CTFR 18/497,488 CTFR 96215 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on 03/05/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner. Examiner’s Note To help the reader, examiner notes in this detailed action claim language is in bold, strikethrough limitations are not explicitly taught and language added to explain a reference mapping are isolated from quotations via square brackets. Response to Arguments 07-37 AIA Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive. An explanation is provided below . Applicant alleges on p.13: That is, as discussed during the interview, on page 2071, LI discloses that edge devices generate a "local model" based on a "local data set," and the local model is then sent to the server. The Abstract of SINGH discusses updating a trained first machine learning model based on a transformation function based on a second machine learning model. As such, LI and SINGH do not disclose "transmit an indication of micro-Doppler measurements associated with a first artificial intelligence or machine learning (AI/ML) model, the indication of the micro-Doppler measurement comprising information identifying a set of micro-Doppler measurements," as recited in amended claim 1 ( emphasis added). LI transmits the local model to the server, and SINGH updates a first ML model based on a transformation function. For at least the foregoing reasons, Applicant submits that amended claim 1 is patentable over LI and SINGH. The Examiner respectfully disagrees. The claim does not require transmitting the raw micro-Doppler measurement data, but rather “ indication of the micro- Doppler measurement ”. Thus, even if Li’s edge device sends its trained local model to the server, the local model is indicates the micro-Doppler measurements. Applicant alleges on p.14: Additionally, Applicant respectfully submits that LI and SINGH do not disclose each and every feature recited in claim 13. For example, LI and SINGH do not disclose "wherein the micro-Doppler measurements comprise one or more of: an indication of a recognized object, an indication of a measurements associated with the first AI/ML model, or an indication of a confidence of the first AI/ML model for object recognition," as recited in claim 13 ( emphasis added). The Examiner respectfully disagrees. Li teaches ‘an indication of a recognized object’ from the above claim limitation list where only one item needs to be taught. See at least fig 2 of Li . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-8, 12-13, 15-16, 18-23, 26-27, 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. ( CSI-based Vehicle target recognition and Collaborative CNN with Data-Importance-Aware [NPL] hereinafter Li) in view of Singh et al. (US 20230316086 hereinafter Singh) . Regarding claim 1, Li teaches A wireless communication device for wireless communication, comprising (p.2069 “Ubiquitous wireless access of vehicles can be realized through installing wireless communication devices on roadsides, and vehicles”) : one or more memories; and one or more processors, coupled to the one or more memories (p.2070 “the distributed edge computing architecture by processing the micro Doppler characteristics of the target”) , configured to cause the wireless communication device to: transmit an indication of micro-Doppler measurements associated with a first artificial intelligence or machine learning (AI/ML) model (fig 2) , the indication of the micro- Doppler measurement comprising information identifying a set of micro-Doppler measurements (Li p.2071 “First, the edge device obtains channel state information through the intel 5300 NIC to generate a local data set of micro-Doppler characteristics. The edge device aggregates the local model according to the local data set, and the model is received from other devices and sends the trained model to the server”) ; receive, in association with transmitting the indication of the micro-Doppler measurements, an indication of a second AI/ML model that is an update of the first AI/ML model (p.2072 “In distributed ML, the contribution of the local data set of each device to the update of the ML model can be regarded as the importance of the device data set”) ; and transmit, in connection with using the second AI/ML model, an indication associated with object recognition (Abstract “A distributed collaborative machine learning method of DIA is proposed to realize the target recognition of the vehicle”; p.2069 “We propose a cooperative machine learning method with DIA, which can recognize different road traffic participants (vehicles, pedestrians, bicycles) in the distributed edge computing architecture by processing the micro Doppler characteristics of the target.”) . Li does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Singh teaches a second AI/ML model that is an update of the first AI/ML model (Abstract “The electronic device updates the trained first machine learning model, based on the application of the transformation function. The update of the trained first machine learning model corresponds to an unlearning of at least one of the received data subset or a set of features associated with the second machine learning model.”). Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Singh with the teachings of Li. One would have been motivated to do so in order to advantageously improve the ML model (Singh 0045). