DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 2-17 are pending in the application.
Claim Objections
Claim 10 4th line “an image the electronic device” should be “an image of the electronic device”.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 2 and 10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and/or 13 of U.S. Patent No. 11,798,250 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons.
Listed in the following tables is a limitation-to-limitation comparison of the examined claims 2 and 10 and the conflicting claims 1 and 13.
Application being examined 19/000,287 (hereafter ‘287 application)
Conflicting patent 11,798,250 (hereafter ‘250 patent)
2. A system for evaluating a condition of an electronic device, the system comprising:
a camera configured to capture an image of the electronic device; and
one or more processors associated with the camera and configured to:
obtain the image;
apply a first machine learning model to the image, wherein the first machine learning model is trained to analyze the image and output a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determine a condition of the electronic device based on the output of the first machine learning model; and
determine, via a second machine learning model different from the first machine learning model, an offer price for the electronic device based on the condition.
1. A system for evaluating a condition of an electronic device, comprising:
a kiosk that includes:
an inspection plate configured to hold the electronic device;
one or more light sources arranged above the inspection plate and configured to direct one or more light beams towards the electronic device;
one or more cameras configured to capture at least one image of a first side of the electronic device based on at least one lighting condition generated by the one or more light sources; and
one or more processors in communication with the one or more cameras, the one or more processors configured to:
extract a set of features of the electronic device based on the at least one image of the electronic device;
apply a trained first neural network on the set of features to compute a score within a range of values and indicative of a severity of damage to the electronic device, wherein the first neural network is trained using a set of images associated with distinct cosmetic evaluation indications; and
wherein an output of the first neural network comprises a brand and/or model of the electronic device;
determine the condition of the electronic device based on the score and the brand and/or model; and
determine, via a second neural network, an offer price for the electronic device based on the determined condition,
wherein the second neural network is different from the first neural network.
Application being examined 19/000,287 (hereafter ‘287 application)
Conflicting patent 11,798,250 (hereafter ‘250 patent)
10. One or more non-transitory, computer-readable media having instructions that, when executed by one or more processors, cause the one or more processors to perform operations (See claim 1 of ‘250 patent, in which a processor is disclosed to perform similar steps as that of claim 13. Computer-readable medium is inherent.) to evaluate a condition of an electronic device, the operations comprising:
obtaining, via a camera, an image the electronic device;
applying a first machine learning model to the image, wherein the first machine learning model is trained to analyze the image and output a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determining a condition of the electronic device based on the output of the first machine learning model; and
determining, via a second machine learning model different from the first machine learning model, an offer price for the electronic device based on the condition.
13. A computer-implemented method for evaluating a condition of an electronic device, comprising:
capturing, by at least one camera of a kiosk, at least one image of a first side of the electronic device, wherein the kiosk includes multiple light sources;
extracting a set of features of the electronic device based on the at least one image of the electronic device;
applying a trained first neural network on the set of features to compute a score within a range of values and indicative of a severity of damage to the electronic device, wherein the first neural network is trained using a set of images associated with distinct cosmetic evaluation indications; and
wherein an output of the first neural network comprises a brand and/or model of the electronic device;
determining the condition of the electronic device based on the score and the brand and/or model; and
determine, via a second neural network, an offer price for the electronic device based on the determined condition,
wherein the second neural network is different from the first neural network.
Therefore claims 1/13 of the ‘250 patent teach every limitation in claim 2/10 of the ‘287 application.
Claims 2 and 10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and/or 11 of U.S. Patent No. 12,223,684 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons.
Listed in the following tables is a limitation-to-limitation comparison of the examined claims 2 and 10 and the conflicting claims 1 and 11.
Application being examined 19/000,287 (hereafter ‘287 application)
Conflicting patent 12,223,684 (hereafter ‘684 patent)
2. A system for evaluating a condition of an electronic device, the system comprising:
a camera configured to capture an image of the electronic device; and
one or more processors associated with the camera and configured to:
obtain the image;
apply a first machine learning model to the image, wherein the first machine learning model is trained to analyze the image and output a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determine a condition of the electronic device based on the output of the first machine learning model; and
determine, via a second machine learning model different from the first machine learning model, an offer price for the electronic device based on the condition.
