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 .
Status of the Application
2. Claim 1-20 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawings filed on 7/31/24 are acceptable for examination proceedings.
Claim Objections
Claim 5, and 15 are objected to because of the following informalities: Both claim includes the phrase “use feedback” (at Ln. 4 for the claim 5 and at Ln. 5 for the claim 15). It should be corrected to “user feedback”. Appropriate correction is required.
Allowable Subject Matter
Claim 7, and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when 35 U.S.C 101 abstract idea rejection is overcome.
Claim 8-9, and 18-20 are also rejected due to their direct/indirect dependency over the claim 7 and 17, respectively.
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.
4. Claims 1, 13, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more as fully discussed below.
Regarding Independent claim 1, 13, and 16:
Step 1: Yes
Claim 1 is drawn to a method, claim 13 is drawn to a non-transitory computer-readable medium containing instructions, that when executed by one or more processors, are configured to cause the one or more processors to perform operations, and claim 16 is drawn to a system, therefore claim 1, 13 and 16 falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Independent claim 1, 13 and 16 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 1, 13 and 16 recites claim limitation of “determining, by the computing device a control signal configured to cause the IoT device to perform the action” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally perform a simple determining control signal function.
Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Claim 1, 13, and 16 recites claim limitation of “a machine learning model, at least a portion of the data, the machine learning model configured to determine an IoT device associated with the action to be performed using the portion of the data”, wherein machine learning model is fundamentally built on mathematics and includes the mathematical formulas. Hence the claimed limitation of machine learning model to determine that under their broadest reasonable interpretation, enumerates a mathematical concept.
Thus, these claimed functions are the judicial exceptions that are no more than an abstract idea processed by a mathematical algorithm (See MPEP 2106.04(a)(2)(I)).
Hence claim 1, 13, and 16 are the judicial exceptions that are no more than an abstract idea processed by mental concept and a mathematical calculation.
Step 2A, Prong 2: No
Claim 1, 13, and 16 recites additional limitation of “receiving, by a computing device, data indicating an action to be performed; providing, by the computing device to a machine learning model; receiving, by computing device from the machine learning model, an output from the machine learning model, the output indicating the IoT device associated with the action to be performed; and transmitting, by the computing device, the control signal to the IoT device.” The functions of receiving, providing and transmitting are forms of insignificant input or output solution activities (i.e., extra solution), such that data receiving and outputting are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 1, 13, and 16 further recite additional limitation of “computing device;
Claim 13 further recites “non-transitory computer-readable medium containing instructions, one or more processors,”
Claim 16 further recite additional limitation of “one or more processor, computer-readable medium” are considered as do not integrate into practical application and are recited at a high level of generality such that thy amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
Step 2B: No
The additional functions that are a form of insignificant extra-solution activity, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element to be well-understood, routine, and conventional when claimed in a merely generic manner for data storing, collecting, receiving, transmitting, outputting, or displaying (See MPEP § 2106.05(d)(II)(i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) or (iv: Storing and retrieving information, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))).
As such, claim 1, 13, and 16 are not patent eligible.
Dependent claims 2-12, 14-16, and 17-20:
Step 1: Yes
Claim 2-12 are drawn to a method, claim 14-15 are drawn to a non-transitory computer-readable medium containing instructions, that when executed by one or more processors, are configured to cause the one or more processors to perform operations, and claim 117-20 are drawn to a system, therefore claim 2-12, 14-15, and 17-20 are falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Dependent claim 2-12, 14-15, and 17-20 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 3 recites claim limitation of “the determining the control signal is performed by the machine learning model”;
Claim 7 recites limitation of “detecting, by the computing device, a change associated with the IoT device or additional IoT devices”;
Claim 8 recites claim limitation of “determining, by the machine learning model of the computing device, one or more sub-actions related to the action to be performed; determining, by the machine learning model, the IoT device or additional IoT devices associated with the one or more sub-actions; and generating, by the machine learning model, the output to indicate the IoT device or the additional IoT devices”;
Claim 9 recites limitation of “determining, by the computing device, a respective control signal configured to cause a respective IoT device to perform an associated sub-actions” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally perform the claimed limitation as discussed above.
Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Claim 5, 7, 11, 15, and 20 recites claim limitation includes the function of training/retraining of a machine learning model, wherein machine learning model is fundamentally built on mathematics and includes the mathematical formulas. Hence the claimed limitation of training/retraining machine learning model to determine that under their broadest reasonable interpretation, enumerates a mathematical concept.
Thus, these claimed functions are the judicial exceptions that are no more than an abstract idea processed by a mathematical algorithm (See MPEP 2106.04(a)(2)(I)).
