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 .
DETAILED ACTION
This communication is a Final Office Action in response to communications received on 2/25/26.
Claims 13, 17-18 have been cancelled.
Claims 1-5, 7, 10-11, 14-15, 19 have been amended.
Claims 21-23 have been added.
Therefore, Claims 1-12, 14-16, 19-23 are now pending and have been addressed below.
Response to Amendment
Applicant has cancelled Claim 13 to overcome the 35 U.S.C 112 rejections. Examiner withdraws the 35 U.S.C. 112 rejections with respect to these and all depending claims unless otherwise indicated.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 1-12, 14-16, 19-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-8 are directed to a method, claims 9-12 are directed to a non-transitory medium and claims 14-16, 19-23 are directed to a system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-12, 14-16, 19-23 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claim 1 recite methods for grading a collectible including receiving, a notification that a card has been received by a platform, wherein; activating the platform, wherein the platform is transferred into a scanning area; capturing, a set of data associated with the card; analyzing that the set of data associated with the card, to determine if the set of data associated with the card is accurate to a predetermined value and without errors; and analyzing, the set of data to identify defects of the card; generating, a grading value of the card based on the analyzed set of data.
Claim 9 recites a computer program product for grading a collectible including identifying that a collectible has been received and determining that the collectible is at a predetermined position; collecting a set of data associated with a collectible, wherein the set of data includes a plurality of images of the collectible and at least one scan of the collectible; analyzing the set of data to determine if the accuracy of the plurality of images and the at least one scan is of a predetermined accuracy level, wherein if it is determined that the set of data is at or above the predetermined accuracy level; calculating a set of grades of the collectible based on an analysis of the set of data, wherein the set of data is analyzed to identify defects and a severity of the identified defects; converting the set of grades into an overall grade of the card and storing the set of grades and the overall grade; and generating a label containing at least the overall grade.
Claim 14 recites system for grading a collectible including receiving a collectible and generating images and scans of the collectible by a series of image; collecting the series of images and data; to analyze the collected series of images and data to determine any defects or irregularities of the collectible and generate a data set; receiving the data set and generate label; and, wherein the collectible and the label are sealed within a tamper proof case.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) including interaction between person and computer), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as (Claim 1) by at least one processor, a grading machine, the grading machine is a enclosed structure, a plurality of sensors and cameras of the grading machine, (Claim 9) medium, a computing device, a grading machine, at least one camera or sensor, (Claim 14) a grading machine, data capturing devices and sensors, a computing device comprising at least one processor, a processing module, a labeling machine, an encapsulating machine), claims 1, 9 and 14 are directed to grading a collectible and generating a label for collectible. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving collectible data, analyzing it, and providing grading for collectible. In particular, the claims only recites the additional element –(Claim 1) by at least one processor, a grading machine, the grading machine is a enclosed structure, a plurality of sensors and cameras of the grading machine, (Claim 9) medium, a computing device, a grading machine, at least one camera or sensor, (Claim 14) a grading machine, data capturing devices and sensors, a computing device comprising at least one processor, a processing module, a labeling machine, an encapsulating machine. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; provide grading for collectible. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the ((Claim 1) by at least one processor, a grading machine, the grading machine is a enclosed structure, a plurality of sensors and cameras of the grading machine, (Claim 9) medium, a computing device, a grading machine, at least one camera or sensor, (Claim 14) a grading machine, data capturing devices and sensors, a computing device comprising at least one processor, a processing module, a labeling machine, an encapsulating machine), these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0052] details “ The computer readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device. The term “computing system” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. Fig 1 and [0091] Grading System 102 is comprised of a grading machine 102, a labeling machine 10, an encapsulating machine 107, and a computing device 280.” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data, which fall under well-understood, routine and conventional functions of generic computers. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claims do not amount to significantly more than the abstract idea itself.
Dependent claims 2-8, 10-12, and 15-16, 19-23 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as Independent claims.
Claims 2-8 recites identifying, by the at least one processor, that the card is positioned on a platform of the grading machine in a predetermined position; wherein the collecting of the set of data includes images of varying lighting and scans of surfaces and edges of the card; the grading machine receives a signal to reposition the card to capture additional images and scans of the card; wherein a scan of the card identifies a surface structure of the card; integrating, by the at least one processors, the surface structure of the card into a readable code; wherein the grade of the card is calculated based on the different identified defects; wherein if it is determined that the set of data associated with the card is inaccurate or with errors, the card is reinserted into the grading machine and a second set of images and data associated with the card is collected. These limitations further limit the abstract idea of claim 1 and merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 10-12 recites wherein when the set of data associated with the collectible contains a surface scan of the collectible; the surface scan of the collectible is converted into data which is incorporated into the label. wherein the set of data associated with the collectible is compiled in a data base, and further comprising, using the set of data to train a module to detect defects in similar collectibles. These limitations further limit the abstract idea of claim 9 by defining type of data collected. Claim 12 recites the set of data associated with the collectible is compiled in a data base, and further comprising, using the set of data to train a module to detect defects in similar collectibles. The limitations of “training a module to detect defects” such training and applying training to module is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 15-16, 19-23 recites the scanner collected at least one image of the collectible within the tamper proof case; wherein the grading machine, labeling machine, encapsulating machine, and scanner integrated into a single device; the grading machine has a platform for receiving the collectible, wherein the platform is able to translate from a first position to a second position; wherein the platform is able to move in more than one axis; wherein image and data capturing devices and sensors are positioned within the grading machine based on the second position of the platform; wherein the images and scans include edge and surface patterns; wherein the image and data capturing devices and sensors includes a laser, wherein the laser is able to perform 3D mapping of the surface and edges of the collectible. These limitations further limit the abstract idea of claim 14 by collecting specific data. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are merely used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 9-12, 14-16, 19-22 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Frisbee (US 20220036371 A1).
