DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 6/12/26. 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 .
Response to Arguments
Applicant’s arguments filed 6/12/2026 with respect to the 101 rejection have been considered and are not persuasive. The examiner respectfully traverses applicant’s arguments.
Applicant argues: “As amended, the recited "transmitting" and "receiving" limitations of claim 1 impose meaningful limits on the claim such that it is not nominally or tangentially related to the claimed subject matter. Accordingly, these limitations should not be considered as "extra-solution activity" in the context of the Step 2A, Prong Two SME analysis” (Applicant’s argument at page 8)
Examiner responses: The examiner respectfully disagrees. There is nothing in the amended claim 1 transmitting and receiving steps that integrates the exception into a practical application. These steps are directed to pre/post solution activities related to the training of the AI model. The transmitting steps are for requesting to generate a resource and requesting to perform training based on the resource. The receiving step is for notify the completion of the training. Since the receiving step and transmitting steps are related to the training of the AI model, they are considered extra solution activity as per MPEP 2106.05(g).
Applicant argues: “Claim 1 recites limitations which cover a particular solution to the problem of artificial intelligence in M2M systems. That is, viewed as a whole, the alleged judicial exception and additional elements provide a particular way to operate an Al model in an M2M system, providing an improvement in the technology area of AI enablement in M2M systems.”(Applicant argument at page 9)
Examiner responses: The examiner respectfully disagrees. Theres is nothing the amended claim 1 that reflects the improvement in the technological area as applicant asserts. Instead, the amended claim 1 only recites a series of steps that are directed to requesting to generate resource, request to perform training of AI model and finally receiving the completion notification once the training of the AI model completed. There is no details of how the training is done or how the AI model operates that is considered to be improvement in the AI technology area. The examiner respectfully submits that a generic request to perform the training of the AI model and receiving the completion notification as recited in the amended claim 1 does not provide any improvement in the AI technology area as applicant asserts.
Applicant’s remaining arguments with respect to other claims rejected under 35 USC 101 are substantially encompassed in the argument above, therefore examiner responds with the same rationale as stated above.
For at least the foregoing reasons, the examiner maintains the 35 USC 101 rejection.
Applicant’s arguments with respect to the claims rejected under 35 USC 102 have been considered and are moot in view of new ground of rejection.
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.
2. Claims 1-11 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. claims 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 by the first device, 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 “by the first device” and “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, by a first device of the M2M system that implements an artificial intelligence application a first message for requesting to generate a resource associated with training of an artificial intelligence model to a second device of the M2M system that manages a resource associated with training of the artificial intelligence model, wherein the first message includes information necessary to specify the artificial intelligence model that is selected by the second device; transmitting, by the first device, a second message for requesting to perform the training based on the resource to the second device, wherein the training of the artificial intelligence model is performed by a third device of the M2M system based on a request of the second device; receiving, by the first device, 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 “by the first device “ and “using the trained artificial intelligence model” are recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic model with generic computer.
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 “by the first device” and “artificial intelligence model” are 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 does 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 does 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 does 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 does not provide an inventive concept, claim 5 therefore is ineligible.
Claim 6:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The limitation “assisting by the second device, 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 “by the second device” and “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, by a second device of the M2M system that manages a resource associated with training of the artificial intelligence model, a first message for requesting to generate a resource associated with training of an artificial intelligence model from a first device of the M2M system that implements an artificial intelligence application, wherein the first request message includes information necessary to specify the artificial intelligence model that is selected by the second device; receiving, by the second device, 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 “by the second device” and “using the trained artificial intelligence model” are recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic model using generic computer.
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 “by the second device” and “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 does 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 does 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 does 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 does 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 does not provide an inventive concept, claim 11 therefore is ineligible.