Step 2A, Prong 2: No
Claim 5, and 15 recites additional limitation of “further comprising : receiving, by the computing device, user feedback indicating an accuracy of the output; and providing, by the computing device, at least one of the output or the use feedback such that the machine learning model is retrained”;
Claim 6 recites additional limitation of “the user feedback is provided”;
Claim 7 recites additional limitation of “transmitting, by the computing device, data indicating the change to a server; receiving by the computing device, a training data from the server , the training data associated with the change; and providing, by the computing device, the training data to the machine learning model such that the machine learning model is retrained”;
Claim 10 recites additional limitation of “further comprising: receiving, by the computing device, a status update from the IoT device after executing the action; and causing, by the computing device, a confirmation or result of the action to be displayed”;
Claim 17 recites additional limitation of “display an error message when the machine learning model is unable to determine the output;”
Claim 18 recites additional limitation of “display, one or more options displayed”
Claim 19 recites additional limitation of “receive at least one user input responsive to the one or more options to resolve the error message”.
The functions of receiving, providing, transmitting and displaying are forms of insignificant input or output solution activities (i.e., extra solution), such that data receiving and outputting are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 2, 4, 14 recites the execution of machine learning model locally and on a remote server” are considered as do not integrate into practical application and are recited at a high level of generality such that thy amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
Step 2B: No
The additional functions that are a form of insignificant extra-solution activity, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element to be well-understood, routine, and conventional when claimed in a merely generic manner for data storing, collecting, receiving, transmitting, outputting, or displaying (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) or (iv: Storing and retrieving information, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))).
As such, dependent claim 2-12, 14-15, 17-20 are not patent eligible.
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)(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.
5. Claims 1-4, 13-14, and 16 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Hu (PG Pub: 2023/0236795).
6. Regarding claim 1, Hu discloses:
A method for controlling an Internet of Things (IoT) device, the method comprising: receiving, by a computing device, data indicating an action to be performed (e.g., As shown in FIG. 2, at 202, computing device 130 may receive at least two data packets for floating-point arithmetic operations from at least one source device. As an example, the at least one source device may be IoT device 110 shown in FIG. 1. As another example, the at least one source device may be two or more IoT devices, and each of the IoT devices sends data packets for floating-point arithmetic operations to computing device 130 in edge switch 120.) (Para. [0027]);
providing, by the computing device to a machine learning model, at least a portion of the data, the machine learning model configured to determine an IoT device associated with the action to be performed using the portion of the data (e.g., After performing the floating-point arithmetic operation on the data packet from IoT device 110, computing device 130 may send the data packet which has been subjected to the floating-point arithmetic to cloud (network) 140, and via the cloud 140, the data packet which has been subjected to the floating-point arithmetic can be sent to computing node 150. It should be understood that computing node 150 may be a server which has functions of training a machine learning model or a deep learning model. Hence, data communication from IoT device 110 to computing node 150 is implemented, and the entire data operation process of the communication is completed at the corresponding edge switch) (Para. [0023], also refer to Para. [0038]));
receiving, by computing device from the machine learning model, an output from the machine learning model, the output indicating the IoT device associated with the action to be performed (e.g., Correspondingly, in a model application stage, IoT device 110 may also send floating-point numerical values collected in real time to edge switch 120 for relevant floating-point arithmetic, and edge switch 120 sends a calculation result to computing node 150 via cloud 140.) (Para. [0038]);
determining, by the computing device a control signal configured to cause the IoT device to perform the action (e.g., Computing node 150 may generate a control signal based on the calculation result by using the trained model,) (Para. [0038]);
and transmitting, by the computing device, the control signal to the IoT device (e.g., and send the control signal to IoT device 110 via cloud 140 and edge switch 120, so as to adjust relevant functions of IoT device 110.) (Para. [0038]).
7. Regarding claim 2, Hu discloses:
The method of claim 1, wherein the machine learning model (e.g., after collecting a sufficient amount of field data, computing node 150 may train a corresponding machine learning or deep learning model) (Para. [0038]) is executed locally on the computing device (e.g., In order to transmit data, IoT device 110 is usually communicatively connected to edge switch 120. Edge switch 120 is usually arranged near IoT device 110, which serves as an edge computing node, so as to provide a data exchange service for the corresponding IoT device) (Para. [0020]).
8. Regarding claim 3, Hu discloses:
The method of claim 1, wherein the determining the control signal is performed by the machine learning model (e.g., Computing node 150 may generate a control signal based on the calculation result by using the trained model) (Para. [0038]).
9. Regarding claim 4, Hu discloses:
The method of claim 1, wherein the machine learning model is executed on a remote server (e.g., It should be understood that computing node 150 may be a server which has functions of training a machine learning model or a deep learning model) (Para. [0023]).
10. Regarding claim 13, Claim 13 recites a non-transitory computer-readable medium containing instructions, that when executed by one or more processors that implement the method of claim 1, with substantially the same limitations, respectively. Therefore the rejection applied to claim 1 also applies to claim 13 respectively.
11. Regarding claim 14, as to claim 14, applicant is directed to the citation for claim 4 above.
12. Regarding claim 16, Claim 16 recites a non-transitory computer-readable medium containing instructions, that when executed by one or more processors that implement the method of claim 1, with substantially the same limitations, respectively. Therefore the rejection applied to claim 1 also applies to claim 13 respectively.
Claim Rejections - 35 USC § 103
13. 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.
