CTNF 18/558,996 CTNF 92068 DETAILED ACTION This office action is responsive to the above identified application filed 11/3/2023. The application contains claims 17-30, all examined and rejected. 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. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The Information Disclosure Statement with references submitted 9/19/2024, 3/17/2025 have been considered and entered into the file. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 21 and 28 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 21 disclose and/or multiple times that it is unclear if the claim require one or more of the claimed alternatives. For examiner purposes examiner considered that the claim require at least one of the alternatives. Claim 28 disclose and/or multiple times that it is unclear if the claim require one or more of the claimed alternatives. For examiner purposes examiner considered that the claim require at least one of the alternatives. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 17-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 17 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 17 and 24 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process). Claims 17-30 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include machine and process, and as in independent Claim 17 and 24, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, The claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: “process, information on aspects of a machine learning algorithm to be used in an ununiform and/or distributed-manner operating radio communication network”, “examine information on access units in the radio communication network”, “ select at least one access unit among the access units to which the machine learning algorithm is to be delivered or from which the machine learning algorithm is to be requested based on the aspects of the machine learning algorithm and on the information on the access units for the machine learning algorithm being used in the ununiform and/or distributed-manner operating radio communication network in a centrally controlled manner” (Mental process, observation, evaluation and judgment). The claim recites additional elements as “An apparatus comprising: at least one processor and at least one memory including a computer program code, the at least one memory and the computer program code configured to, with the at least one processor” “central network unit” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “wherein the aspects are characterizing to usage of the machine learning algorithm”, “wherein the information is associated with the location and/or capabilities of the access units” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), “transmit the machine learning algorithm and/or the information on aspects of a machine learning algorithm to the selected at least one access unit or request the machine learning algorithm from the selected at least one access unit” (insignificant extra-solution activity, MPEP 2106.05(g)). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: “An apparatus comprising: at least one processor and at least one memory including a computer program code, the at least one memory and the computer program code configured to, with the at least one processor” “central network unit” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “wherein the aspects are characterizing to usage of the machine learning algorithm”, “wherein the information is associated with the location and/or capabilities of the access units” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), “transmit the machine learning algorithm and/or the information on aspects of a machine learning algorithm to the selected at least one access unit or request the machine learning algorithm from the selected at least one access unit” (well-understood, routine, or conventional, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). In the instant case, Claim 17 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. Independent claim 24 are the same analogy and rejected using similar analysis as claim 1. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. claims 18 disclose “wherein the information on aspects of the machine learning algorithm comprise validity time, validity area, storage location, information on application layer, needed computing power, needed computing speed, identity information of the machine learning algorithm, status of the machine learning algorithm, and/or interest or ability of the access units to use the machine learning algorithm, wherein the information is available as metadata of the machine learning algorithm or with the machine learning algorithm as trained.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 19 disclose “wherein the access units comprise distributed units, other central network units and/or user devices.