Regarding Claim 9. Frisbee discloses the computer program product for grading a collectible ([0060] each order for grading, which can comprise a request to grade one or more collectable items, [0075] the memory on the computing device has stored thereon or therein instructions that when executed by at least one hardware processor cause the at least one hardware processor to operate the CNNs to perform several tasks), the computer program product comprising: a computer non-transitory readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:
Frisbee discloses identifying that a collectible has been received by a grading machine and determining that the collectible is at a predetermined position within the grading machine by at least one camera or sensor ([0018] placing a collectable item into a first plastic sleeve to form a sleeved collectable item; placing the sleeved collectable item into a receiving space of a hopper, [0024] one or more sensors for detecting sleeved collectable items located in the at least one infeed hopper and the at least one completed hopper. The sleeved collectable items can be sleeved collectable cards, such as trading cards. [0059] an infeed hopper 122 is filled good cards from discreet numbers of requesters or customers, such as from one to fifty customers. For example, an infeed hopper is filled with 68 sleeved cards from customer #344., [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback (notification) to the robot arm 102 as to the locations of the hoppers, whether the hoppers contain sleeved cards, and how high the stacks of cards are in each of the hoppers, among others., [0007] The automated evaluation system can comprise a housing and a light fixture mounted within the housing for providing backlight for the camera. In some examples, front light fixtures for front lighting can also be provided. The housing can have frames and/or panels and a working surface for which to mount various components. [0057] the automated evaluation system or card identifying and inspecting system 100 comprises a robot arm or robot 102 located within a housing 104 (enclosed structure), which can have a frame and panels forming support surfaces for supporting the various components of the automated evaluation system 100. [0062]
Frisbee discloses collecting a set of data associated with a collectible ([0018] obtaining images of a collectable item, [0060] each order for grading, which can comprise a request to grade one or more collectable items, can be located in its own hopper so that each infeed hopper represents a single order from a single requester), wherein the set of data includes a plurality of images of the collectible and at least one scan of the collectible ([0061] each infeed hopper 122 can comprise multiple cards from one requester or from multiple requesters, with each request from a requester referred to as an order. For example, order #1 can comprise ten (10) cards to be graded, order #2 can comprise fifty-five (55) cards to be graded, and order #3 can comprise one (1) card to be graded, etc. Each order can be assigned an order identifier (ID), such as a barcode or a quick response (QR) code. [0079] a user or customer can acquire front and back surface images of a collectable card and then transmit the acquired images to the computing device 150, to the remote server 164, or to the Cloud for use by the system 100 to analyze and inspect the images. [0087] The camera 136 (FIG. 1) then decodes the barcode to obtain an OrderID stored in the control computer 150 (FIG. 2) or elsewhere, such as on the Cloud or a local server, at 182. For example, the camera scans the barcode to acquire data stored into the barcode., [0088] The OrderID is then passed to an application program interface (API) to retrieve order data associated with the particular OrderID at 184. The order data can include such information as the name of the requester, the number of cards associated with the order, and the grading provider's ID No. assigned to each card in the order); wherein the set of data is collected by a set of cameras and sensors contained within the grading machine enclosure ([0087] The camera 136 (FIG. 1) then decodes the barcode to obtain an OrderID stored in the control computer 150 (FIG. 2) or elsewhere, such as on the Cloud or a local server, at 182. For example, the camera scans the barcode to acquire data stored into the barcode., [0088] The OrderID is then passed to an application program interface (API) to retrieve order data associated with the particular OrderID at 184. The order data can include such information as the name of the requester, the number of cards associated with the order, and the grading provider's ID No. assigned to each card in the order. [0090] The camera 136 (FIG. 1) is activated to take one or more images of the sleeved card at 192. Fig 1 #126 sensors, 136 camera, 104 an automated evaluation system);
Frisbee discloses analyzing the set of data to determine if the accuracy of the plurality of images and the at least one scan is of a predetermined accuracy level ([0068] artificial intelligence (AI), such as machine learning (ML), is used to analyze the images for various parameters, such as for scratch, alignment, print quality, etc., [0101] The imaged cards, which can be imaged sleeved cards, are then evaluated and provided with a score based on a number of parameters, such as measurements at step 270, [0102] the protocol can evaluate other card parameters and factors, including edges, corners, color or colors, surface, authenticity, eye appeal, centering, focus or sharpness, print imperfections, staining, scratches, chipping, creases, and optionally other factors at 272-300. Each parameter or characteristic reviewed can be given a score.); wherein if it is determined that the set of data is at or above the predetermined accuracy level ([0095] At 232, the measurements are compared to expected values (predetermined accuracy level) for the specific card product and specific card, which may herein be referred to as Spec#. The system queries whether the measurements are within correct thresholds at 234. If yes, then the software displays measurement data to card graders during the grading process at 236)