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Singh merely teaches that it is well-known to incorporate the particular ML features. Since both Singh and Li disclose similar ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 2, the cited prior art teaches The wireless communication device of claim 1, wherein the first AI/ML model is associated with object recognition (Li Abstract “a micro Doppler feature data set. A distributed collaborative machine learning method of DIA is proposed to realize the target recognition of the vehicle”) . Regarding claim 3, the cited prior art teaches The wireless communication device of claim 1, the object recognition is based at least in part on updated micro-Doppler measurements that are updated relative to the micro-Doppler measurements based at least in part on a format for input to the second AI/ML model (Singh 0027 “The parameters of the ML model may be tuned and weights may be updated so as to move towards a global minimum of a cost function for the ML model. After several epochs of the training on feature information in the training dataset, the ML model may be trained to output a prediction/classification result for a set of inputs. The prediction result may be indicative of a class label for each input of the set of inputs (e.g., input features extracted from new/unseen instances).”) . Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Singh with the teachings of Li. One would have been motivated to do so in order to advantageously improve the ML model (Singh 0045). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Singh merely teaches that it is well-known to incorporate the particular ML features. Since both Singh and Li disclose similar ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 4, the cited prior art teaches The wireless communication device of claim 1, wherein the indication of the second AI/ML model comprises one or more of: an indication of one or more of an input format or an output format, an indication of weights applicable to nodes of the second AI/ML model, an indication of one or more layers to add to the second AI/ML model (Singh 0027 “Each ML model of the first ML model 110 and the second ML model 112 may be defined by its hyper-parameters, for example, number of weights, cost function, input size, number of layers, and the like. The parameters of the ML model may be tuned and weights may be updated so as to move towards a global minimum of a cost function for the ML model.”) , an indication of one or more parameters of the one or more layers to add, an indication of an update to a convolution kernel, an indication of a pooling operation, or an indication of an activation function. Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Singh with the teachings of Li. One would have been motivated to do so in order to advantageously improve the ML model (Singh 0045). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Singh merely teaches that it is well-known to incorporate the particular ML features. Since both Singh and Li disclose similar ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 5, the cited prior art teaches The wireless communication device of claim 1, wherein the indication associated with the object recognition comprises one or more of: an indication of a recognized object, an indication of measurements associated with the second AI/ML model, or an indication of a substructure of the second AI/ML model (Li Abstract “Experimental results show that the vehicle recognition accuracy of the proposed method is higher than 95%, and the convergence speed of the machine learning model is improve”) . Regarding claim 6, the cited prior art teaches The wireless communication device of claim 5, wherein the measurements associated with the second AI/ML models may include an intermediate layer of AI/ML model-based object recognition to be completed by a network node (Li Fig. 4 “Neural network structure”) . Regarding claim 7, the cited prior art teaches The wireless communication device of claim 6, wherein the network node comprises an additional wireless communication device or a computing device (Li fig 3 [edge devices]) . Regarding claim 8, the cited prior art teaches The wireless communication device of claim 5, wherein the indication of the update to the first AI/ML models comprises one or more of: an update to the substructure, or an indication of a position of the substructure within the first AI/ML model (Singh 0027 “Each ML model of the first ML model 110 and the second ML model 112 may be defined by its hyper-parameters, for example, number of weights, cost function, input size, number of layers, and the like. The parameters of the ML model may be tuned and weights may be updated so as to move towards a global minimum of a cost function for the ML model.” [corresponds to an update to the substructure]) . Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Singh with the teachings of Li. One would have been motivated to do so in order to advantageously improve the ML model (Singh 0045). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Singh merely teaches that it is well-known to incorporate the particular ML features. Since both Singh and Li disclose similar ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 12, the cited prior art teaches The wireless communication device of claim 1, wherein one or more of the first AI/ML model or the second AI/ML model is associated with one or more of: one or more supported micro-Doppler spectra (Li fig 2 [micro-doppler spectra as an input to the CNN]) , a supported number of paths associated with the one or more supported micro-Doppler spectra, or a threshold of one or more of supported delay spreads, Doppler shifts, angles of arrival, or signal strengths. Regarding claim 13, the cited prior art teaches The wireless communication device of claim 1, wherein the micro-Doppler measurements comprise one or more of: an indication of a recognized object, an indication of a measurements associated with the first AI/ML model, or an indication of a confidence of the first AI/ML model for object recognition (Li Abstract “Experimental results show that the vehicle recognition accuracy of the proposed method is higher than 95%, and the convergence speed of the machine learning model is improve”) . Regarding claim 15, the cited prior art teaches The wireless communication device of claim 1, wherein the one or more processors are further configured to cause the wireless communication device to transmit one or more of: an