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 20 disclose “causing the apparatus to request training update for the machine learning algorithm for renewing the validity time or changing the validity area” (mental process) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 21 disclose “wherein the capabilities of the access units comprise computing power, number and/or location of radio units under the control of the access units, mobility, radio coverage, and/or support for executing the machine learning algorithm and/or interest for receiving the machine learning algorithm” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 22 disclose “comprising causing the apparatus to form a cluster of a part of the access units and carrying out the transmitting the machine learning algorithm to a head of the cluster or the requesting the machine learning algorithm from the head of the cluster” (mental process) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 23 disclose “wherein the processing of the information on the aspects of the machine learning algorithm and/or the examining information on access units comprises processing information stored to be available to the central network unit and/or requesting the information from the access units” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 17; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 18-23, 25-30,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101. Claim Rejections - 35 USC § 102 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 (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. 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 17-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by OTTERSTEN et al. [US 2021/0345134 A1, hereinafter D1] . With regard to Claim 17, D1 teach a apparatus comprising: at least one processor and at least one memory including a computer program code, the at least one memory and the computer program code configured to, with the at least one processor (Fig. 4B, 418, 419, 300, ¶¶30-32) , cause the apparatus at least to: process, by a central network unit, information on aspects of a machine learning algorithm , to be used in an ununiform and/or distributed-manner operating radio communication network, wherein the aspects are characterizing to usage of the machine learning algorithm (¶14, “The wireless communications system comprises a central network node and one or more intermediate network nodes arranged between the central network node and one or more leaf network operating in the wireless communications network …”, ¶16, “ the wireless communications system performs, based on the determined prediction, one or more operations relating to the at least one network node and transmits the determined prediction and/or information relating to the machine learning model to one or more other network nodes”, ¶58, “By the use of extra signalling, information about the model type, e.g. the type of the machine learning model, and a prediction of a performance of a network node may be exchanged between the wireless device, different network nodes, and the cloud network node”, “ neural networks, decision trees, and random forests”, “signalling may be performed via a series of distributed, intermediate network nodes”, ¶209, “ML model messages comprising e.g. model descriptions (model types, structure description), model parameters, meta-data on what training data models are based on, message whether to use existing ML model in device or receive ML model from BS or repository, etc.“) ; examine information on access units in the radio communication network, wherein the information is associated with the location and/or capabilities of the access units (Fig. 6, “ML capability query”, “ML cap response”, ¶2, “The RAN covers a geographical area which is divided into service areas or cell areas, which may also be referred to as a beam or a beam group, with each service area or cell area … A service area or cell area is an area, e.g. a geographical area, where radio coverage is provided by the radio network node”, ¶58, “the location of the wireless device may be used to determine which of the machine learning models to use for the relevant predictions”, ¶81, “The physical architecture involves network nodes with sufficient computational, storage and communication capabilities for some level of machine learning”, ¶206, “Node information message comprising e.g. node ML model capabilities , node capabilities in terms of processing / learning and storage , types of training data available and needed , etc.”, ¶67, “a beam is associated with a more dynamic and relatively narrow and directional radio coverage compared to a conventional cell that is typically omnidirectional and/or provides more static radio coverage. A beam is typically formed and/or generated by beamforming and/or is dynamically adapted based on one or more recipients of the beam, such as one of more characteristics of the recipients, e.g. based on which direction a recipient is located”, ¶216, “The BS queries the UE/device about its ML capabilities [ML capability query] and the UE/device responds [ML capability response]”) ; select at least one access unit among the access units to which the machine learning algorithm is to be delivered or from which the machine learning algorithm is to be requested based on the aspects of the machine learning algorithm and on the information on the access units for the machine learning algorithm being used in the ununiform and/or distributed-manner operating radio communication network in a centrally controlled manner (Fig. 6, ¶58, “the location of the wireless device may be used to determine which of the machine learning models to use for the relevant predictions”, ¶74, “During a prediction mode, separate machine learning models may be used for each site or communications link. The machine learning model corresponding to a