DETAILED ACTION Remarks This office action is issued in response to communication filed on 8/29/2023 . Claims 1-14 are pending in this Office Action. Notice of Pre-AIA or AIA Status 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 § 101 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 1-1 1 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 6 and 12 : Step 1: Statutory Category ?: Yes. claim s 1 , 6 and 12 recites a method (i.e., a “process”) which is one of statutory categories. Claim 1: Step 2A-Prong 1: Judicial Exception Recited ?: Yes. The limitation “ performing a predicting operation using the trained artificial intelligence model ” is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “ using the trained artificial intelligence model ” language, there is nothing in the claim that prevents the limitation from being performed in the human mind. Step 2A-Prong 2 : Integrated into a practical application? No. Claim 1 recites additional elements of “ transmitting a first message for requesting to generate a resource associated with training of an artificial intelligence model to a second device ; t ransmitting a second message for requesting to perform the training based on the resource to the second device ; receiving a third message for notifying completion of the training of the artificial intelligence model from the second device ” which are simply data gathering steps and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)). The additional element of “ using the trained artificial intelligence model ” is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic model . Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No. Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of “ artificial intelligence model ” is at best equivalent of adding the words “apply it” to the judicial exception. The additional receiving steps are mere data gathering and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible. Claim 2 recites additional element of “ wherein the resource includes at least one of an attribute for information on resources storing learning data for the training, an attribute for information on a per-tuple ratio of the learning data, an attribute for information on the artificial intelligence model, information on a parameter used in the artificial intelligence model, an attribute for storing the trained artificial intelligence model, and an attribute for information for triggering to build the artificial intelligence model ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 2 therefore is ineligible. Claim 3 recites additional element of “ downloading software generated to use the artificial intelligence model from the second device ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 3 therefore is ineligible. Claim 4 recites additional element of “ wherein the performing of the predicting operation using the trained artificial intelligence model comprises: transmitting input data to be input into the trained artificial intelligence model to the second device; and receiving a result predicted from the input data from the second device ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 4 therefore is ineligible. Claim 5 recites additional element of “ wherein the third message includes at least one of information indicating the completion of the training of the artificial intelligence model and information indicating performance of the trained artificial intelligence model ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 5 therefore is ineligible. Claim 6 : Step 2A-Prong 1: Judicial Exception Recited ?: Yes. The limitation “ assisting a predicting operation using the artificial intelligence model ” is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “ using the trained artificial intelligence model ” language, there is nothing in the claim that prevents the limitation from being performed in the human mind. Step 2A-Prong 2 : Integrated into a practical application? No. Claim 6 recites additional elements of “ receiving a first message for requesting to generate a resource associated with training of an artificial intelligence model from a first device; receiving a second message for requesting to perform the training based on the resource from the first device; transmitting a third message for requesting to build the artificial intelligence model to the third device ” which are simply data gathering steps and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)). The additional element of “ using the trained artificial intelligence model ” is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic model . Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No. Claim 6 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of “ artificial intelligence model ” is at best equivalent of adding the words “apply it” to the judicial exception. The additional receiving steps are mere data gathering and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 6 therefore is ineligible. Claim 7 recites additional element of “ wherein the resource includes at least one of an attribute for information on resources storing learning data for the training, an attribute for information on a per-tuple ratio of the learning data, an attribute for information on the artificial intelligence model, information on a parameter used in the artificial intelligence model, an attribute for storing the trained artificial intelligence model, and an attribute for information for triggering to build the artificial