DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office Action is in response to the submission filed 2026-07-13 (herein referred to as the Reply) where claim(s) 1-2, 4-9, 11, 13-18, 20 are pending for consideration.
35 USC §102 - Claim Rejections
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) is/are rejected under AIA 35 U.S.C. 102(a)(2) as being unpatentable over WANG_996 (US20210064996)
Claim(s) 1
WANG_996 teaches
obtaining N pieces of training data, wherein for one piece of training data of the N pieces of training data, the one piece of training data of the N pieces of training data corresponds to one piece of mark information, the mark information indicates an attribute of the training data, and N is an integer greater than 0; and The training module teaches and/or trains DNNs using known input data, which implicitly would need to obtain in order to use; that the training data corresponds to information that indicates an attribute of the training data is implicitly disclosed in [0059]: For instance, the training module trains DNN(s) for different purposes, such as processing communications transmitted over a wireless communication system (e.g., encoding downlink communications, modulating downlink communications, demodulating downlink communications, decoding downlink communications, encoding uplink communications, modulating uplink communications, demodulating uplink communications, decoding uplink communications) in conjunction with [0061]: the neural network table includes input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration. <FIG(s). 2; para. 0059-0062, 0067-0070>.
sending indication information to a terminal device, wherein the indication information indicates information about M artificial intelligence models, for one artificial intelligence model of the M artificial intelligence models, the one artificial intelligence model of the M artificial intelligence models is trained based on X pieces of training data in the N pieces of training data, the X pieces of training data are determined based on the mark information, and M and X are integers greater than 0. Training module extracts learned parameter configurations from the DNN to identify the NN formation configuration elements and/or NN formation configuration, and then adds and/or updates the NN formation configuration elements and/or NN formation configuration in the neural network table in conjunction with: the base station synchronizes the neural network table with the neural network table such that the NN formation configuration elements and/or input characteristics stored in one neural network table is replicated in the second neural network table, which would implicitly require communicating (sending/receiving) the synchronized data. <FIG(s). 2; para. 0059-0062>.
wherein the mark information indicates at least one of: a type of a wireless channel associated with the training data, wherein the type comprises line of sight and non-line of sight; input characteristics includes CQI, CSI and frequency bands indicate a type of channel <para. 0061>.
Claim(s) 9, 18
WANG_996 teaches
receiving indication information, wherein the indication information indicates information about M artificial intelligence models, for one artificial intelligence model of the M artificial intelligence models, the one artificial intelligence model of the M artificial intelligence models is trained based on X pieces of training data in N pieces of training data, and M, N, and X are integers greater than 0, wherein for one piece of training data of the N pieces of training data, the one piece of training data of the N pieces of training data corresponds to one piece of mark information, Training module extracts learned parameter configurations from the DNN to identify the NN formation configuration elements and/or NN formation configuration, and then adds and/or updates the NN formation configuration elements and/or NN formation configuration in the neural network table in conjunction with: the base station synchronizes the neural network table with the neural network table such that the NN formation configuration elements and/or input characteristics stored in one neural network table is replicated in the second neural network table, which would implicitly require communicating (sending/receiving) the synchronized data. [0061]: the neural network table includes input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration. <FIG(s). 2; para. 0059-0062, 0067-0070>.
the mark information indicates an attribute of the training data, and the mark information indicates at least one of: a type of a wireless channel associated with the training data, wherein the type comprises line of sight and non-line of sight; input characteristics includes CQI, CSI and frequency bands indicate a type of channel <para. 0061>.
Claim(s) 2, 11, 20
WANG_996 teaches
wherein for the one artificial intelligence model of the M artificial intelligence models, the one artificial intelligence model of the M artificial intelligence models corresponds to one training condition, and mark information corresponding to the X pieces of training data meets the training condition corresponding to the one artificial intelligence model of the M artificial intelligence models. Training module trains DNN(s) for different purposes, such as processing communications transmitted over a wireless communication system. A purpose can be considered a condition. Input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration. <para. 0059-0062>.