Frisbee discloses calculating a set of grades of the collectible based on an analysis of the set of data ([0102] Each parameter or characteristic reviewed can be given a score. The scores for each factor or characteristic are evaluated by the machine learning model that outputs a grade or score on a grading scale, such as 1 to 10 at step 302. In an example, the first pass score for the collectable card can be an average score, rounded to the nearest whole number, obtained from the plurality of scores of the different factors and characteristics reviewed. In another example, one or more of the different factors and characteristics can be weighted more than others. For example, if the authenticity score is 6, its produced score from the AI model can be 1.1 times 6, or 6.6. ), wherein the set of data is analyzed to identify defects and a severity of the identified defects ([0096] If the measurements are not within correct thresholds, the protocol queries whether the measurements meet an automatic rejection threshold at 238. For example, a particular card may have its edges trimmed in order to remove frayed edges. However, if the trimming is excessive, then the card dimensions can measure well below an acceptable threshold for that particular card type. Thus, if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming” (severity of defect). At 242, the measurement data of the rejected card is then added to the measurement database, [0113] The images are then algorithmically compared at 384 and measurements derived from prior and current images are compared at 386. A composite image is then generated and presented to an expert or grader to highlight changes (defects) to the card at 388.) ;
Frisbee discloses converting the set of grades into an overall grade of the card ([0102] Each parameter or characteristic reviewed can be given a score. The scores for each factor or characteristic are evaluated by the machine learning model that outputs a grade or score on a grading scale, such as 1 to 10 at step 302. In an example, the first pass score for the collectable card can be an average score, rounded to the nearest whole number, obtained from the plurality of scores of the different factors and characteristics reviewed. In another example, one or more of the different factors and characteristics can be weighted more than others. For example, if the authenticity score is 6, its produced score from the AI model can be 1.1 times 6, or 6.6. [0108] At 350, the protocol queries whether the human grade matches the computer-generated first-pass grade. If yes, the grade is finalized at 352. If no, the card is evaluated by a second human grader at 354. Human graders can confer and can issue a final score or grade that differs from the computer generated first-pass grade or score using machine learning.) and storing the set of grades and the overall grade ([0108]The card images and final grade are then added to the database at 356.); and
Frisbee discloses generating a label containing at least the overall grade. ([0048] taking images of incoming collectable items and issuing a barcode and a unique identifier for each sleeved collectable item. [0092] additional information may be printed with the barcode, such as a grade, a message, a date, etc. The sleeved card that has been imaged and has a barcode specific placed onto the sleeve is then placed into a completed hopper at 204. Thus, each processed card can have a barcode and a unique identifier, such as a unique grader service provider ID, associated therewith attached to the sleeve so that each card can be tracked by the barcode and the unique identifier. [0120] the card can be imaged on the front and back sides and then placed directly into a clear protective housing with a final grade without first being placed inside a sleeve. [0130] A label 558 can also be added to the interior of the card holder 570. The label 558 can be provided with information regarding the collectable card, identification number, barcode and/or QR code that can be used to access a database to verify the identification or certification number, name of the grading entity, and a final grade “#”, among other information. )
Regarding Claim 10. Frisbee discloses the computer program product of claim 9,
Frisbee discloses wherein when the set of data associated with the collectible contains a surface scan and edge scan of the collectible ([0068] compiling the images of the sleeved card to determine parameters such as edges of the cards, staining, surface quality, and card dimensions. [0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system. [0092] additional information may be printed with the barcode, such as a grade, a message, a date, etc., [0038] surface parameter [0095] the pixel dimensions are converted to physical dimensions. Precise angles and parallelism of the card edges are also measured at 230. At 232, the measurements are compared to expected values for the specific card product and specific card)
Regarding Claim 11. Frisbee discloses the computer program product of claim 10,
Frisbee discloses wherein the surface scan and edge scan of the collectible is converted into defect map.([0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system. [0092] additional information may be printed with the barcode, such as a grade, a message, a date, etc. The sleeved card that has been imaged and has a barcode specific placed onto the sleeve is then placed into a completed hopper at 204. Thus, each processed card can have a barcode and a unique identifier, such as a unique grader service provider ID, associated therewith attached to the sleeve so that each card can be tracked by the barcode and the unique identifier. [0096] If the measurements are not within correct thresholds, the protocol queries whether the measurements meet an automatic rejection threshold at 238. For example, a particular card may have its edges trimmed in order to remove frayed edges. However, if the trimming is excessive, then the card dimensions can measure well below an acceptable threshold for that particular card type. If so, the card can be rejected as most collectors would not deem the excessively trimmed card desirable. Thus, if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming” (defect map). [0130] A label 558 can also be added to the interior of the card holder 570. The label 558 can be provided with information regarding the collectable card, identification number, barcode and/or QR code that can be used to access a database to verify the identification or certification number, name of the grading entity, and a final grade “#”, among other information. )
Regarding Claim 12. Frisbee discloses the computer program product of claim 9,
Frisbee discloses wherein the set of data associated with the collectible is compiled in a database ([0094] The measurements obtain are compared against existing measurement data for cards from the same type stored in the database of card images. The new measurement data is then added to the database and stored for future reference., [0032] a) accessing a first image and a second image from the database, the first image comprising a front side image of a first sleeved collectable item and the second image comprising a back side image of the first sleeved collectable item), and further comprising, using the set of data to train a module to detect defects in similar collectibles ([0070] the automated evaluation system 100 can continue to learn to recognize attributes, such as by updating its knowledge base to reflect information added to the database to retrain and/or by human intervention. For example, the AI model can be re-trained or fine-tuned to improve detection precision.).
