CTNF 18/663,470 CTNF 81709 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. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-12-aia AIA (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. 07-15-03-aia AIA Claim(s) 1-11 and 13-24 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Naik et al. (Naik), U.S. Publication No. 2024/0154912 . Regarding Claims 1 and 13, Naik discloses an access device (i.e., a device (e.g., a station (STA) or an access point (AP)); see paragraph [0090]) , comprising: at least one processor (i.e., processor 1440; see figure 14) ; and at least one memory (i.e., memory 1430; see figure 14) storing instructions that, when executed by the at least one processor, cause the access device at least to: transmit, to a computing device, traffic statistics data related to traffic passing through the access device based on at least one statistics data model for statistics data (i.e., the device 205- a (e.g., an AP) may offer downloadable identification or classification models to the device 205- c (e.g., or one or more other associated non-AP STAs) that support machine learning and the traffic classification. In such an example, the device 205- a may receive a request 230 for information associated with the machine learning model to be used at the device 205- c for classifying traffic. In response, the device 205- a may transmit a message indicating machine learning model information 235 to the device 205- c .; see paragraph [0123]) ; receive, from the computing device, traffic configuration information for the traffic based on at least one configuration data model for traffic configuration, wherein the traffic configuration information is determined by utilizing an artificial intelligence (AI)/machine learning (ML) model to process the traffic statistics data (i.e., The device 205- c may use the machine learning model information 235 to obtain (e.g., build, construct, updated) the machine learning model for classifying traffic. For example, the device 205- c may identify a traffic class associated with the signaling 210- b . In such an example, the device 205- c (e.g., a non-AP STA, such as a client device) may perform one or more operations in accordance with the identified traffic class.; see paragraph [0124]) ; and configure the traffic based on the traffic configuration information (i.e., For example, the device 205- a may determine to communicate with the device 205- a in accordance with (or based on) the identified traffic class.; see paragraphs [0124]) . Also, see paragraphs [0117]-[0118] and [0121]. Regarding Claims 2 and 24, Naik discloses wherein the access device is caused to configure the traffic by: controlling at least one of quality of service (QoS) (see paragraph [0126]) , bandwidth allocation, or traffic scheduling based on the traffic configuration information. Regarding Claims 3 and 15, Naik discloses wherein the at least one statistics data model and the at least one configuration data model are applied to at least one of a media access control (MAC) layer (see paragraph [0101]) , an Internet protocol (IP) layer, or a transport layer. Regarding Claims 4 and 16, Naik discloses wherein the access device is further caused to: transmit a request message to the computing device to request that the computing device transmit the traffic configuration information to the access device (see paragraph [0168]) . Regarding Claims 5 and 17, Naik discloses wherein the access device is further caused to: perform a part of computation associated with utilizing the AI/ML model to process the traffic statistics data, wherein the other part of the computation is performed by the computing device (see paragraph [0128]) . Regarding Claims 6 and 18, Naik discloses wherein the at least one statistics data model comprises at least one of the following: a first statistics data model for MAC layer connection information (see paragraph [0128]) ; a second statistics data model for IP layer connection information; a third statistics data model of a statistics data list of MAC layer connection for the MAC layer connection information; or a fourth statistics data model of a statistics data list of IP layer connection for the MAC layer connection information. Regarding Claims 7 and 19, Naik discloses wherein the at least one configuration data model comprises at least one of the following: a first configuration data model for MAC layer control information; a second configuration data model for IP layer control information (see paragraph [0126]) ; a third configuration data model of a MAC layer control list for the MAC layer control information; or a fourth configuration data model of an IP layer control list for the MAC layer control information. Regarding Claims 8 and 20, Naik discloses wherein the first statistics data model comprises at least one of the following: a label associated with the MAC layer connection; a source MAC address associated with the MAC layer connection; a virtual local area network (VLAN) number associated with the MAC layer connection; a destination MAC address associated with the MAC layer connection; a priority associated with the MAC layer connection (see paragraph [0128]) ; a setup time associated with the MAC layer connection; a sustain time associated with the MAC layer connection; or a statistics timestamp associated with the MAC layer connection. Regarding Claims 9 and 21, Naik discloses wherein the second statistics data model comprises at least one of the following: a label associated with the IP layer connection; an IP version associated with the IP layer connection; a transport protocol associated with the IP layer connection; a source IP address associated with the IP layer connection (see paragraph [0126]) ; a source port number associated with the IP layer connection; a destination IP address associated with the IP layer connection; a destination port number associated with the IP layer connection; a setup time associated with the IP layer connection; a sustain time associated with the IP layer connection; or a statistics timestamp associated with the IP layer connection. Regarding Claims 10 and 22, Naik discloses wherein the first configuration data model comprises at least one of the following: a label associated with MAC layer connection; a VLAN number associated with the MAC layer connection; or a priority associated with the MAC layer connection (see paragraph [0128]) . Regarding Claims 11 and 23, Naik discloses wherein the second configuration data model comprises at least one of the following: a label associated with the IP layer connection; a VLAN number associated with the IP layer connection; a priority associated with the IP layer connection; or a differentiated service code point (DSCP) associated with the IP layer connection (see paragraph [0126]) . Regarding Claim 14, Naik discloses wherein the computing device is caused to determine the traffic configuration information by: utilizing the AI/ML model to determine at least one of network topology information, traffic distribution information, traffic allocation information, and traffic pattern information based on the traffic statistics data; and utilizing the AI/ML model to determine the traffic configuration information based on the at least one of the network topology information, the traffic distribution information, the traffic allocation information, and the traffic pattern information (see paragraphs [0124]- [0125]) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANTELL LAKETA HEIBER whose telephone number is (571)272-0886. The examiner can normally be reached on M-F from 9am to 5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy, can be reached at telephone number 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /SHANTELL L HEIBER/Primary Examiner, Art Unit 2645 April 10, 2026 Application/Control Number: 18/663,470 Page 2 Art Unit: 2645 Application/Control Number: 18/663,470 Page 3 Art Unit: 2645 Application/Control Number: 18/663,470 Page 4 Art Unit: 2645 Application/Control Number: 18/663,470 Page 5 Art Unit: 2645 Application/Control Number: 18/663,470 Page 6 Art Unit: 2645 Application/Control Number: 18/663,470 Page 7 Art Unit: 2645
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