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 communications filed on 4/15/2026.
Claims 1-30 are pending.
DETAILED ACTION
Response to Arguments
Applicant's arguments filed 4/15/2026 have been fully considered but they are not persuasive. In the response filed, applicant argues, in substance:
a) In page 10 of the response filed, applicant argues that Moosavi et al. (US 20250175393 A1) fails to disclose the claimed limitations because claim 1 recites “receiving one or more configurations indicating transmission of gradient information using mapping information” where the mapping information indicates, for example, “codebooks” (as shown by paragraphs 95-96, 109, 112-113, and 130 of the specification) or, alternatively, “how to quantize” (page 10, line 14 of remarks).
In response to argument (a), the examiner respectfully disagrees.
It is noted that the features upon which applicant relies (i.e., “codebooks”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
While the specification does provide examples on what the claimed “mapping information” may be, none of those examples are provided as a set definition for the term “mapping information”. Therefore, the term “mapping information” in the claims is broad enough to cover any mapping information related to “one or more configurations” for the transmission of gradient information.
Moosavi is directed to a system for "training a shared global learning model by exchanging model updates" (¶[0002]). To perform this, a "cluster head aggregates gradients from the agent entities" (¶[0061]). Each agent entity "transmits to the server entity 200 their model update" (¶[0051]).
Prior to the transmission of the model updates (or gradients) in Moosavi, the “server entity 200 configures the agent entities” (¶[0065], underline for emphasis) by indicating to the agent entities how to transmit the gradients (“use over-the-air transmission with direct analog modulation for communicating local updates of the iterative learning process to the cluster head 120a:120c” (¶[0066]).
That is, the server entity 200 transmits mapping information to the agent entities, the mapping information indicating (or “mapping”) transmission of gradients towards a cluster head.
b) In page 10 of the response filed, applicant argues that Moosavi et al. (US 20250175393 A1) fails to teach the claimed limitations because in Moosavi “only “cluster head” can perform digital transmission, but it does not transmit its own local gradient”.
In response to argument (b), the examiner respectfully disagrees.
Initially, the examiner notes that the limitations recite “the transmission type indicates an analog transmission or a digital transmission of the gradient information” (underline for emphasis) and “transmitting the signal to a network entity […] based on the transmission type”. That is, the limitation is met by a reference that teaches either an indication of analog transmission or an indication of digital transmission, but not necessarily both.
Further, the claims don’t require the UE to transmit its own gradient (i.e., a gradient derived by the UE itself), instead the claim recites “generating a signal including quantized gradient information based on the mapping information and a set of gradients”.
Therefore, the claim limitation “the transmission type indicates an analog transmission” is met by Moosavi, because in Moosavi the gradient information is transmitted from each UE (agent entity) to a network entity (cluster head) according to the mapping information (¶[0065]-[0066]) and transmission type (¶[0066], analog).
Further, while not required due to the alternate nature of the word “or”, Moosavi also teaches “the transmission type indicates a digital transmission of the gradient information” because the cluster heads transmit a digital channel to transmit aggregated local updates to the server, where none of the limitations require the gradient information to be derived by the UE itself.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1 and 10 is/are rejected under 35 U.S.C. 102(A)(2) as being anticipated by Moosavi et al. (US 20250175393 A1, hereinafter Moosavi).
