DETAILED ACTION
In the response filed August 15, 2025, the Applicant elected Group II, claims 12-16; and has withdrawn Group I, claims 1-11. Claims 12-16 are pending in the current application.
Notice of 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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character not mentioned in the description: “421 Provide Unverified Sales Data” as found in Figure 4. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Election/Restrictions
Applicant's election without traverse of Group II, claims 12-16 in the reply filed on August 15, 2025, is acknowledged. Applicant argues amended claims 1-11 should be examined together as independent claim 1 is written to depend from claim 12. This is not found persuasive because independent claim 1 is of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 1 claims dependence on the system claim of claim 12 wherein claim 1 is a method claim and claim 12 is not a preceding claim.
In addition, claims 1-11 are drawn to a process for training a machine learning model whereas 12-16 are drawn to a device for determining an estimated price for collectable cards. The inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). The requirement is deemed proper and is therefore made FINAL.
Claim Objections
Claim 12 is objected to because of the following informalities: Claim 12 recites the limitation “collectible” in lines 6 and 7. Collectible in line 6 and 7 should read --collectable-- as this appears to be a typographical error of the limitation “collectable” as found in lines 1, 11, and 12. Appropriate correction is required.
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 12-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 12-16 are drawn to a machine, which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 12 recites/describes the following steps:
“collecting … information regarding the collectible cards, the …information including values of the collectible cards, and also at least one piece of information related to the card other than the value of the card,”
“receiving card data of the collectable card;” and
“…determine the estimated price of the collectable card.”
These steps, under broadest reasonable interpretation, describe or set-forth collecting information regarding a collectable card and determining an estimated price of the collectable card, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any elements recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A – Prong Two:
The claims recite the additional elements/limitations of: “a price estimation system…, the price estimation system comprising: one or more computer devices having a computer processor and computer memory,” “datasets,” and “a machine learning model,” (claim 12).
The claims recite the additional elements/limitations of: “at least one piece of information…encoded into a number vector,” and “training a machine learning model using the collected datasets;” (claim 12).
The requirement to execute the claimed steps/functions using “a price estimation system…, the price estimation system comprising: one or more computer devices having a computer processor and computer memory,” “datasets,” and “a machine learning model,” “at least one piece of information…encoded into a number vector,” and “training a machine learning model using the collected datasets;” (claim 12), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 13-16 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a price estimation system…, the price estimation system comprising: one or more computer devices having a computer processor and computer memory,” “datasets,” and “a machine learning model,” “at least one piece of information…encoded into a number vector,” and “training a machine learning model using the collected datasets;” (claim 12), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 13-16 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
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.
Claims 12, 13, 15, and 16, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goonetilleke (US 2023/0109001 A1).
Regarding claim 12, Goonetilleke discloses a price estimation system for determining an estimated price for collectable cards (Par. [0027], an asset refers to any physical object, which may be listed on a server by a provider and viewed by a consumer. Examples of assets include, but are not limited to, trading cards), the price estimation system comprising:
one or more computer devices having a computer processor and computer memory, the computer memory storing executable code that, when executed by the computer processor, enables the computer system to perform a process (Par. [0073], a chipset 410 coupled to at least one processor 405. Coupled to the chipset 210 is volatile memory 415, a network adapter 420, an input/output (I/O) device(s) 425, a storage device 430 representing a non-volatile memory, and a display 435) that comprises the following steps:
collecting datasets of information regarding the collectible cards, the datasets of information including values of the collectible cards (Par. [0036], The extrinsic data store 320 maintains a record of extrinsic data compiled for and associated with a particular asset and timestamps assigned to each entry of extrinsic data describing when the entry was compiled. As described herein, extrinsic data represents events or circumstances that would impact or affect the classification of an asset, for example historical prices of the asset, current events or news related to the asset), and also at least one piece of information related to the card other than the value of the card, which is encoded into a number or a vector (Par. [0039], For example, the year a player wins the Most Valuable Player award, the classification of the trading card for the player during that year will increase. Alternatively, the year a player sits out due to injury, the classification of their trading card will decrease. Accordingly, the extrinsic data compiler 330 compiles extrinsic data for an asset at a present time to determine the classification of the asset at the present time. The extrinsic data compiler 330 transmits extrinsic data gathered for an asset to the component vector encoder 340. The component vector encoder 340, in turn, generates principal components of the asset from the transmitted extrinsic data and encodes those principal components into a format to be input to a machine-learned model, for example a feature vector);
training a machine learning model using the collected datasets (Par. [0053], The machine-learning model is trained based on a training dataset including a population of assets and, for each asset of the training dataset, an aggregation of principal components as labeled by a historical classification and a time associated with the entry);
receiving card data of the collectable card (P.ar [0078], Upon receiving the image of the asset, the asset classification platform 130 extracts 420 intrinsic data from the asset); and
using machine learning to determine the estimated price of the collectable card (Par. [0020], “classification” by the asset classification platform 130 is the result of inputting attributes extracted from and compiled for an asset into a machine-learned model to characterize the asset in a manner that provides insight to both providers and consumers. An asset may be classified according to a predicted popularity of the asset or level of consumer interest in the asset, a rarity of the asset, a condition of the asset, a value of the asset, or a price of the asset; Par. [0079], The output of the trained model is a predicted classification of the asset).