particular site or communications link may be updated based on data, such as ACK/NACK, from that site. Thereby, machine learning models optimized to the specific characteristic of the site are obtained”, ¶75, “By the term “site” when used in this disclosure is meant a location of a device radio network node”, ¶84, “computational capabilities and storage capabilities are provided higher in more central/higher nodes”, ¶89, “By the expression “suitable machine learning model” is meant a machine learning that have adequate learning capabilities given the computation and storage capabilities of the node where it resides”, ¶94, “clustering may be done in different ways. For example, the clustering may be based on geography, e.g. near neighbours may be clustered together. As another example, the clustering may be logical based on e.g., environment, traffic type, user needs”, ¶209, “ML model messages comprising e.g. model descriptions ( model types , structure description ) , model parameters , meta - data on what training data models are based on , message whether to use existing ML model in device or receive ML model from BS or repository , etc.”, ¶217, ¶231, “This will depend on the computational capability of the network node, the storage capacity and the current load. One of the benefits of having distributed network nodes, is the possibility of exchanging this type of information so a possible node for training and/or storing the prediction model may be identified”) , and transmit the machine learning algorithm and/or the information on aspects of a machine learning algorithm to the selected at least one access unit or request the machine learning algorithm from the selected at least one access unit (Fig. 6, ¶219, “The BS , e.g. the radio network node 110 , 111 , then 2 transmits a ML model suitable for the device's objective function and capabilities [ ML model transmission )“, ¶221,” After the refinement of the ML model ( s ) , the BS transmits the updated model to the device [ ML model transmission ] and to nodes concerned with clustered / global models related to the current device type and objective function ( s ) [ ML model trans mission ] . When the global model ( s ) has been refined , then the updated global model is distributed [ Global ML model update “, ¶223, “If the ML model is stored in the UE , the [ ML model transmission ] message indicates that the on - board model should be used , and which model to use if multiple models are available . If the most current model is not on - board , then the model parameters are transmitted in this message”) . With regard to Claim 18, D1 teach the apparatus of claim 17, wherein the information on aspects of the machine learning algorithm comprise validity time, validity area, storage location, information on application layer, needed computing power, needed computing speed, identity information of the machine learning algorithm, status of the machine learning algorithm, and/or interest or ability of the access units to use the machine learning algorithm (¶209, “ML model messages comprising e.g. model descriptions ( model types , structure description ) , model parameters , meta - data on what training data models are based on , message whether to use existing ML model in device or receive ML model from BS or repository , etc.”) , wherein the information is available as metadata of the machine learning algorithm or with the machine learning algorithm as trained (¶209, “meta - data on what training data models are based on”) . With regard to Claim 19, D1 teach the apparatus of claim 17, wherein the access units comprise distributed units, other central network units and/or user devices (¶96, “The wireless communications system 10 comprises a central network node 130, 201, 202 and one or more intermediate network nodes 110, 111, 130 arranged between the central network node 130, 201, 202 and one or more leaf network nodes 120, 122 operating in the wireless communications network 100”, ¶97, “the central network node may be the core network node 130, the external node 201 or a cloud network node 202. The one or more intermediate network nodes may be the first radio network node 110, the second radio network node 111, and/or the core network node 130. Further, the site-specific network node may be the first radio network node 110, the second radio network node 111, the first communications device 120, and/or the second communications device”, ¶98, “the one or more intermediate network nodes 110, 111, 130 may be distributed nodes”) . With regard to Claim 20, D1 teach the apparatus according any to claim 17, further comprising causing the apparatus to request training update for the machine learning algorithm for renewing the validity time (Fig. 6, “Training data collection req.”, “Training data trans”, “ML model re-training”, ¶221, BS then updates the ML model based on the received training data [ML model re-training]. After the refinement of the ML model(s), the BS transmits the updated model to the device [ML model transmission] and to nodes concerned with clustered/global models related to the current device type and objective function(s) [ML model transmission]. When the global model(s) has been refined, then the updated global model is distributed [Global ML model update]”, ¶223, “If the most current model is not on-board, then the model parameters are transmitted in this