Regarding Claim 14. Frisbee discloses the system comprising:
Frisbee discloses a grading machine designed for receiving a collectible and generating images and scans of the collectible by a series of image and data capturing devices and sensors (Fig 9 # 502 automated evaluation system (grading system), [0122] automated evaluation system 500 comprises a housing 502, which can be a cubicle-like space comprising a plurality of walls or panels defining a working space 504 having a plurality of automated evaluation system components located therein, or adjacent thereto or nearby. At least one infeed hopper 122 and at least one completed hopper 124 are provided with the automated evaluation system 500, [0123] A working platform 132 can be provided for placing a sleeved card 134, [0124] ] One or more images of the front side of the sleeved card 134 can be taken by the camera 136 located directly above the sleeved card 134. For example, two or more images can be taken of the front side of the sleeve card, for example three images, five images, or ten images. The images can be enhanced using one or more light fixtures 138, 140 for generating different lighting conditions for obtaining a set of images to facilitate processing of the sleeved card 134. Alternatively or additionally, light fixtures 139 can be placed below the sleeved card, below the camera 136, to generate backlighting or backlight during the imaging., [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback to the robot arm 102 as to the locations of the hoppers, whether the hoppers contain sleeved cards, and how high the stacks of cards are in each of the hoppers, among others., );
Frisbee discloses a computing device comprising at least one processor for collecting the series of images and data incorporated into the grading machine ([0079] the image acquisition system 136a with one or more cameras 136 can be housed locally with the computing device 150. For example, the image acquisition system 136a can be incorporated with the computing device 150. In other words, the computing device 150 can be configured as part of a device for capturing and/or storing images from the image acquisition system 136a, such as for storing data files captures by the cameras. Fig 9 #150 computing device);
Frisbee discloses a processing module to analyze the collected series of images and data to determine any defects or irregularities of the collectible and generate a data set ([0068] artificial intelligence (AI), such as machine learning (ML), is used to analyze the images for various parameters, such as for scratch, alignment, print quality, etc.;
Frisbee discloses a labeling machine, capable of receiving the data set and generate a label ([0096] if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming”. At 242, the measurement data of the rejected card is then added to the measurement database, [0097] if the measurements do not meet the automatic rejection threshold, a size warning is then added to a card grader interface during the grading process at 244. Thus, during grading process, the grader can see the warning associated with the card and can determine whether the card is properly sized for grading at 246, [0119] if the answer is no, the grader is presented with a likely match for verification and/or correction at 424. The confidence factor is then adjusted at 426 and the card is assigned an internal identifier or Spec# at 414. ); and
Frisbee discloses an encapsulating machine, wherein the collectible and the label are sealed within a tamper proof case ([0130] the collectable card 500 is placed inside an interior of a card holder 570, which is typically made from a two-piece clear plastic housing that irreversibly locks together to provide a secured housing that is extremely difficult or impossible to separate. A label 558 can also be added to the interior of the card holder 570. The label 558 can be provided with information regarding the collectable card, identification number, barcode and/or QR code that can be used to access a database to verify the identification or certification number, name of the grading entity, and a final grade “#”, among other information., [0003] tamper-evident holder)
Regarding Claim 15. Frisbee discloses the system of claim 14, further comprising,
Frisbee teaches a scanner, wherein the scanner collected at least one image of the collectible. (Fig 9 # 510 scanner [0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system. [0003] seals the card in a tamper-evident holder, and assigns a serial number to the card along with information about the card for future reference.)
Regarding Claim 16. Frisbee discloses the system of claim 14,
Frisbee teaches wherein the grading machine, labeling machine, encapsulating machine, and scanner integrated into a single device. (Fig 9 #502 and [0122] automated evaluation system 500 comprises a housing 502, which can be a cubicle-like space comprising a plurality of walls or panels defining a working space 504 having a plurality of automated evaluation system components located therein, or adjacent thereto or nearby, [0125] The working platform 132 can comprise markers or boundaries to guide the technician on where to place the sleeved card 134. Alternatively, the sleeved card 134 can be imaged using a scanner 510., [0127] method 1000 for evaluating, inspecting, and/or authenticating sleeved cards in accordance with aspects of the invention. In an example, a customer or an order is placed to evaluate, inspect, authenticate and/or grade one or more cards, with only one collectable card 550 shown schematically. As discussed above, the card 550 is then placed inside a plastic sleeve 552 to create a sleeved card 134. An order identifier 554 is then place on the exterior of the sleeve 552. [0130] A label 558 can also be added to the interior of the card holder 570. The label 558 can be provided with information regarding the collectable card, identification number, barcode and/or QR code, [0133] the robotic system for identifying and grading collectable items and components thereof have been specifically described and illustrated herein, many modifications and variations will be apparent ).
Regarding Claim 19. Frisbee discloses the system of claim 14,
Frisbee teaches wherein the grading machine has a platform for receiving the collectible, wherein the platform is able to translate from a first position to a second position. ([0063] the working platform 132 can have more than one designated location for placement of the sleeved card. As shown, the working platform 132 has three designated locations with the sleeved card 134 being placed in the middle designated location. In other examples, there can be a different number of designated locations, such as two or more than three. The different locations can also be on different elevations. A sleeved card can be placed in any of the designated locations on the working platform and more than one sleeved card can be placed on the working platform at a time. [0066] the flipping mechanism 144 can be a pick and place robot that picks up the sleeved card 134, flips the sleeved card, then places the flipped sleeved card back onto the same designated location of the working platform 132 for further imaging of the second side of the sleeved card. The working platform 132 can be structured to cooperate with the flipping mechanism, such as incorporates cut-outs, openings, and different geometries to accommodate for the movements and requirements of the flipping mechanism., [0092] The card is moved past a printer 160 which prints a barcode specific to the sleeved card that has just been imaged and then places the barcode onto the plastic sleeve at 202. ) wherein the second position is enclosed within the grading machine ([0060] the processed sleeved cards, after being imaged, are placed inside a completed hopper 124. Once a completed hopper is filled with processed sleeved cards, the robot arm then places the next completed sleeved card in a next available completed hopper 124 [0062]) For example, proximity sensors may be used to sense the location of the hoppers. The information provided by the sensors is used by the robot arm 102 to move the sleeved cards between the hoppers to process the cards, as further discussed below. The sensors 126 can be mounted on brackets 128 located within the housing 104 or to brackets that formed part of the housing frame (grading machine) ([0063] the working platform 132 can have more than one designated location for placement of the sleeved card. As shown, the working platform 132 has three designated locations with the sleeved card 134 being placed in the middle designated location).