1. A system for evaluating a condition of an electronic device, the system comprising:
a kiosk that includes:
one or more cameras configured to capture at least one image of a first side of the electronic device; and
one or more processors in communication with the one or more cameras, the one or more processors configured to:
apply a first machine learning model to the at least one image of the electronic device, wherein the first machine learning model is trained to output, based on analyzing the at least one image, a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determine the condition of the electronic device based on the cosmetic rating; and
determine, via a second machine learning model, an offer price for the electronic device based on the determined condition, wherein the second machine learning model is different from the first machine learning model.
Application being examined 19/000,287 (hereafter ‘287 application)
Conflicting patent 12,223,684 (hereafter ‘684 patent)
10. One or more non-transitory, computer-readable media having instructions that, when executed by one or more processors, cause the one or more processors to perform operations (See claim 1 of ‘684 patent, in which a processor is disclosed to perform similar steps as that of claim 11. Computer-readable medium is inherent.) to evaluate a condition of an electronic device, the operations comprising:
obtaining, via a camera, an image the electronic device;
applying a first machine learning model to the image, wherein the first machine learning model is trained to analyze the image and output a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determining a condition of the electronic device based on the output of the first machine learning model; and
determining, via a second machine learning model different from the first machine learning model, an offer price for the electronic device based on the condition.
11. A computer-implemented method for evaluating a condition of an electronic device, the method comprising:
capturing, by at least one camera of a kiosk, at least one image of a first side of the electronic device;
applying a first machine learning model to the at least one image of the electronic device, wherein the first machine learning model is trained to output, based on analyzing the at least one image, a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;
determining the condition of the electronic device based on the cosmetic rating; and
determining, via a second machine learning model, an offer price for the electronic device based on the determined condition, wherein the second machine learning model is different from the first machine learning model.
Therefore claim 1/11 of the ‘684 patent teaches every limitation in claim 2/10 of the ‘287 application.
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 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 2-4, 9-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over DION et al. (US 20210110366 A1), in view of MCCARTY et al. (US Publication 2019/0019147 A1, hereafter MCCARTY) and Yost (US 20180300776 A1).
As per claim 2, DION teaches a system (Abstract) for evaluating a condition of an electronic device, the system comprising:
a camera configured to capture an image of the electronic device (FIG. 3; para. [0046] “The device entry door 306 is manipulated to control access to a scanning area, referred to herein as the camera chamber, where the device is scanned using one or more cameras”); and
one or more processors associated with the camera (para. [0063], [0079]-[0080], [0096]) and configured to:
obtain the image (para. [0079]-[0080]);
apply a first machine learning model to the image, wherein the first machine learning model is trained to analyze the image and output a brand and/or model of the electronic device (para. [0263]-[0269] “… in order to classify the trade device make and model from images of the trade device itself, the recognition server may use a deep learning algorithm …”) and a cosmetic rating of the electronic device
determine a condition of the electronic device based on the output of the first machine learning model (para. [0272]); and
determine,
DION does not teach a cosmetic rating of the electronic device specific to the brand and/or model.
MCCARTY teaches performing re-inventory testing on an electronic device. Specifically cosmetic defect is detected based on brand and/or model type (para. [0149]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of DION to incorporate the teaching of MCCARTY to consider performing a cosmetic rating of the electronic device specific to the brand and/or model. Doing so would avoid manufacture difference and model difference as recognized by MCCARTY (para. [0149] “For example, a device made by a certain manufacturer may have a different testing procedure than a device made by another manufacturer. Similarly, testing may also vary based on differences between models made by the same manufacturer”).
DION in view of MCCARTY does not further teach using a different machine learning model to determine an offer price for the electronic device based on the condition.
Yost discloses a platform and method for enabling electronic devices to initiate device-related services automatically (Abstract; FIG. 1). Specifically, Yost teaches using a machine learning model to determine an offer price for an electronic device based on condition of the device (para. [0068]; para. [0078] “one or more machine learning and/or pattern recognition techniques may be used to identify a formula that is best able to predict a value of the device”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of DION (as modified by MCCARTY ) to incorporate the teaching of Yost to use a different machine learning model to determine an offer price for the electronic device based on the condition. Doing so would provide a more accurate value since the machine learning model is trained on past valuation data as recognized by Yost (para. [0078]).