14. Claim 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Heiland (Pat: 11115823).
15. Regarding claim 11, Hu teaches the method of claim 1 but does not specifically teach further comprising, retraining the machine learning model based on receiving, at the computing device, a new capability of the IoT device.
Heiland teaches further comprising, retraining the machine learning model based on receiving, at the computing device, a new capability of the IoT device (e.g., Step 820 involves updating the machine learning model with the received label. Accordingly, the method 800 may continuously update and retrain the machine learning model(s) based on the newly-labeled IoT devices (and non-IoT devices) (Col. 13, Ln. 63-67).
Because Heiland is also directed to machine learning based classification of IoT devices, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hu and Heiland before him/her, to modify the teachings of Hu to include the teaching of Heiland in order to providing the at least one extracted feature as input to a classifier executing a machine learning model configured to classify the device as an internet-of-things (IoT) device or a non-IoT device based on the at least one extracted feature (Col. 1, Ln. 51-54).
16. Regarding claim 12, Hu teaches the method of claim 1 but does not specifically teach wherein data indicating the action to be performed is a voice command.
Heiland teaches wherein data indicating the action to be performed is a voice command (e.g., These IoT devices include, but are not limited to, security cameras, thermostats, healthcare-related devices, printers, smart TVs, refrigerators, lights, smartphones, voice-command devices, humidity control devices, appliances, wearable technologies, fitness monitoring devices, vehicles, traffic monitoring systems, transportation systems, or the like. This list is merely exemplary, and other types of IoT devices whether available now or invented hereafter may be identified in accordance with the various embodiments described herein) (Col. 5, Ln. 28-37).
17. Claim 5-6, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Kochura (Pub: 2019/0074089).
18. Regarding claim 5, Hu teaches the method of claim 1 but does not specifically teach further comprising: receiving, by the computing device, user feedback indicating an accuracy of the output; and providing, by the computing device, at least one of the output or the use feedback such that the machine learning model is retrained.
Kochura further comprising: receiving, by the computing device, user feedback indicating an accuracy of the output (e.g., Embodiments of the present invention utilize multiple sensors and feedback mechanisms to obtain monitoring data and related contextual information. Embodiments of the present invention utilize analytics, machine learning, and cognitive methods to analyze monitoring data, predict changes to a state of health of the user, and obtain feedback from the user. Feedback from the user is utilized to generate, refine, and update one or more models that represent or predict various states of health associated with the user and/or models that represent the consumption of various foods and beverages) (Para. [0013]); and providing, by the computing device, at least one of the output or the use feedback such that the machine learning model is retrained (e.g., Feedback from the user is utilized to generate, refine, and update one or more models that represent or predict various states of health associated with the user and/or models that represent the consumption of various foods and beverages) (Para. [0013], also refer to Para. [0017]).
Because Kochura is also directed to machine learning and IoT devices, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hu and Kochura before him/her, to modify the teachings of Hu to include the teaching of Kochura in order to improve the scope and accuracy of models (Para. [0017]).
19. Regarding claim 6, the combination of Hu and Kochura teaches the method of claim 5, wherein Kochura further teaches wherein the user feedback is provided to a server and the retraining occurs at the server (e.g., In one embodiment, system 102 communicates through network 110 to device 120 and device 130. In some embodiments, system 102 communicates with one or more other computing systems and/or computing resources, such as a web server, an e-mail server, a network of health care service providers, etc. (not shown) via network 110) (Para. [0038]).
20. Regarding claim 14, as to claim 14, applicant is directed to the citation of claim 6, above.
21. Regarding claim 15, as to claim 15, applicant is directed to the citation of claim 5, above.
22. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Volkerink (Pub: 2022/0036300).
23. Regarding claim 10, Hu teaches the method of claim 1 but does not specifically teach further comprising: receiving, by the computing device, a status update from the IoT device after executing the action; and causing, by the computing device, a confirmation or result of the action to be displayed.
Volkerink further comprising: receiving, by the computing device, a status update from the IoT device after executing the action (e.g., the IoT sampling system may be updated to reflect the change in the field so that the association between the platform (e.g., the hardware identifier) and the asset (e.g., tracking barcode) are up-to-date in the IoT sampling system. For example, the IoT sampling system may update its tracking system of every platform included or associated with an instruction to reflect the proposed instructions automatically or the IoT sampling system may receive an input from the authorized user that includes any changes the authorized user made to the platforms according to the instruction. For example, the IoT sampling system may automatically update the status of platforms to reflect any outputted instructions) (Para. [0139]); and causing, by the computing device, a confirmation or result of the action to be displayed (e.g., and then receive a confirmation from the authorized user that the instructions were carried out fully, or the authorized user may indicate which portions of the instructions were not followed.) (Para. [0139]).
Because Volkerink is also directed to IoT sampling system, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hu and Volkerink before him/her, to modify the teachings of Hu to include the teaching of Volkerink in order to optimize system performance, cost, and confidence levels (Para. [0003]).
Conclusion
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/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116
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