Regarding Claim 20. Frisbee discloses the system of claim 19,
Frisbee teaches wherein the platform is able to move in more than one axis. ([0058] A robot hand 116 comprising one or more fingers 118 is provided at the end of the robot arm 102, which is attached to the robot arm via a movable end joint 114 that can be configured to rotate along an axis and tilt., [0063] the sleeved collectable item can be a sleeved collectable card. The sleeved card 134 has a front side and a back side, or a first side and a second side. The robot arm 102 can pick up the sleeved card 134 from the first infeed hopper 122a, as an example, and then place the sleeved card 134 onto the working platform 132 with the front side or first side of the sleeved card facing upward, to face the camera 136.[0066] The working platform 132 can be structured to cooperate with the flipping mechanism, such as incorporates cut-outs, openings, and different geometries to accommodate for the movements and requirements of the flipping mechanism.)
Regarding Claim 21. (New) Frisbee discloses the system of claim 14,
Frisbee teaches wherein image and data capturing devices and sensors are positioned within the grading machine based on the second position of the platform (Fig 1 # 126 sensors, 136 camera [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback to the robot arm 102 as to the locations of the hoppers [0063] The robot arm 102 can pick up the sleeved card 134 from the first infeed hopper 122a, as an example, and then place the sleeved card 134 onto the working platform 132 with the front side or first side of the sleeved card facing upward, to face the camera 136. The camera can be a high-resolution digital color camera with a fixed focal length camera lens to acquire a desired field of view from a mounted or fixed working distance with reference to a focal plane, such as the surface of the sleeved card when the sleeved card is placed onto the working platform)
Regarding Claim 22. (New) Frisbee discloses the system of claim 14,
Frisbee teaches wherein the images and scans include edge and surface patterns. ([0063] The camera can be a high-resolution digital color camera with a fixed focal length camera lens to acquire a desired field of view from a mounted or fixed working distance with reference to a focal plane, such as the surface of the sleeved card when the sleeved card is placed onto the working platform. Enabling the same camera to capture collectable items of different heights or distances to the camera lens. [0068] compiling the images of the sleeved card to determine parameters such as edges of the cards, staining, surface quality, and card dimensions. [0126] the automated evaluation system or card identifying and inspecting system 500 uses image data generated by the camera 136, or cameras, to perform a task or a set of tasks related to authenticating and evaluating the conditions of the sleeved card 134.compiling the images of the sleeved card to determine parameters such as edges of the cards, staining, surface quality, and card dimensions)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Frisbee (US 2022/0036371 A1) in view of Mahajan et al. (US 10,753,882 B1)
Regarding Claim 1. Frisbee discloses the computer-implemented method ([0005] an automated evaluation system that uses artificial intelligence (AI) that have been trained to analyze collectable cards for various parameters and authenticity.)comprising:
Frisbee discloses receiving, by at least one processor, a notification that a card has been received by a platform of a grading machine ([0018] placing a collectable item into a first plastic sleeve to form a sleeved collectable item; placing the sleeved collectable item into a receiving space of a hopper, [0024] one or more sensors for detecting sleeved collectable items located in the at least one infeed hopper and the at least one completed hopper. The sleeved collectable items can be sleeved collectable cards, such as trading cards. [0059] an infeed hopper 122 is filled good cards from discreet numbers of requesters or customers, such as from one to fifty customers. For example, an infeed hopper is filled with 68 sleeved cards from customer #344., [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback (notification) to the robot arm 102 as to the locations of the hoppers, whether the hoppers contain sleeved cards, and how high the stacks of cards are in each of the hoppers, among others.); wherein the grading machine is a enclosed structure ([0007] The automated evaluation system can comprise a housing and a light fixture mounted within the housing for providing backlight for the camera. In some examples, front light fixtures for front lighting can also be provided. The housing can have frames and/or panels and a working surface for which to mount various components. [0057] the automated evaluation system or card identifying and inspecting system 100 comprises a robot arm or robot 102 located within a housing 104 (enclosed structure), which can have a frame and panels forming support surfaces for supporting the various components of the automated evaluation system 100. [0062]
Frisbee discloses activating, by the at least one processor, the platform, into a scanning area within the grading machine ([0019] moving the sleeved collectable item with the robotic arm onto the working platform and activating the camera to take one or more images of the sleeved collectable item. [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback (activate) to the robot arm 102 as to the locations of the hoppers, whether the hoppers contain sleeved cards, and how high the stacks of cards are in each of the hoppers, among others. For example, proximity sensors may be used to sense the location of the hoppers. The information provided by the sensors is used by the robot arm 102 to move (transferred) the sleeved cards between the hoppers to process the cards [0082] At 180, the robotic arm 102 (FIG. 1) takes the top sleeved card from one of the infeed hoppers 122 off the stack and places the item onto one of the designated locations of the photography jig or working platform 132 (FIG. 1). For purposes of discussions, the first sleeved item can be assumed to be the information order barcode or sleeved OrderInfo card);
Frisbee discloses capturing, by the at least one processor, a set of data associated with the card via a plurality of sensors and cameras of the grading machine ([0087] The camera 136 (FIG. 1) then decodes the barcode to obtain an OrderID stored in the control computer 150 (FIG. 2) or elsewhere, such as on the Cloud or a local server, at 182. For example, the camera scans the barcode to acquire data stored into the barcode., [0088] The OrderID is then passed to an application program interface (API) to retrieve order data associated with the particular OrderID at 184. The order data can include such information as the name of the requester, the number of cards associated with the order, and the grading provider's ID No. assigned to each card in the order. [0090] The camera 136 (FIG. 1) is activated to take one or more images of the sleeved card at 192.);
