 is the apparatus performing the method of claim 22 and is rejected for the same rationale and reasoning.
As per claim 37, claim 37 is the apparatus performing the method of claim 23 and is rejected for the same rationale and reasoning.
As per claim 38, claim 38 is the apparatus performing the method of claim 24 and is rejected for the same rationale and reasoning.
As per claim 39, claim 39 is the apparatus performing the method of claim 25 and is rejected for the same rationale and reasoning.
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.
Claims 26 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe in view of Kent, United States Patent Application Publication No. 2011/0238407.
As per claim 26, Watanabe discloses the method according to claim 21, but does not disclose sending fourth information to the first node, wherein the fourth information requests the data augmentation manner of the first data. However, Kent teaches sending fourth information to the first node, wherein the fourth information requests the data augmentation manner of the first data ([Claim 18 of the prior art] wherein a speech sample (augmentation manner) is requested).
Both Watanabe and Kent describe augmenting training data for a learning model. One could use the method of requesting samples by manner In Kent, with the sample creation in Watanabe to teach the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the method of determining if a node has samples to train a model without providing additional samples or augmentation and then providing samples as in Watanabe with the request to augment the training data with data obtained in a certain manner as in Kent to be able to fulfill a particular need for samples of a certain type.
As per claim 40, claim 40 is the apparatus performing the method of claim 25 and is rejected for the same rationale and reasoning.
Allowable Subject Matter
Claims 27 and 34 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The following limitations in claim 27 including, “wherein the fourth information comprises indication information of a first data augmentation manner, and wherein: the first information comprises acknowledgment information, and the acknowledgment information indicates that the data augmentation manner of the first data comprises the first data augmentation manner; or the first information comprises negative acknowledgment information and indication information of a second data augmentation manner, the negative acknowledgment information indicates that the data augmentation manner of the first data does not comprise the first data augmentation manner, and the data augmentation manner of the first data comprises the second data augmentation manner.” are neither anticipated nor obvious over the prior art on record. Thus, the claim is objected to.
Claim 34 is substantially similar to claim 27 and is objected to for the same rationale and reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KANNAN SHANMUGASUNDARAM whose telephone number is (571)270-7763. The examiner can normally be reached M-F 9:00 AM -6:00 PM.
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/KANNAN SHANMUGASUNDARAM/Primary Examiner, Art Unit 2168
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