Claim(s) 4, 13
WANG_996 teaches
wherein the training condition comprises at least one of:
the type of the wireless channel; configuration parameters for evaluating inputs includes CQI, CSI and frequency bands indicate a type of channel <para. 0061>.
Claim(s) 5, 14
WANG_996 teaches
wherein the training condition corresponding to the one artificial intelligence model is preset. The training purpose and associated configuration information is predetermined. <para. 0059-0062>.
Allowable Subject Matter
Claim(s) is/are indicated as having allowable subject matter and objected to.
Claim(s) 6, 15
The claim(s) is/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.
Closest prior art is WANG_996 (US20210064996) as discussed herein, but Wang does not teach M training conditions corresponding to the M artificial intelligence models.
Claim(s) 7, 16
The claim(s) is/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.
Closest prior art is WANG_996 (US20210064996) as discussed herein, but Wang does not teach M training conditions corresponding to the M artificial intelligence models.
Claim(s) 8, 17
The claim(s) is/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.
Closest art KUPITMAN - US20210004320 teaches an indication of a request to evaluate an artificial intelligence (AI) mode but does not teach sending the indication information to the terminal device, wherein the indication information (1) indicates information about the artificial intelligence model requested by the terminal device and (2) wherein the indication information indicates information about M artificial intelligence models, for one artificial intelligence model of the M artificial intelligence models, the one artificial intelligence model of the M artificial intelligence models is trained based on X pieces of training data in the N pieces of training data. The Examiner has interpreted the intelligence model requested by the terminal device to be distinct from the M artificial intelligence models.
In addition to the explicit reasons given herein, allowability is also determined in view of the combination of references required for obviousness, the inter-relationship between other claimed limitations, and the claimed invention as a whole. Accordingly, amendments that do not incorporate the allowable claims into the base/intervening claims in its entirely, are not allowable. This includes amendments that incorporate the allowable claims into the base/intervening claims in part or in a non-narrowing manner (i.e., changing the scope of the subject matter).
Relevant Cited References
LOS/NLOS Marking for training data
TADAYON - US20210136527
ALBERT - US20220053345
RSRP Marking for training data
AGARWAL - US20220086760
BHORKAR - US20220136846
SSB Index Marking for training data
PRASAD - US20220271851
TA marking for training data
MONDAL - US20190287031
SHEN - US20210167875
CellID marking for training data
[RA] KIM - WO2012115450
Response to Arguments
The following arguments in the Reply have been fully considered but they are not persuasive:
The Reply argues that the metrics of WANG_996 do not teach the features of claim 3 (now canceled), primarily in that WANG_996 fails to teach “wherein the type comprises line of sight and non-line of sight.” However, the Examiner notes that the feature in question requires a type that encompasses both “light of sight” (LOS) and “non-line of sight” (NLOS) – the phrasing is “wherein the type comprises line of sight and non-line of sight” not “wherein the type comprises line of sight or non-line of sight.” Consequently, with regards to LOS, the type cover all scenarios effectively making it moot. This is akin to “What type of food do you like?” and answering with “My type of food is Mexican and non-Mexican food” which is effectively covering all foods; an indication of any food would be included in the type. In this manner, wireless channel characteristics of WANG_996 is for a wireless channel and a wireless channel is implicitly at least one of LOS, NLOS or a hybrid of both and since the claimed type covers all scenarios, the characteristics indicates the claimed type.
In the interest in expediting prosecution, even if amendments changed the phrasing to “wherein the type comprises line of sight or non-line of sight” the Examiner performed an anticipatory search and has identified various references that teach the feature of claim 3 (now canceled). The Examiner believes these arts are sufficient to cover all five alternative embodiments such that it is the Examiner’s sentiment that claim 3 is not a novel feature.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE TACDIRAN whose telephone number is 571-272-1717. The examiner can normally be reached on M-TH, 10-5PM EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Rutkowski can be reached on 571-270-1215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
/ANDRE TACDIRAN/
Primary Examiner, Art Unit 2415
Prosecution Insights