Claim Rejections - 35 USC § 103
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.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhdanov et al.(US Patent 11,983.244 B1, hereinafter “Zhdanov”) and further in view of Brendre et al. (US Patent Application Publication 2018/0322417 A1, hereinafter “Bendre”)
As to claim 1, Zhdanov teaches a method for operating an artificial intelligence model in a machine-to-machine (M2M) system, the method comprising:
transmitting, by a first device of the M2M system that implements an artificial intelligence application a first message for requesting to generate a resource associated with training of an artificial intelligence model to a second device of the M2M system that manages a resource associated with training of the artificial intelligence model, wherein the first message includes information necessary to specify the artificial intelligence model that is selected by the second device (Zhdanov col 16, lines 1-5 teaches client 710 may submit an initiateTaggingModelTraining request to setup a training configuration) ;
transmitting, by the first device, a second message for requesting to perform the training based on the resource to the second device, [wherein the training of the artificial intelligence model is performed by a third device of the M2M system based on a request of the second 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)
receiving, by the first device, 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, by the first device, a predicting operation using the trained artificial intelligence model.( Zhdanov col 17, 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)
Zhdanov fails to expressly teach wherein the training of the artificial intelligence model is performed by a third device of the M2M system based on a request of the second device.
However, Bendre teaches wherein the training of the artificial intelligence model is performed by a third device of the M2M system based on a request of the second device. (Bendre par [0121] teaches the scheduler device may assign the first and second ML training requests to different trainer devices)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Zhdanov and Bendre to achieve the claimed invention. One would have been motivated to make such combination to save time, improve the use of computing resources and reduce costs on specialized hardware (Bendre par [0004])
As to claim 2, Zhdanov and Bendre teach the 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 and Bendre teach the 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 and Bendre teach the 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 and Bendre teach the 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, by a second device of the M2M system that manages a resource associated with training of the artificial intelligence model, a first message for requesting to generate a resource associated with training of an artificial intelligence model from a first device of the M2M system that implements an artificial intelligence application, wherein the first request message includes information necessary to specify the artificial intelligence model that is selected by the second device; (Zhdanov col 16, lines 1-5 teaches client 710 may submit an initiateTaggingModelTraining request to setup a training configuration)
Receiving, by the second device, 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, by the second device, a third message for requesting to build the artificial intelligence model to a third device of the M2M system, wherein the training of the artificial intelligence model is performed by the third device based on the third message]; and
Assisting, by the second device, 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)
Zhdanov fails to expressly teach transmitting, by the second device, a third message for requesting to build the artificial intelligence model to a third device of the M2M system, wherein the training of the artificial intelligence model is performed by the third device based on the third message.
However, Bendre teaches transmitting, by the second device, a third message for requesting to build the artificial intelligence model to a third device of the M2M system, wherein the training of the artificial intelligence model is performed by the third device based on the third message. (Bendre par [0121] teaches the scheduler device may assign the first and second ML training requests to different trainer devices)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Zhdanov and Bendre to achieve the claimed invention. One would have been motivated to make such combination to save time, improve the use of computing resources and reduce costs on specialized hardware (Bendre par [0004])
As to claim 7, Zhdanov and Bendre teach 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 and Bendre teach 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 and Bendre teach 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 and Bendre teach 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 parameters, 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 and Bendre teach 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 an artificial intelligence model in a machine-to-machine (M2M) system, the method comprising:
Receiving, [by a third device of the M2M system that performs training for the artificial intelligence model], a first message for requesting to build an artificial intelligence model to be used in a first device of the M2M system that implements the artificial intelligence model from a second device of the M2M system that manages a resource associated with training of the artificial intelligence model; (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)
Zhdanov fails to expressly teach the receiving is by a third device of the M2M system that performs training for the artificial intelligence model.
However, Bendre teaches receiving, by a third device of the M2M system that performs training for the artificial intelligence model, a first message for requesting to build an artificial intelligence model to be used in a first device. (Bendre par [0121] teaches the scheduler device may assign the first and second ML training requests to different trainer devices)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Zhdanov and Bendre to achieve the claimed invention. One would have been motivated to make such combination to save time, improve the use of computing resources and reduce costs on specialized hardware (Bendre par [0004])
As to claim 13, Zhdanov and Bendre teach the method of claim 12, wherein 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 and Bendre teach 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Namgoong et al. US PGPub 2025/0184717 A1, par [0160] discloses forwarding the model training request to a different device.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached 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, Viker Lamardo can be reached at 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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