Frisbee discloses analyzing, by the at least one processor, that the set of data associated with the card, to determine if the set of data associated with the card is accurate to a predetermined value and without errors ([0072] the user interface 152 may be used to provide attributes (predetermined value) of a sleeved card, such as by designating the card as “authentic, [0094] FIG. 4 is a schematic diagram representing a protocol 220 for card measurement and size verification using the automated evaluation system. The goal of this process is to check measurements (predetermined value) of each card to determine if they fall within a baseline for the type of card in question and to reject or flag the card for closer examination if they do not. The measurements obtain are compared against existing measurement data for cards from the same type stored in the database of card images. [0095] The system queries whether the measurements are within correct thresholds at 234. If yes, then the software displays measurement data to card graders during the grading process at 236., [0102] the protocol can evaluate other card parameters and factors, including edges, corners, color or colors, surface, authenticity, eye appeal, centering, focus or sharpness, print imperfections, staining, scratches, chipping, creases, and optionally other factors at 272-300. Each parameter or characteristic reviewed can be given a score. if the authenticity score is 6, its produced score from the AI model can be 1.1 times 6, or 6.6). , [0068] classify collectable item defect types (such as parameters with low scores for poor quality or high scores for being pristine), collectable item classes (such as particular types or series), and/or whether the collectable items are authentic based on images of the collectable items. claim 14 authentication score );
Frisbee discloses analyzing, by the at least one processor, the set of data to identify defects of the card ([0096] If the measurements are not within correct thresholds, the protocol queries whether the measurements meet an automatic rejection threshold at 238. For example, a particular card may have its edges trimmed in order to remove frayed edges. However, if the trimming is excessive, then the card dimensions can measure well below an acceptable threshold for that particular card type. Thus, if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming”. At 242, the measurement data of the rejected card is then added to the measurement database, [0113] The images are then algorithmically compared at 384 and measurements derived from prior and current images are compared at 386. A composite image is then generated and presented to an expert or grader to highlight changes (defects) to the card at 388.) ;
Frisbee discloses generating, by the at least one processor, a grading value of the card based on the analyzed set of data ([0096] if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming”. At 242, the measurement data of the rejected card is then added to the measurement database, [0097] if the measurements do not meet the automatic rejection threshold, a size warning is then added to a card grader interface during the grading process at 244. Thus, during grading process, the grader can see the warning associated with the card and can determine whether the card is properly sized for grading at 246, [0119] if the answer is no, the grader is presented with a likely match for verification and/or correction at 424. The confidence factor is then adjusted at 426 and the card is assigned an internal identifier or Spec# at 414., [0130] The scores for each factor or characteristic are evaluated by the machine learning model that outputs a grade or score on a grading scale, such as 1 to 10 at step 302. In an example, the first pass score for the collectable card can be an average score, rounded to the nearest whole number, obtained from the plurality of scores of the different factors and characteristics reviewed. The collectable card 500 is placed inside an interior of a card holder 570, which is typically made from a two-piece clear plastic housing that irreversibly locks together to provide a secured housing (slab) that is extremely difficult or impossible to separate. A label 558 can also be added to the interior of the card holder 570. The label 558 can be provided with information regarding the collectable card, identification number, barcode and/or QR code that can be used to access a database to verify the identification or certification number, name of the grading entity, and a final grade “#”, among other information.)
Frisbee does not specifically teach wherein the platform is transferred into a scanning area within the grading machine
Mahajan teaches the platform is transferred into a scanning area within the grading machine (Fig 2 #202 and Col 5 lines 56-60 a set of inspection and cosmetic grading units 100 with an external conveyance system 202 which transports an object to a unit for evaluation. A conveyance system may comprise frames supporting rollers, wheels or belts and may be motor powered or manual devices. Col 7 lines 37-42 object conveyance or feeding 202, 522, 524 and 526. The automated object controller 516 performs automated object motions, movements and object pick, place and handling functions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the platform is transferred into a scanning area within the grading machine, as disclosed by Mahajan in the system disclosed by Frisbee, for the motivation of providing a conveyance system 202 which transports an object to a unit for evaluation (Col 5 lines 56-60 Mahajan)
Regarding Claim 2. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches wherein the received notification further comprises, identifying, by the at least one processor, that the card is positioned on a platform of the grading machine in a predetermined position. ([0015] One or more sensors can be provided to detect the location presence of the hoppers and sleeved collectable items. [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback (notification) to the robot arm 102 as to the locations of the hoppers, whether the hoppers contain sleeved cards, and how high the stacks of cards are in each of the hoppers, among others, [0024] and claim 4 one or more sensors for detecting sleeved collectable items located in the at least one infeed hopper and the at least one completed hopper., [0063] the working platform 132 can have more than one designated location for placement of the sleeved card. As shown, the working platform 132 has three designated locations with the sleeved card 134 being placed in the middle designated location. In other examples, there can be a different number of designated locations, such as two or more than three. The different locations can also be on different elevations. [0088] The OrderID is then passed to an application program interface (API) to retrieve order data associated with the particular OrderID at 184.);
Regarding Claim 3. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches collect data related to the surface patterns and edge patterns of the card ([0025] The system can further comprise a housing and light fixture mounted within the housing for providing backlight for the camera., [0064] One or more images of the front side of the sleeved card 134 can be taken by the camera 136 located directly above the sleeved card 134. For example, two or more images can be taken of the front side of the sleeve card, for example three images, five images, or ten images. The images can be enhanced using one or more light fixtures 138, 140 for generating different lighting conditions for obtaining a set of images to facilitate processing of the sleeved card 134 The light fixtures can be placed below the sleeved card, below the camera, to generate backlighting or backlight during the imaging. Backlighting allows the system to better evaluate edges of the sleeve card 134 from surfaces or areas that do not form part of the card from the images taken. [0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system. [0008] scanners may be used to capture images of the front and back of cards to be analyzed by the automated evaluation system or card identifying and inspecting system, [0126] the automated evaluation system or card identifying and inspecting system 500 uses image data generated by the camera 136, or cameras, to perform a task or a set of tasks related to authenticating and evaluating the conditions of the sleeved card 134.compiling the images of the sleeved card to determine parameters such as edges of the cards, staining, surface quality, and card dimensions)