intelligence model ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 7 therefore is ineligible. Claim 8 recites additional element of “ wherein the assisting of the predicting operation comprises providing software generated to use the artificial intelligence model to the first device ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 8 therefore is ineligible. Claim 9 recites additional element of “ wherein the assisting of the predicting operation comprises: transmitting input data to be input into the trained artificial intelligence model to the second device; and receiving a result predicted from the input data from the second device ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 9 therefore is ineligible. Claim 10 recites additional element of “ wherein the third message includes at least one of information indicating the artificial intelligence model, information necessary for the training of the artificial intelligence model, information on learning data for the training, and information necessary for accessing the learning data ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 10 therefore is ineligible. Claim 11 recites additional element of “ receiving a fourth message including information on the trained artificial intelligence model from the third device; and transmitting a fifth message for notifying completion of the training of the artificial intelligence model to the first device ” which is simply data gathering and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)), subsection II. Even when considered in combination, the additional element do es not provide an inventive concept, claim 1 1 therefore is ineligible. Claim Rejections - 35 USC § 102 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 1-14 are rejected under 35 U.S.C. 102 FILLIN "Insert either \“(a)(1)\” or \“(a)(2)\” or both. If paragraph (a)(2) of 35 U.S.C. 102 is applicable, use form paragraph 7.15.01.aia, 7.15.02.aia or 7.15.03.aia where applicable." \d "[ 2 ]" (a)(2) as being anticipated by by Zhdanov et al.(US Patent 11,983.244 B1, hereinafter “Zhdanov”) As to claim 1 , Zhdanov teaches a method for operating a first device in a machine-to-machine (M2M) system, the method comprising: transmitting a first message for requesting to generate a resource associated with training of an artificial intelligence model to a second device ( Zhdanov col 16, lines 1-5 teaches client 710 may submit an initiateTaggingModelTraining request to setup a training configuration) ; transmitting a second message for requesting to perform the training based on the resource to the second device ; (Zhdanov col 16, lines 24-30 the client 710 may then submit a ge t initerations request 717 to the MLS to start the training iterations of the tag prediction model itself) receiving a third message for notifying completion of the training of the artificial intelligence model from the second device ( Zhdanov col 17, lines 1-10 teaches a trainingComplete message 721 may be transmitted from the MLS to the client) ; and performing a predicting operation using the trained artificial intelligence model . ( Zhdanov col 1 7 , lines 8-15 teaches after training is complete, a trained version of the tag prediction models and/or feature generation model may be used to classify media items that were not used during training) As to claim 2 , Zhdanov teaches t he method of claim 1, wherein the resource includes at least one of an attribute for information on resources storing learning data for the training, an attribute for information on a per-tuple ratio of the learning data, an attribute for information on the artificial intelligence model, information on a parameter used in the artificial intelligence model, an attribute for storing the trained artificial intelligence model, and an attribute for information for triggering to build the artificial intelligence model . ( Zhdanov col 16, lines 1-10 teaches the request may indicate various properties of a desired data item classifier via respective parameters) As to claim 3 , Zhdanov teaches t he method of claim 1, further comprising downloading software generated to use the artificial intelligence model from the second device. ( Zhdanov Fig.9 and col 10, lines 35-45 teaches interactive interface for obtaining tags for media items) As to claim 4 , Zhdanov teaches t he method of claim 1, wherein the performing of the predicting operation using the trained artificial intelligence model comprises: transmitting input data to be input into the trained artificial intelligence model to the second device; and receiving a result predicted from the input data from the second device . ( Zhdanov col 19, lines 15-25 teaches interface with proposed tags for songs) As to claim 5 , Zhdanov teaches t he method of claim 1, wherein the third message includes at least one of information indicating the completion of the training of the artificial intelligence model and information indicating performance of the trained artificial intelligence model . ( Zhdanov col 17, lines 1-10 teaches a trainingComplete message 721 may be transmitted from the MLS to the client) As to claim 6 , Zhdanov teaches a method for operating a second device in a machine-to-machine (M2M) system, the method comprising: receiving a first message for requesting to generate a resource associated with training of an artificial intelligence model from a first device ; ( Zhdanov col 16, lines 1-5 teaches client 710 may submit an initiateTaggingModelTraining