Regarding claim 1, Moosavi discloses a method of wireless communication at a user equipment (UE) (¶[0044], "Each of the user equipment 170a:170c comprises, is collocated with, or integrated with, a respective agent entity"), comprising:
receiving one or more configurations indicating transmission of gradient information using mapping information (¶[0049], "The server entity 200 broadcasts the current parameter vector of the learning model, θ(i), to the agent entities 300a, 300b"; ¶[0050], "Each agent entity 300a, 300b performs a local optimization of the model by running T steps of a stochastic gradient descent update on θ(i)"; ¶[0065], "The server entity 200 configures the agent entities 300b, 300c to, as part of performing the iterative learning process:" [0066] "use over-the-air transmission with direct analog modulation for communicating local updates of the iterative learning process to the cluster head 120a:120c"; ¶[0067], "The server entity 200 configures the cluster head 120a:120c of each cluster 110a:110c to, as part of performing the iterative learning process:" ¶[0068] "aggregate the local updates received from the agent entities 300b, 300c within its cluster 110a:110c, and" ¶[0069] "use unicast digital transmission for communicating aggregated local updates to the server entity 200") and
a transmission type, wherein the transmission type indicates an analog transmission or a digital transmission of the gradient information (¶[0013], "The agent entity acts as a cluster head of a cluster of agent entities. The method comprises receiving configuration from the server entity. According to the configuration, the agent entity is to, as part of performing the iterative learning process, aggregate local updates received in over-the-air transmission with direct analog modulation from the agent entities within the cluster and to use unicast digital transmission for communicating the aggregated local updates to the server entity"; ¶[0066], "use over-the-air transmission with direct analog modulation for communicating local updates"; ¶[0067], "use unicast digital transmission for communicating aggregated local updates to the server entity 200") and
the gradient information is associated with training a global machine learning model (¶[0002], "multiple (possible very large number of) agents, for example implemented in user equipment, participate in training a shared global learning model by exchanging model updates with a centralized parameter server");
generating a signal including quantized gradient information based on the mapping information and a set of gradients (¶[0051], "Each agent entity 300a, 300b transmits to the server entity 200 their model update […] where θk (i, 0) is the model that agent entity k received from the server entity" - generating inherent),
wherein the set of gradients is associated with the training of the global machine learning model with local training data at the UE (¶[0002], "multiple (possible very large number of) agents, for example implemented in user equipment, participate in training a shared global learning model by exchanging model updates with a centralized parameter server"); and
transmitting the signal to a network entity via the analog transmission or the digital transmission based on the transmission type (¶[0051], "Each agent entity 300a, 300b transmits to the server entity 200 their model update […] where θk (i, 0) is the model that agent entity k received from the server entity").
Regarding claim 10, Moosavi discloses an apparatus for wireless communication at a user equipment (UE), comprising: a memory; and a processor coupled with the memory (¶[0147]-[0148]).
The remaining limitations of claim 10 are similar in scope to those of claim 1. Therefore, claim 10 is rejected for the same reasons as set forth in the rejection of claim 1, above.
Claim(s) 21-22 is/are rejected under 35 U.S.C. 102(A)(2) as being anticipated by Pezeshki et al. (US 20220124779 A1, hereinafter Pezeshki).
Regarding claim 21, Pezeshki discloses a method of wireless communication at a network entity (¶[0072], "base station"), comprising:
generating a first set of configurations for a first set of user equipments (UEs) indicating digital transmission of first gradient information using first mapping information (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration. The federated learning configuration may include an indication of a periodic communication scheme for communicating with the base station 410 to facilitate federated learning associated with a machine learning component" - generating inherent; ¶[0073], "the periodic communication scheme may include an SPS configuration for downloading global updates associated with the machine learning component from the base station 410 and/or a configured grant configuration for uploading, to the base station 410, local updates associated with the machine learning component. For example, an SPS configuration may allocate periodic resources intended for transmissions of transport blocks carrying global updates. The periodic resources may include time domain resources, frequency domain resources, and/or spatial domain resources, among other resources. Dynamic scheduling may be used to allocate resources for any re-transmissions"; ¶[0074], "the configured grant configuration may configure digital transmissions of gradient vectors from UEs to the base station");
generating a second set of configurations for a second set of UEs indicating analog transmission of second gradient information using second mapping information (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration. The federated learning configuration may include an indication of a periodic communication scheme for communicating with the base station 410 to facilitate federated learning associated with a machine learning component" - generating inherent; ¶[0073], "the periodic communication scheme may include an SPS configuration for downloading global updates associated with the machine learning component from the base station 410 and/or a configured grant configuration for uploading, to the base station 410, local updates associated with the machine learning component. For example, an SPS configuration may allocate periodic resources intended for transmissions of transport blocks carrying global updates. The periodic resources may include time domain resources, frequency domain resources, and/or spatial domain resources, among other resources. Dynamic scheduling may be used to allocate resources for any re-transmissions"; ¶[0074], "the configured grant configuration may configure analog over-the-air aggregation of gradient vectors. In this case, the base station may configure each UE with the same resources"; ¶[0075], "As an example, a first set of resources (e.g., a first set of symbols and/or slots) may be allocated for transmitting a first global update, a second set of resources may be allocated for transmitting a second global update, a third set of resources may be allocated for transmitting a third global update, and so on. Each set of resources may occur in accordance with a periodicity of the SPS configuration"); and
transmitting the first set of configurations to the first set of UEs and the second set of configurations to the second set of UEs (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration").