Frisbee does not specifically teach plurality of sensors are able to collect data related to the surface patterns and edge patterns of the card
Mahajan teaches plurality of sensors are able to collect data related to the surface patterns and edge patterns of the card (Col 3 lines 65-67, Col 4 lines 2-6 An inspection and cosmetic grading machine as disclosed herein may comprise four modular subsystems: an image capturing apparatus, image processing module, object/material handling components, and depth sensing apparatus. Image capture apparatus includes modular hardware components, such as camera (lens, image sensor) and lighting systems that may be interchanged for evaluating products of different sizes, colors, materials and expected defect size and type, Abstract Detected defects are localized and measured for depth of defect by an advanced optical sensor (laser & UV sensor), providing a 3D representation of defects., Fig 3 #306 optical sensor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included plurality of sensors are able to collect data related to the surface patterns and edge patterns of the card, as disclosed by Mahajan in the system disclosed by Frisbee, for the motivation of providing a method of measuring depth of defect by an advanced optical sensor (laser) and providing a 3D representation of defects. (Abstract Mahajan)
Regarding Claim 4. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches wherein the analysis of the set of data identifies predetermined errors, further comprising, generating, by the one or more processors, an alert, wherein the alert is associated with reposition of the card. ([0022] A automated evaluation system (grading machine) for identifying and grading collectable items [0064]the sleeved card 134 is flipped by a flipping mechanism or flipping device 144 and placed back onto the working platform 132 with the back side of the sleeved card facing upward, such as facing the camera 136. One or more images, such as two or more images, of the back side or second side of the sleeved card 134 can be taken by a camera 136, under different lighting conditions produced by the light fixtures 138, 140, including with backlighting., [0069] Other components may be housed in the base 106 for transmitting instructions (alert) to the robot arm or for controlling functions of the robot arm, such as one or more processors or microcontrollers [0068] the robot arm 102 may be tasked with picking up and placing a sleeved card from an infeed hopper 122 onto the working platform 132, pausing for one or more images to be taken by the camera 136, pausing for the flipping mechanism 144 to flip the sleeved card, pausing for the camera to take additional images of the second side, and then picking and placing the sleeved card into the completed hopper 124. [0113] The images are then algorithmically compared at 384 and measurements derived from prior and current images are compared at 386. A composite image is then generated and presented to an expert or grader to highlight changes (defects) to the card at 388.) )
Regarding Claim 5. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches wherein a scan of the card identifies a surface structure of the card using sensor ([0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system. [0087] the camera scans the barcode to acquire data stored into the barcode., [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback to the robot arm 102 as to the locations of the hoppers)
Frisbee does not specifically teach using an ultra violet light sensor
Mahajan teaches using an ultra violet light sensor (Abstract Detected defects are localized and measured for depth of defect by an advanced optical sensor (laser & UV sensor), providing a 3D representation of defects., Fig 3 #306 optical sensor, Claim 9 sweeping an advanced optical spot laser sensor perpendicular to the defect at the point of highest reflected light intensity, and generating a vertical displacement signal)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included using an ultra violet light sensor, as disclosed by Mahajan in the system disclosed by Frisbee, for the motivation of providing a method of measuring depth of defect by an advanced optical sensor (laser) and providing a 3D representation of defects. (Abstract Mahajan)
Regarding Claim 6. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 5, further comprising,
Frisbee teaches integrating, by the at least one processors, the surface structure of the card into a readable code. ([0061] A divider can alternatively be a machine readable identifier, such as a symbol or a tab, that lets the automated evaluation system knows that a new or different order from the analyzed order is up next. [0092] additional information may be printed with the barcode, such as a grade, a message, a date, etc. The sleeved card that has been imaged and has a barcode specific placed onto the sleeve is then placed into a completed hopper at 204. Thus, each processed card can have a barcode and a unique identifier, such as a unique grader service provider ID, associated therewith attached to the sleeve so that each card can be tracked by the barcode and the unique identifier. )
Regarding Claim 7. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches wherein the grade of the card is calculated based on the different identified defects.([0050] a computer to generate a first-pass grade for a collectable item. [0095] If the measurements are not within correct thresholds, the protocol queries whether the measurements meet an automatic rejection threshold at 238. For example, a particular card may have its edges trimmed in order to remove frayed edges. However, if the trimming is excessive, then the card dimensions can measure well below an acceptable threshold for that particular card type (defect). if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming” [0097] if the measurements do not meet the automatic rejection threshold, a size warning is then added to a card grader interface during the grading process at 244. Thus, during grading process, the grader can see the warning associated with the card and can determine whether the card is properly sized for grading at 246. If not, then the card is rejected at 240. If yes, then the grader renders a grade for the cards, such as a number between 1 and 10, [0100] a protocol 264 for teaching a computer to generate a first-pass grade for a trading card, which can be re-checked by one or more human graders at later stages. The process attempts to have the computer render an initial opinion, which is then confirmed or rejected by one or more human graders, [0073] the AI model analyzes card images and output or classifies information about the card images, such as the card's border is off, the colors are off, the signature appears fake, etc., but the ultimate grading or card scoring is performed manually by one or more human graders. In an example, one or more human graders provide or give a collectable card a grade, between 1-10, using information generated by AI model, which includes analyzing images of the card using the AI model.)
