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 § 103
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 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 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Number 11,018,939 B1 (hereinafter “Harris”) in view of U.S. Patent Application Publication 2022/0335242 A1 (hereinafter “Bossard”).
With respect to claims 1, 9, and 17, Harris discloses
“A system comprising”: Harris, abstract;
“an Internet of Things (loT) service to provide back-end data processing for a plurality of loT devices, the loT service comprising”: Harris 2:28-32, figs. 1-14, 15A-15D, 16, and 17 (computing devices, home automation devices, IoT devices, etc. are often capable of communicating with, interacting with, and controlling other devices);
“an interface, implemented at least partly in hardware, to securely couple the loT service to an loT app executed on a mobile device of a user …”; Harris 52:23-27 (app is provided for smartphone or other client device to permit user to scan product to determine compatible devices);
“a machine-learning (ML)-based device recognition engine of the IoT service comprising one or more general-purpose or special-purpose processors coupled to the interface, the ML-based device recognition engine to … identify a device and/or specifications of the device captured in the image by the mobile device”; Harris, abstract, 6:57-64, 12:55-63, 24:55-25:4 (neural network, which are a type of machine learning, are used in vision recognition to recognize devices); and
“loT product identification logic of the IoT service to identify one or more loT devices based on the device and/or specifications of the device identified by the ML-based device recognition engine”; Harris, abstract, 6:57-64, 12:55-63, 24:55-25:4 (e.g., scanned text and machine readable labels can be used to recognize device);
“wherein the loT service is to transmit an indication of the one or more loT devices to the loT app via the interface”. Harris 12:26-27, 52-32-38 (compatible devices are identified to user).
Harris does not explicitly disclose that using an image to identify the device. Bossard discloses
“the interface to receive an image of a device captured by the mobile device”; Bossard, abstract; and
“perform ML-based object recognition techniques”. Bossard, abstract.
Both Harris and Bossard relate to identifying devices. Harris, abstract; Bossard, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the visual search feature as taught by Bossard in the method of Harris with the motivation of identifying devices when other data (such as barcodes) are unavailable or insufficient for the purpose. See Bossard ¶¶ 0014-0017.
With respect to claims 2, 10, and 18, Harris discloses
“wherein the loT service further comprises: image pre-processing logic to generate normalized image data based on the image, the ML-based device recognition engine to perform object recognition on the normalized image data to identify the device and/or the specifications of the device captured in the image”. Harris 12:55-63, 24:20-27, 24:55-25:4 (ML engine performs pre-processing to aid in recognizing objects, including re-sampling, noise reduction, contrast enhancement, and space scale representation).
With respect to claims 3, 11, and 19, Harris discloses
“wherein the image pre-processing logic is to perform at least one of: image scaling to generate normalized image data of a particular resolution, image resampling to configure the normalized image data for a particular coordinate system, bit depth or color space adjustments for a specific color space, and noise reduction to filter undesired image artifacts”. Harris 5:13-15, 12:55-63, 24:20-32, 24:55-25:4, 25:15-18, 46:37-41 (ML engine performs pre-processing to aid in recognizing objects, including re-sampling, noise reduction, contrast enhancement, and space scale representation).
With respect to claims 4, 12, and 20, Harris discloses
“further comprising: training logic to train the ML-based object recognition engine using a plurality of images of a corresponding plurality of devices in a training data set, the ML-based object recognition engine to associate object characteristics extracted from the plurality of images with the corresponding plurality of devices, wherein the corresponding plurality of devices include loT devices and unconnected devices”. Harris 5:13-15, 12:55-63, 24:20-32, 24:55-25:4, 25:15-18, 46:37-41 (e.g., feature extraction and noise reduction).
With respect to claims 5, 13, and 21, Harris discloses
“wherein the training logic is to periodically or continually provide additional sets of images of devices to the ML-based object recognition engine and is to further provide feedback related to devices detected in the images by the ML-based object recognition engine”. Harris 47:6-24 (recommendation engine is trained on catalog products as they are identified and thus is retrained over time).
With respect to claims 6, 14, and 22, Harris discloses
“wherein the ML-based object recognition engine is to implement an artificial neural network machine learning model, which is to be updated responsive to the training logic and feedback”. Harris 5:61-66, 9:41-49, 46:37-41 (object recognition engine is updated over time to identify objects better).
With respect to claims 7, 15, and 23, Harris discloses
“wherein the loT app is to prompt the user to capture the image responsive to input from the user indicating a desire to find a replacement for the device”. Harris 10:1-7, 32:4-12 (user is prompted to capture image by user interface in order to identify compatible devices).
With respect to claims 8, 16, and 24, Harris discloses
“wherein the indication of the one or more loT devices includes user-selectable links to be provided in the loT app, the user-selectable links to provide the user with options to purchase the corresponding loT devices”. Harris 32:4-19 (links can be saved relating to compatible devices, which user can utilize to purchase devices).
Remarks
The rejections under 35 U.S.C. § 101 are withdrawn in light of applicant’s amendments to the claims.
Applicant argues Harris does not disclose certain limitations of the amended claims. The examiner agrees and cites the additional reference Bossard.
Conclusion
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 ETHAN D CIVAN whose telephone number is (571)270-3402. The examiner can normally be reached Monday-Thursday 8-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A Smith can be reached at (571) 272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ETHAN D. CIVAN
Primary Examiner
Art Unit 3688
/ETHAN D CIVAN/Primary Examiner, Art Unit 3688