Regarding claim 22, Pezeshki discloses an apparatus for wireless communication at a network entity, comprising: a memory; and a processor coupled with the memory (¶[0011], "a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a base station") and configured to:
generate a first set of configurations for a first set of user equipments (UEs) indicating digital transmission of first gradient information using first mapping information (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration. The federated learning configuration may include an indication of a periodic communication scheme for communicating with the base station 410 to facilitate federated learning associated with a machine learning component" - generating inherent; ¶[0073], "the periodic communication scheme may include an SPS configuration for downloading global updates associated with the machine learning component from the base station 410 and/or a configured grant configuration for uploading, to the base station 410, local updates associated with the machine learning component. For example, an SPS configuration may allocate periodic resources intended for transmissions of transport blocks carrying global updates. The periodic resources may include time domain resources, frequency domain resources, and/or spatial domain resources, among other resources. Dynamic scheduling may be used to allocate resources for any re-transmissions"; ¶[0074], "the configured grant configuration may configure digital transmissions of gradient vectors from UEs to the base station");
generate a second set of configurations for a second set of UEs indicating analog transmission of second gradient information using second mapping information (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration. The federated learning configuration may include an indication of a periodic communication scheme for communicating with the base station 410 to facilitate federated learning associated with a machine learning component" - generating inherent; ¶[0073], "the periodic communication scheme may include an SPS configuration for downloading global updates associated with the machine learning component from the base station 410 and/or a configured grant configuration for uploading, to the base station 410, local updates associated with the machine learning component. For example, an SPS configuration may allocate periodic resources intended for transmissions of transport blocks carrying global updates. The periodic resources may include time domain resources, frequency domain resources, and/or spatial domain resources, among other resources. Dynamic scheduling may be used to allocate resources for any re-transmissions"; ¶[0074], "the configured grant configuration may configure analog over-the-air aggregation of gradient vectors. In this case, the base station may configure each UE with the same resources"; ¶[0075], "As an example, a first set of resources (e.g., a first set of symbols and/or slots) may be allocated for transmitting a first global update, a second set of resources may be allocated for transmitting a second global update, a third set of resources may be allocated for transmitting a third global update, and so on. Each set of resources may occur in accordance with a periodicity of the SPS configuration"); and
transmit the first set of configurations to the first set of UEs and the second set of configurations to the second set of UEs (¶[0072], "base station 410 may transmit, and the UE 405 may receive, a federated learning configuration").
Allowable Subject Matter
Claims 2-9, 11-20, and 23-30 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.
Conclusion
THIS ACTION IS MADE FINAL. 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 BORIS D GRIJALVA LOBOS whose telephone number is (571)272-0767. The examiner can normally be reached M-F 10:30AM to 6:30PM EST.
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/BORIS D GRIJALVA LOBOS/ Primary Patent Examiner, Art Unit 2446
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