Regarding Claim 8. Frisbee as modified by Mahajan teaches the computer-implemented method of claim 1,
Frisbee teaches wherein if it is determined that the set of data associated with the card is inaccurate or with errors ([0096] a particular card may have its edges trimmed in order to remove frayed edges. However, if the trimming is excessive, then the card dimensions can measure well below an acceptable threshold for that particular card type. If so, the card can be rejected as most collectors would not deem the excessively trimmed card desirable. Thus, if the card meets an automatic rejection threshold, then the card is rejected and marked as such at 240. In an example, the card can be marked as “Minimum Size Requirement” or “Evidence of Trimming”), the card is reinserted into the grading machine and a second set of images and data associated with the card is collected. ([0102] The card is then evaluated by a human grader at step 304. [0106] a computer to generate a first-pass grade for a trading card, which can be re-checked by one or more human graders at later stages., [0112] The card images are analyzed using computer vision algorithm to generate feature point vectors at 376. A digital fingerprint generated from the feature factors is compared to stored digital fingerprints in the database at 378. [0113] the protocol queries whether there are additional images to process at 390 and return to 376 if yes)
Regarding Claim 23. (New) Frisbee discloses the system of claim 14,
Frisbee teaches wherein the image and data capturing devices and sensors compile images of the surface and edges of the collectible. (Fig 1 # 126 sensors, 136 camera [0062] A plurality of sensors 126 can be located above the hoppers 122, 124 to provide feedback to the robot arm 102 as to the locations of the hoppers [0068] compiling the images of the sleeved card to determine parameters such as edges of the cards, staining, surface quality, and card dimensions. [0079] The image acquisition system 136a can alternatively or additionally include an image scanner 510 (FIG. 9) for scanning front and back surface images of collectable cards to be analyzed by the system.)
Frisbee does not specifically teach a laser, wherein the laser is able to perform 3D mapping of the surface and edges of the collectible.
Mahajan teaches a laser, wherein the laser is able to perform 3D mapping of the surface and edges of the collectible. (Abstract Detected defects are localized and measured for depth of defect by an advanced optical sensor (laser), providing a 3D representation of defects., Fig 3 #306 optical sensor, Claim 9 sweeping an advanced optical spot laser sensor perpendicular to the defect at the point of highest reflected light intensity, and generating a vertical displacement signal)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a laser, wherein the laser is able to perform 3D mapping of the surface and edges of the collectible, as disclosed by Mahajan in the system disclosed by Frisbee, for the motivation of providing a method of measuring depth of defect by an advanced optical sensor (laser) and providing a 3D representation of defects. (Abstract Mahajan)
Response to Arguments
Applicant's arguments filed 2/25/26 have been fully considered but they are not persuasive.
Regarding 101 rejection, applicant states that claims recite practical application and provides a technical solution. Examiner has considered all arguments and respectfully disagrees. 101 rejection has been updated based on the amendments. Further, the judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving collectible data, analyzing it, and providing grading for collectible. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Regarding the improvement remark, applicant does not point out what improvement is achieved, further claims/specification do not provide details on how the improvement is achieved.
Applicant’s arguments with respect to claims prior art rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shalamberidze (US 2022/0261984) discloses receiving a set of images of collectables, where each image is associated with a defect type label. A set of preprocessed images is generated based on the images by detecting a boundary defining the collectable in the image, performing image analysis on segments of images to grade images of collectables.
Mahajan (US 10,753,882) discloses a system and method for inspection and cosmetic grading of objects is provided. Camera and lighting assemblies capture images of an object and create a 2D composite image which is processed by an image processing module with a deep learning machine algorithm to detect surface defects in the object. Detected defects are localized
Kass (US 10,teaches a tamper proof case (Col 2 lines 4-49 A professionally graded card is inspected for authenticity and rated on various criteria, for its condition. The card is then assigned an overall grade, generally from 1-10, sealed in a tamper-proof holder (slab) and assigned a certification number (identifying information) that is maintained by the grading company. Increasing the value of the graded card in comparison to an ungraded card of equal or similar condition by means of offering the card owner or buyer an assurance of the card’s authenticity and condition. (Col 1 lines 49-54 Kass). Kass teaches set of data associated with the card is accurate and without errors (Col 25 lines 58-67, Col 26 lines 1-4 a person may be granted access to the database 130 and confirm that the owner of the collectible 116 is as represented by a third party. This feature permits a buyer of a collectibe116 to confirm that the collectible 116 is authentic and that the seller is the legitimate owner). Kass teaches grading system integrated into a single device (Fig 1 #102 and Col 6 lines 41-50The computerized system 100 comprises an image acquisition device 102 and a computer system 104. The image acquisition device 102 comprises an imaging device 106, a housing 108 defining an internal space 110, a stage 112, and at least one light source 114 to illuminate at least part of the internal space 110, wherein the stage 112 is within the housing 108 and receives a collectible 116. Col 7 lines 23-30 the imaging device is a scanner, the scanner can be physically isolated from or replace and/or supplement the housing 108 and can have its own self-contained stage 112, internal space 110, at least one light source 114, and a sensor to capture the image of the collectible on the surface. Such a scanner device is intended to be understood as an alternative or supplement in any description of the system herein. One example of a scanner can be a flatbed scanner.)
Potter (US 2021/0065353) discloses a grade report can be created for the collectible card being graded. This grade report can be based on the results of applying the card feature model to the isolated spatial features of the card. Many or all of the feature to feature comparisons can be considered, and this can be done by way of averaging or otherwise considering the scoring of many different spatial feature comparisons.
Kass (US 2023/0325392) discloses system for grading and authenticating sport and non-sport card collectibles and other printed objects including event ticket, event programs, photographs, photograph facsimiles, brochures, and the like. The horizontal configuration may allow for the system to be on a same surface to allow for the collectible to be transported from component to component. The horizontal configuration may allow for a conveyor belt to be utilized to transport the collectible, either manually or automatically, to different stages/components of the system during the grading process In another example, such as in the vertical configuration, at least some of the components of the system 3700 may be arranged in a vertical configuration to reduce a footprint of the system 3700.
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 SANGEETA BAHL whose telephone number is (571)270-7779. The examiner can normally be reached 7:30 - 4PM.
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, Jessica Lemieux can be reached at 571-270-3445. 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.
/SANGEETA BAHL/Primary Examiner, Art Unit 3626