message” , validity time is the period during which the machine learning model is considered current and reliable for use before it needs retraining, refinement, replacement, or updated parameters ) or changing the validity area. With regard to Claim 21, D1 teach the apparatus according to claim 17, wherein the capabilities of the access units comprise computing power, number and/or location of radio units under the control of the access units, mobility, radio coverage, and/or support for executing the machine learning algorithm and/or interest for receiving the machine learning algorithm (¶81, “The physical architecture involves network nodes with sufficient computational, storage and communication capabilities for some level of machine learning”, ¶206, “Node information message comprising e.g. node ML model capabilities , node capabilities in terms of processing / learning and storage , types of training data available and needed , etc.”, ¶216, “The BS queries the UE/device about its ML capabilities [ML capability query] and the UE/device responds [ML capability response]”, ¶219, “The BS , e.g. the radio network node 110 , 111 , then 2 transmits a ML model suitable for the device's objective function and capabilities [ ML model transmission )“, ¶2) . With regard to Claim 22, D1 teach the apparatus of claim 17, further comprising causing the apparatus to form a cluster of a part of the access units and carrying out the transmitting the machine learning algorithm to a head of the cluster or the requesting the machine learning algorithm from the head of the cluster (Fig. 2B, “Intermediate ML Nodes”, “Site- Specific ML Nodes”, “Cluster head”, ¶227, “Similarly, the protocol may be used to exchange ML models between different BSs and cluster heads, assigning and reassigning BS to different clusters, select cluster heads and determine cluster-common learning objectives“) . With regard to Claim 23, D1 teach the apparatus of claim 17, wherein the processing of the information on the aspects of the machine learning algorithm and/or the examining information on access units comprises processing information stored to be available to the central network unit and/or requesting the information from the access units (¶206, “Node information message comprising e.g. node ML model capabilities , node capabilities in terms of processing / learning and storage , types of training data available and needed , etc.”, ¶216, “The BS queries the UE/device about its ML capabilities [ML capability query] and the UE/device responds [ML capability response]”, ¶209, “ML model messages comprising e.g. model descriptions ( model types , structure description ) , model parameters , meta - data on what training data models are based on , message whether to use existing ML model in device or receive ML model from BS or repository , etc.”) . With regard to Claim 24, Claim 24 is similar in scope to claim 17; therefore it is rejected under similar rationale. With regard to Claim 25, Claim 25 is similar in scope to claim 18; therefore it is rejected under similar rationale. With regard to Claim 26, Claim 26 is similar in scope to claim 19; therefore it is rejected under similar rationale. With regard to Claim 27, Claim 27 is similar in scope to claim 20; therefore it is rejected under similar rationale. With regard to Claim 28, Claim 28 is similar in scope to claim 21; therefore it is rejected under similar rationale. With regard to Claim 29, Claim 29 is similar in scope to claim 22; therefore it is rejected under similar rationale. With regard to Claim 30, Claim 30 is similar in scope to claim 23; therefore it is rejected under similar rationale. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. US Patent No. 20220321423 issued to NORRMAN et al. that disclose Network Data Analytics Function (NWDAF) that collects data from other network functions in the network. The NWDAF also provides services to service consumers (e.g. other network functions). The services include, for example, retrieving data or making predictions based on data collated at the NWDAF. See at least ¶4 Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/558,996 Page 2 Art Unit: 2148 Application/Control Number: 18/558,996 Page 4 Art Unit: 2148 Application/Control Number: 18/558,996 Page 5 Art Unit: 2148 Application/Control Number: 18/558,996 Page 6 Art Unit: 2148 Application/Control Number: 18/558,996 Page 7 Art Unit: 2148 Application/Control Number: 18/558,996 Page 8 Art Unit: 2148 Application/Control Number: 18/558,996 Page 9 Art Unit: 2148 Application/Control Number: 18/558,996 Page 10 Art Unit: 2148 Application/Control Number: 18/558,996 Page 11 Art Unit: 2148 Application/Control Number: 18/558,996 Page 12 Art Unit: 2148 Application/Control Number: 18/558,996 Page 13 Art Unit: 2148 Application/Control Number: 18/558,996 Page 14 Art Unit: 2148 Application/Control Number: 18/558,996 Page 15 Art Unit: 2148 Application/Control Number: 18/558,996 Page 16 Art Unit: 2148 Application/Control Number: 18/558,996 Page 17 Art Unit: 2148 Application/Control Number: 18/558,996 Page 18 Art Unit: 2148 Application/Control Number: 18/558,996 Page 19 Art Unit: 2148 Application/Control Number: 18/558,996 Page 20 Art Unit: 2148 Application/Control Number: 18/558,996 Page 21 Art Unit: 2148 Application/Control Number: 18/558,996 Page 22 Art Unit: 2148
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