As per claim 3, dependent upon claim 2, DION in view of MCCARTY and Yost further teaches the system comprises a kiosk (DION FIG. 3), wherein the kiosk includes the camera (DION FIG. 3) and wherein the one or more processors are in communication with and positioned remotely from the kiosk (DION FIG. 1).
As per claim 4, dependent upon claim 2, DION in view of MCCARTY and Yost further teaches the second machine learning model is configured to determine the offer price based on a sub-model configured to predict a resale value of the electronic device (Yost para. [0038], [0078]).
As per claim 9, dependent upon claim 2, DION in view of MCCARTY and Yost teaches wherein the second machine learning model is configured to use a plurality of sub-models distributed across different locations in a network (DION FIG. 1).
Claim 10, an independent medium claim, recites similar steps corresponding to claim 2. Therefore the recited steps of 10 are mapped to DION in view of MCCARTY and Yost in the same manner as corresponding steps in claim 2. DION additionally teaches system elements, such as a computer-readable media and one or more processors (FIG. 1; para. [0062]).
As per claim 11, dependent upon claim 2, DION in view of MCCARTY and Yost teaches wherein the one or more processors include at least one processor of a kiosk (DION FIG. 1, 3), wherein the kiosk includes the camera (DION FIG. 3), and wherein the instructions, when executed, cause the one or more processors to perform at least a subset of the operations locally at the kiosk via the at least one processor (DION FIG. 3; para. [0045] “In addition to, or in place of the keyboard, the touchscreen may provide displayed buttons and the like that the user can “touch” in order to provide inputs to the computer programs executed on kiosk 302”).
As per claim 13, dependent upon claim 10, DION in view of MCCARTY and Yost teaches wherein the operations further comprise determining whether the image captured by the camera is acceptable or defective, and wherein applying the first machine learning model to the image is performed only when the image captured by the camera is determined to be acceptable (DION para. [0141] “The kiosk may be configured with one or more particular markings for the purpose of being analyzed for quality when the markings are detected in images captured by the trade device's camera(s)”. It means when the markings are not detected, the image’s quality is not acceptable.)
As per claim 14, dependent upon claim 10, DION in view of MCCARTY and Yost teaches wherein the operations further comprise causing the second machine learning model to use a sub-model to predict a resale value of the electronic device, and wherein the second machine learning model is configured to determine the offer price based on the condition and the predicted resale value (Yost para. [0002] “This functionality (or a lack thereof) would presumably impact the device's resale value”; para. [0038], [0068], [0078]).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over DION et al. (US 20210110366 A1), in view of MCCARTY et al. (US Publication 2019/0019147 A1, hereafter MCCARTY) and Yost (US 20180300776 A1), and further in view of Bian et al. (US Publication 2020/0175669 A1, hereafter Bian).
As per claim 7, dependent upon claim 2, DION in view of MCCARTY and Yost does not teach the recited limitations.
Bian in an analogous field discloses an inspection system for detecting at least one defect in a work piece. Specifically a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece are captured. At least some of the images in the first and second sets are acquired using different light settings. Bian further analyzes the first set of images to generate a first prediction image associated with the first position, and analyzes the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. Bian then merges the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions. It is noticed that Bian uses at least one neural network models to generate the first and second prediction images, and to predict the at least one defect. See Abstract, FIG. 1-2 for system, FIG. 3 for position and lighting settings, FIG. 6 for generating a prediction image from a set of images, FIG. 7 for merging two prediction images, and FIG. 8-9 for perdition of defects using neural network. See also corresponding description, for example para. [0020], [0039], [0044], [0070], [0073] and [0080].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of DION (as modified by MCCARTY and Yost) to incorporate the teaching of Bian to include: wherein the image is a combined image that includes multiple images of a side of the electronic device under respective lighting conditions, wherein the camera is configured to capture the multiple images under the respective lighting conditions, and wherein the one or more processors are configured to process and combine the multiple images to generate the combined image. The motivation for doing so would be increasing parts inspection accuracy and certainty by resolving inconsistent and/or limited lighting of the parts, as recognized by Bian (para. [0004]).