Regarding claim 13, Goonetilleke discloses the price estimation system of claim 12. Goonetilleke also discloses wherein the at least one piece of information related to the card other than the value of the card is encoded into a real number or a vector (Par. [0039], For example, the year a player wins the Most Valuable Player award, the classification of the trading card for the player during that year will increase. Alternatively, the year a player sits out due to injury, the classification of their trading card will decrease. Accordingly, the extrinsic data compiler 330 compiles extrinsic data for an asset at a present time to determine the classification of the asset at the present time. The extrinsic data compiler 330 transmits extrinsic data gathered for an asset to the component vector encoder 340. The component vector encoder 340, in turn, generates principal components of the asset from the transmitted extrinsic data and encodes those principal components into a format to be input to a machine-learned model, for example a feature vector).
Regarding claim 15, Goonetilleke discloses the price estimation system of claim 12. Goonetilleke also discloses wherein the at least one piece of information related to the card other than the value of the card is information indicative of popularity of the cards (Par. [0082], The extrinsic data compiler 330 may also compile extrinsic data related to the subject of the asset, such as statistical performances, betting odds, popularity).
Regarding claim 16, Goonetilleke discloses the price estimation system of claim 15. Goonetilleke also discloses wherein the information indicative of popularity of the card includes a number of the same cards manufactured, a number of cards manufactured for the same player of the card, and a number of times the same cards, and/or similar cards, were requested to be graded by an entity engaged in grading of cards (Par. [0056], the component vector encoder may determine a conversion relationship between the classification determined for a first variety based on the classification determined for a second variety under the same conditions. For example, a first variety of trading cards may be designed as “blue” cards, indicating that 200 copies of the particular card were printed. A second variety of trading cards may be designated as “gold” cards, indicating that only 10 copies of the particular card was printed – “wherein the information indicative of popularity of the card includes a number of the same cards manufactured, a number of cards manufactured for the same player of the card”).
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.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Goonetilleke (US 2023/0109001 A1) in view of Cabrita Condessa et al. (US 2024/0126247 A1), hereinafter Condessa.
Regarding claim 14, Goonetilleke discloses the price estimation system of claim 12. Goonetilleke does not explicitly disclose wherein the at least one piece of information related to the card other than the value of the card is encoded into a floating point number or a vector represented as an array of floating point numbers. Condessa teaches wherein the at least one piece of information is encoded into a floating point number or a vector represented as an array of floating point numbers (Par. [0032], This model is configured to embed a set of measurement data (e.g., of varying type described above, such as floating precision number, string, integer, Boolean, time series measurements, aggregation of statistics, etc.) into a float array or vector that the dynamics model can consume). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the price estimation system of Goonetilleke to include the vector abilities of Condessa to teach “wherein the at least one piece of information related to the card other than the value of the card is encoded into a floating point number or a vector represented as an array of floating point numbers ,” since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patrick Kim whose telephone number is (571)272-8619. The examiner can normally be reached Monday - Friday, 9AM - 5PM EST.
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, Resha Desai can be reached at (571)270-7792. 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.
/Patrick Kim/Examiner, Art Unit 3628
Prosecution Insights