indication of available storage for the second AI/ML model, an indication of identified portions of the first AI/ML model for updating, an indication of one or more parameters of the object recognition (Li p.2072 “Because devices cannot share the raw data, it is necessary to reversely derive the importance of each device’s data according to the model parameters uploaded by the device and assign weights accordingly. The DIA algorithm we proposed is based on two fundamental concepts for weight calculation. (1) Variability. If the parameters of the uploaded model fluctuate more, the faster the model converges and the greater the importance of the data”) , an indication of a request for further sensing in association with an output of the first AI/ML model or the second AI/ML model. Regarding claim 16 , claim 16 recites substantially the same limitations as claim 1 and therefore rejected for substantially the same reasons as claim 1 . Regarding claim 17, the cited prior art teaches The wireless communication device of claim 16, wherein the one or more processors are further configured to cause the wireless communication device to: receive, in connection with use of the second AI/ML model, an indication associated with object recognition (Li p.2070 “The framework of the proposed vehicle target recognition and vehicle speed measurement system is shown in Fig.2. The system contains two modules. Vehicle target recognition module”) . Regarding claim 18 , claim 18 recites substantially the same limitations as claim 2 and therefore rejected for substantially the same reasons as claim 2 . Regarding claim 19 , claim 19 recites substantially the same limitations as claim 3 and therefore rejected for substantially the same reasons as claim 3 . Regarding claim 20 , claim 20 recites substantially the same limitations as claim 4 and therefore rejected for substantially the same reasons as claim 4 . Regarding claim 21 , claim 21 recites substantially the same limitations as claim 5 and therefore rejected for substantially the same reasons as claim 5 . Regarding claim 22 , claim 22 recites substantially the same limitations as claim 6 and therefore rejected for substantially the same reasons as claim 6 . Regarding claim 23 , claim 23 recites substantially the same limitations as claim 8 and therefore rejected for substantially the same reasons as claim 8 . Regarding claim 26 , claim 26 recites substantially the same limitations as claim 12 and therefore rejected for substantially the same reasons as claim 12 . Regarding claim 27 , claim 27 recites substantially the same limitations as claim 13 and therefore rejected for substantially the same reasons as claim 13 . Regarding claim 29 , claim 29 recites substantially the same limitations as claim 1 and therefore rejected for substantially the same reasons as claim 1 . Regarding claim 30 , claim 30 recites substantially the same limitations as claim 1 and therefore rejected for substantially the same reasons as claim 1 . 07-21-aia AIA Claim (s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. ( CSI-based Vehicle target recognition and Collaborative CNN with Data-Importance-Aware [NPL] hereinafter Li) in view of Singh et al. (US 20230316086 hereinafter Singh) and further in view of Ishii et al. (US-20220398766 hereinafter Ishii) . Regarding claim 9, the cited prior art teaches The wireless communication device of claim 1, wherein the one or more processors are further configured to cause the wireless communication device to: identify a recognized object in association with an output of the second AI/ML model . The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Ishii teaches wherein the one or more processors are further configured to cause the wireless communication device to: identify a recognized object in association with an output of the second AI/ML model (Abstract “a machine learning model that performs a deblurring process of an input image and outputs a feature quantity and a second model which is a machine learning model that performs an object recognition process of the input image and outputs a result of the object recognition are connected so that an output of the first model becomes an input of the second model;”). Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Ishii with the cited prior art. One would have been motivated to do so in order to advantageously improve the recognition accuracy (Ishii 0004). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Ishii merely teaches that it is well-known to incorporate the particular AI features. Since both the cited prior art and Ishii disclose similar AI/ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results . 07-21-aia AIA Claim (s) 10-11, 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. ( CSI-based Vehicle target recognition and Collaborative CNN with Data-Importance-Aware [NPL] hereinafter Li) in view of Singh et al. (US 20230316086 hereinafter Singh) and further in view of Tian et al. (US 20250261163 hereinafter Tian) . Regarding claim 10, the cited prior art teaches The wireless communication device of claim 1, wherein the one or more processors are further configured to cause the wireless communication device to: transmit an indication of a third AI/ML model, the third AI/ML model being an update to the second AI/ML model, in association with local model training at the wireless communication device . The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Tian teaches wherein the one or more processors are further configured to cause the wireless communication device to: transmit an indication of a third AI/ML model, the third AI/ML model being an update to the second AI/ML model, in association with local model training at the wireless communication device (0017 “The first network element determines, based on the measurement result of the first parameter and the correspondence, that a second-type AI model used for positioning is to be switched or updated from a third AI model to a fourth AI model, where the channel measurement result is an input of a first-type AI model used for positioning, the assisted positioning information is an output of the first-type AI model, the first-type AI model is switched or updated from a first AI model corresponding to the third AI model to a second AI model corresponding to the fourth AI model”). Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Tian with the cited prior art. One would have been motivated to do so in order to advantageously improve the AI model (Tian Abstract). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Tian merely teaches that it is well-known to incorporate the particular AI features. Since both the cited prior art and Tian disclose similar AI/ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 11, the cited prior art teaches The wireless communication device of claim 10, wherein the indication of the third AI/ML model comprises: an indication of one or more parameters of the third AI/ML model (Tian 0004 “This application provides a communication method and a communication apparatus, to determine, based on a channel measurement result, whether to switch or update an AI model used for positioning, to improve positioning precision of the AI model.”) , the third AI/ML model being associated with an identified object or one or more parameters of the identified object (Li 2070 “The framework of the proposed vehicle target recognition and vehicle speed measurement system is shown in Fig.2.”) . Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Tian with the cited prior art. One would have been motivated to do so in order to advantageously improve the AI model (Tian Abstract). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Tian merely teaches that it is well-known to incorporate the particular AI features. Since both the cited prior art and Tian disclose similar AI/ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 24 , claim 24 recites substantially the same limitations as claim 10 and therefore rejected for substantially the same reasons as claim 10 . Regarding claim 25 , claim 25 recites substantially the same limitations as claim 11 and therefore rejected for substantially the same reasons as claim 11 . 07-21-aia AIA Claim (s) 10-11, 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. ( CSI-based Vehicle target recognition and Collaborative CNN with Data-Importance-Aware [NPL] hereinafter Li) in view of Singh et al. (US 20230316086 hereinafter Singh) and further in view of Fincannon et al. (US 20250056111 hereinafter Fincannon) . Regarding claim 14, the cited prior art teaches The wireless communication device of claim 13, wherein the indication of the update to the first AI/ML model comprises an amount of updating that is associated with the confidence of the first AI/ML model for object recognition . The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Fincannon teaches wherein the indication of the update to the first AI/ML model comprises an amount of updating that is associated with the confidence of the first AI/ML model for object recognition (Abstract “determining a set of external factors based on the image and/or information from a second sensor, linking the set of imaging parameters and the set of external factors to the object recognition confidence score, and training a predictive model for updating the set of imaging parameters using the object recognition confidence score, the set of imaging parameters, and the set of external factors.”). Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Fincannon with the cited prior art. One would have been motivated to do so in order to advantageously improve object recognition (Fincannon 0046). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Fincannon merely teaches that it is well-known to incorporate the particular AI features. Since both the cited prior art and Fincannon disclose similar AI/ML models using data processing, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results. Regarding claim 28 , claim 28 recites substantially the same limitations as claim 14 and therefore rejected for substantially the same reasons as claim 14 . Conclusion 07-40 AIA 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. 07-96 The prior art made of record and not relied upon is considered pertinent to application’s disclosure: Lien et al. (US 20220019291) discloses “Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture. (See abstract)” Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISMAAEEL A SIDDIQUEE whose telephone number is (571)272-3896. The examiner can normally be reached on Monday-Friday 8am-5pm. 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SIDDIQUEE/ Examiner, Art Unit 3648 /TIMOTHY A BRAINARD/Primary Examiner, Art Unit 3648 Application/Control Number: 18/497,488 Page 2 Art Unit: 3648 Application/Control Number: 18/497,488 Page 3 Art Unit: 3648 Application/Control Number: 18/497,488 Page 4 Art Unit: 3648 Application/Control Number: 18/497,488 Page 5 Art Unit: 3648 Application/Control Number: 18/497,488 Page 6 Art Unit: 3648 Application/Control Number: 18/497,488 Page 7 Art Unit: 3648 Application/Control Number: 18/497,488 Page 8 Art Unit: 3648 Application/Control Number: 18/497,488 Page 9 Art Unit: 3648 Application/Control Number: 18/497,488 Page 10 Art Unit: 3648 Application/Control Number: 18/497,488 Page 11 Art Unit: 3648 Application/Control Number: 18/497,488 Page 12 Art Unit: 3648 Application/Control Number: 18/497,488 Page 13 Art Unit: 3648 Application/Control Number: 18/497,488 Page 14 Art Unit: 3648 Application/Control Number: 18/497,488 Page 15 Art Unit: 3648 Application/Control Number: 18/497,488 Page 16 Art Unit: 3648 Application/Control Number: 18/497,488 Page 17 Art Unit: 3648 Application/Control Number: 18/497,488 Page 18 Art Unit: 3648 Application/Control Number: 18/497,488 Page 19 Art Unit: 3648
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