DETAILED 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 .
Examiner Note
Examiner re-open the prosecution because there was no 101 or 112 rejections.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-20 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node.
The apparatus and the method claim 1 and 11 recites limitation, “generating a resource for training an artificial intelligence (Al) model based on a first message for training the Al model received from a second device; transmitting a second message for performing initial learning of the Al model to a third device; collecting learning data for re-learning for the Al model; and controlling to perform re-learning of the Al model by using the learning data”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites generating a resource for training an artificial intelligence (Al) model based on a first message for training the Al model received from a second device; transmitting a second message for performing initial learning of the Al model to a third device; collecting learning data for re-learning for the Al model; and controlling to perform re-learning of the Al model by using the learning data. The generating step where the first message for training the AI model is received from the second device which can be refer to as collecting step and then transmitting a second message to the third device which can be refer to as outputting certain results and then performing re-learning of the learning data can be analyzing step i.e., mental step of receiving an dog picture and if it’s not a dog picture saying it’s a cat picture recited in the claim is no more than an abstract idea i.e., mental process of collecting information, analyzing it and outputting certain results, etc. See MPEP 2106.04 (a) III A, a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
Claim Rejections - 35 USC § 112
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.
Claim 1-20 are 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 pre-AIA the applicant regards as the invention.
As per independent claims 1 and 11 the claim relates to “generating a resource for training an artificial intelligence (Al) model based on a first message for training the Al model received from a second device; transmitting a second message for performing initial learning of the Al model to a third device; collecting learning data for re-learning for the Al model; and controlling to perform re-learning of the Al model by using the learning data”.
For example, claim 1 relates to performing a method for operating a first device in M2M system. However, there is no correlation between the steps of:
a) generating a resource for training an artificial intelligence (Al) model based on a first message for training the Al model received from a second device;
b) transmitting a second message for performing initial learning of the Al model to a third device;
c) collecting learning data for re-learning for the Al model; and
d) controlling to perform re-learning of the Al model by using the learning data
Therefore, the intended limitations show no correlation between the steps because in step a) and b) of the claim applicant recites generating a resource for training an artificial intelligence (Al) model based on a first message for training the Al model received from a second device and then transmitting a second message for performing initial learning of the Al model to a third device. However, step c) it only says collecting learning data for re-learning for the AI model i.e., Step c) of the claim does not positively recite as a step of the method but instead considered to merely be an intended use.
In addition, collection of the learning data recited in Step c) does not show what that learning data is. Is it the initial learning of the AI model or the new data. Further, the method of ‘causing to access’, ‘causing a negotiation’, and ‘causing a transfer’ may make use of the suitable connection information as claimed, but the actual origin of the suitable connection information (or how it is determined) is not recited as being within the scope of the claim. Then in Step b) the transmitting step i.e., transmitting a second message for performing initial learning of the Al model to a third device and further step c) collecting learning data for re-learning for the Al model does not show who is performing the step. Is it first device, second device or the third device. Further it is not clear what, why and who or on what basis collecting learning data for relearning should be performed or why the system decide for collecting learning data for re-learning or what applicant is trying to achieve by initial learning or from learning data if they are same. There is no correlation between step b) and step c). Therefore, claim 1 is not only vague but also unclear for one having ordinary skill in the art to understand the invention as a whole.
Similarly, 11 recite the same limitation and are rejected for the same reasons.
Further, dependent claims do not overcome the deficiency of the independent claims.
Claim Rejections - 35 USC § 102
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.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ramachandra Iyer Pub. No. US 20190370697 A1 (Now as refer to as Iyer).
Regarding Claim 1, Iyer teaches a method for operating a first device in a machine-to-machine (M2M) system (Para 20 and Fig. 2, a functional block diagram of the tracing device 200, implemented by the system 100 of FIG. 1 i.e., operating a first device in M2M), the method comprising:
generating a resource for training an artificial intelligence (Al) model (Para 42 and Fig. 4 Step 401, the control logic 400 may generate a sequence graph based on training data set i.e., generating a resource for training an artificial intelligence (Al) model ) based on a first message for training the Al model received from a second device(Para 46 and Fig. 4 Step 402, At step 402, the control logic 400 may receive an input from a user. The input may be received by the classifier module 202 through a display module 208 i.e., based on a first message for training the Al model received from a second device);
transmitting a second message for performing initial learning of the Al model to a third device (Para 48, At step 404, the control logic 400 may generate a learning graph based on the similarity between training datasets, It may be noted that the learning graph may be a layer-wise output for the training data organized hierarchically i.e., transmitting a second message. In order to generate the learning graph i.e., initial learning of the AI model, a cluster node may be randomly selected from the user-selected class which is to be traced back. Thereafter, targets may be generated. In some embodiments, the targets may include learning graph for the cluster node (randomly selected from the user-selected class which is to be traced back), a node belonging to a level above the cluster node i.e., third device, and a node belonging to a level below the cluster node i.e., the second device);
collecting learning data for re-learning for the Al model (Para 54 and 55 and Step 406-407, at step 406, the control logic 400 may detect a training dataset (learning source) which may have influenced the misclassification of an input i.e., collecting learning data and At step 407, the control logic 400 may perform a re-learning for the identified dataset. In some embodiments, the re-learning module 206 may perform the re-learning for the identified dataset. It may be noted that the re-learning may be performed with both the test cases, i.e. the misclassified classified object and the test case that was responsible for incorrect learning i.e., re-learning for AI model); and
controlling to perform re-learning of the Al model by using the learning data (Para 55 and 56 and Fig. 4 Step 407 and 408, At step 407, the control logic 400 may perform a re-learning for the identified dataset i.e., performing re-learning of the AI model by using the learning data. At step 408, the control logic 400 may check the consistency of the retrained system. In some embodiments, the consistency may be checked by the consistency check module 207. In some embodiments, the classifier model 202 may now be applied with validation data from the group to which the identified test case belongs. As a result, the misclassification due to the closeness of the two test cases may be eliminated. Further, the classifier model 202 may be applied with validation test cases. As a result, the misclassification due to test cases which are close to the identified test case may be eliminated. In an embodiment, the consistency check may be made a part of training the classifier model 202 i.e., controlling).