request to setup a training configuration) receiving a second message for requesting to perform the training based on the resource from the first device ; (Zhdanov col 16, lines 24-30 the client 710 may then submit a getiniterations request 717 to the MLS to start the training iterations of the tag prediction model itself) transmitting a third message for requesting to build the artificial intelligence model to the third device ; and assisting a predicting operation using the artificial intelligence model. ( Zhdanov col 20, lines 55-65 teaches consumer requests handlers may respond to user requests for content . Tags predicted using the MAS for content items using models which were trained using a combination of transfer learning and active learning techniques may be used to respond to consumer requests to classify catalog entries, to create and populate audio/radio stations, to create and populate channels) As to claim 7 , Zhdanov teaches the method of claim 6, wherein the resource includes at least one of an attribute for information on resources storing learning data for the training, an attribute for information on a per-tuple ratio of the learning data, an attribute for information on the artificial intelligence model, information on a parameter used in the artificial intelligence model, an attribute for storing the trained artificial intelligence model, and an attribute for information for triggering to build the artificial intelligence model . ( Zhdanov col 16, lines 1-10 teaches the request may indicate various properties of a desired data item classifier via respective parameters) As to claim 8 , Zhdanov teaches the method of claim 6, wherein the assisting of the predicting operation comprises providing software generated to use the artificial intelligence model to the first device . ( Zhdanov Fig.9 and col 10, lines 35-45 teaches interactive interface for obtaining tags for media items) As to claim 9 , Zhdanov teaches the method of claim 6, wherein the assisting of the predicting operation comprises: transmitting input data to be input into the trained artificial intelligence model to the second device; and receiving a result predicted from the input data from the second device . ( Zhdanov col 19, lines 15-25 teaches interface with proposed tags for songs) As to claim 10 , Zhdanov teaches the method of claim 6, wherein the third message includes at least one of information indicating the artificial intelligence model, information necessary for the training of the artificial intelligence model, information on learning data for the training, and information necessary for accessing the learning data . ( ( Zhdanov col 17, lines 17-32 teaches parameters include media sources paramters, tag sources parameter, identifiers of one or more tag predictions/classification algorithms, identifier of one or more active learning algorithms…) As to claim 11 , Zhdanov teaches the method of claim 6, further comprising: receiving a fourth message including information on the trained artificial intelligence model from the third device; and transmitting a fifth message for notifying completion of the training of the artificial intelligence model to the first device . ( Zhdanov col 17, lines 1-10 teaches a trainingComplete message 721 may be transmitted from the MLS to the client) As to claim 12 , Zhdanov teaches the method for operating a third device in a machine-to-machine (M2M) system, the method comprising: receiving a first message for requesting to build an artificial intelligence model to be used in a first device from a second device ; ( Zhdanov col 16, lines 1-5 teaches client 710 may submit an initiateTaggingModelTraining request to setup a training configuration) generating the artificial intelligence model; performing training for the artificial intelligence model ( Zhdanov col 16, lines 24-40 teaches the MLS may start one or more labeling sessions ) ; and transmitting a second message including information on the trained artificial intelligence model to the second device . ( Zhdanov col 16, lines 59-65 teaches status indicators or updates may be provided to the clients) As to claim 13 , Zhdanov teaches the method of claim 12, w herein the first message includes at least one of information indicating the artificial intelligence model, information necessary for the training of the artificial intelligence model, information on learning data for the training, and information necessary for accessing the learning data . ( Zhdanov col 16, lines 1-10 teaches the request may indicate various properties of a desired data item classifier via respective parameters) As to claim 14 , Zhdanov teaches the method of claim 12, wherein the second message includes an updated weight value of at least one connection constituting the trained artificial intelligence model . (Zhdanov col 16, lines 24-30 the client 710 may then submit a getiniterations request 717 to the MLS to start the training iterations of the tag prediction model itself. Sending weight updates is well known in the art) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT HIEN DUONG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-7335 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8:00AM-5:00PM . 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, FILLIN "SPE Name?" \* MERGEFORMAT Viker Lamardo can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-5871 . 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. /HIEN L DUONG/ Primary Examiner, Art Unit 2147