As per claim 8, dependent upon claim 2, DION in view of MCCARTY, Yost and Bian teaches wherein the image is a combined image that includes a first image of a first side of the electronic device and a second image of a second side of the electronic device (Bian FIG. 2), wherein the camera is configured to capture the first image and the second image (BIAN FIG. 2), and wherein the one or more processors are configured to process and combine the first image and the second image to generate the combined image (See rejections applied above to claim 7).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over DION et al. (US 20210110366 A1), in view of MCCARTY et al. (US Publication 2019/0019147 A1, hereafter MCCARTY) and Yost (US 20180300776 A1), and further in view of Do et al. (US Publication 2020/0020097 A1, hereafter Do) and Bian et al. (US Publication 2020/0175669 A1, hereafter Bian).
As per claim 12, dependent upon claim 10, DION in view of MCCARTY and Yost teaches capturing multiple images of multiple sides of the electronic device (DION para. [0261] “The instructions may specify that the front, back, and/or sides of the trade device are to be photographed”), but does not teach the rest limitations.
Do teaches normalizing a plurality of images to generate uniform size images (FIG. 14; para. [0098]).
Taking the combined teachings of DION, MCCARTY, Yost and Do as a whole, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to consider normalizing image sizes in order to produce uniform size images for further processing (Do para. [0098]).
DION in view of MCCARTY, Yost and Do does not further teach combining the multiple images into a single image to be provided to the neural network. Bian teaches such limitations (FIG. 7; para. [0020], [0039], [0044], [0070], [0073] and [0080]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of DION (as modified by MCCARTY, Yost and Do) to incorporate the teaching of Bian to combine multiple images to generate a combined mage. The motivation for doing so would be increasing parts inspection accuracy and certainty by resolving inconsistent and/or limited lighting of the parts, as recognized by Bian (para. [0004]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over DION et al. (US 20210110366 A1), in view of MCCARTY et al. (US Publication 2019/0019147 A1, hereafter MCCARTY) and Yost (US 20180300776 A1), and further in view of Bhotika et al. (US Patent 10,824,942, hereafter Bhotika).
As per claim 15, DION in view of MCCARTY and Yost teaches receiving an input from a user indicating an acceptance or a rejection of the offer price (DION para. [0126] “The client may be offered a chance to accept the pre-evaluation payout amount subject to scaled-down evaluation of the device by the kiosk and bypass the time consuming portions of the evaluation process, or to refuse the pre-evaluation payout amount and go through the entire evaluation process”). DION in view of MCCARTY and Yost, however, does not further teach training the second machine learning model based on the image and the input.
Bhotika teaches a method for searching an item shown in an image (Abstract; FIG. 1A-1B). Bhotika further teaches training a neural network for identifying visual attributes of an item. During training, besides training images, metadata including content associated with the images is also used, such as price, brand, designer etc. (col. 17 lines 3-7).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of DION (as modified by MCCARTY, Yost) to incorporate the teaching of Bhotika to train the second machine learning model based on the image and the input. The motivation for doing so would be “non-visual attributes and/or visual attributes that are not associated with the original label grouping may be used to manipulate the feature vector and change the feature vector to a manipulated feature vector”, as recognized by Bhotika (col. 14 ln 16-19)
Allowable Subject Matter
Claims 5-6 and 16-17 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.
Conclusion
Prior art searched but not cited is recorded in PTO-892.
Additional prior art Forutanpour et al. (US 2017/0301078 A1) discloses systems and methods for detecting cracks in an electronic device are disclosed. In one embodiment, the method includes receiving an image of a front side of a mobile device and automatically identifying edges in the image. For given edges among the identified edges, the method includes determining whether another edge among the identified edges is present within a predetermined distance of the given edge. Next, straight line segments corresponding to the edges for which another edge is within the predetermined distance are identified, and then a crack evaluation assessment is assigned to the mobile device based at least in part on the identified straight line segments (Abstract; FIG.1; FIG. 2B).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M Villecco can be reached on (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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