Regarding Claim 2, Iyer teaches wherein the resource includes at least one of first information comprising a learning algorithm, second information defining a re-learning triggering criterion, third information for storing the learning data, fourth information for storing a result of the re-learning, fifth information for storing initial learning data, or sixth information indicating an accuracy rate for prediction using an artificial intelligence model (Fig. 4 Para 46-49, 54-57).
Regarding Claim 3, Iyer teaches wherein the controlling to perform the re- learning comprises performing the re-learning based on a condition for the re-learning being satisfied (Para 56).
Regarding Claim 4, Iyer teaches wherein the condition includes at least one of arrival of a specified hour, the learning data for the re-learning being collected by a specified amount, the learning data for the re-learning being collected at a specified size, occurrence of a request for the re-learning, or a prediction accuracy rate of the artificial intelligence model being below a threshold (Para 54).
Regarding Claim 5, Iyer teaches wherein the condition includes the request for the re-learning that is received from the second device that operates the artificial intelligence model (Para 46).
Regarding Claim 6, Iyer teaches wherein the learning data for the re-learning is generated based on data input that is input for prediction using the artificial intelligence model with the initial learning (Para 46)..
Regarding Claim 7, Iyer teaches wherein the learning data for the re-learning includes the data input for prediction and a label that is generated by an entity which generates the label based on the data input (Para 46).
Regarding Claim 8, Iyer teaches wherein the learning data for the re-learning includes data augmented from the data input that is input for prediction (Para 45 and 46).
Regarding Claim 9, Iyer teaches wherein the controlling to perform the re- learning further comprises: transmitting the learning data for re-learning to the third device that performs learning for the artificial intelligence model; and receiving information on the artificial intelligence model that is re-learned by the third device (Para 48).
Regarding Claim 10, Iyer teaches wherein the controlling to perform the initial learning comprises: transmitting learning data for initial learning to the third device that performs learning for the artificial intelligence model; and receiving information on the artificial intelligence model that is initially learned by the third device (Para 56).
Regarding Claim 11, it has been rejected for the same reasons as claim 1 and further teaches an apparatus first device in a machine-to-machine (M2M) system (Fig. 6 Unit 601 and Para 66), comprising: a transceiver (Fig. 6 Unit 606 Para 66, transceiver 606); and a processor (Fig. 6 Unit 602 and Para 66) coupled with the transceiver (fig. 6 Unit 606 is coupled with processor 602).
Regarding Claim 12, it has been rejected for the same reasons as claim 2.
Regarding Claim 13, it has been rejected for the same reasons as claim 3.
Regarding Claim 14, it has been rejected for the same reasons as claim 4.
Regarding Claim 15, it has been rejected for the same reasons as claim 5.
Regarding Claim 16, it has been rejected for the same reasons as claim 6.
Regarding Claim 17, it has been rejected for the same reasons as claim 7.
Regarding Claim 18, it has been rejected for the same reasons as claim 8.
Regarding Claim 19, it has been rejected for the same reasons as claim 9.
Regarding Claim 20, it has been rejected for the same reasons as claim 10.
Response to Arguments
Applicant’s arguments, see Page 6-10, filed 7/18/2025, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ramachandra Iyer Pub. No. US 20190370697 A1. Although Iyer reference reads on applicant claimed invention, however previous examiner errored by not reviewing claims in light of 35 USC 101 and 112 rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIZAR N SIVJI whose telephone number is (571)270-7462. The examiner can normally be reached Monday-Friday 7-4.
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, Alison Slater can be reached at (571) 270-0375. 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.
NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/Primary Examiner, Art Unit 2647
Prosecution Insights