AIA
Claims 1-2 4-5, 7-11,13-14,16-22 examined for US Ser 18881778 filed 1/7/2025
Canceled 3 6 12 15
New none
Amended 1 9 12-14 16-20
PNG
media_image1.png
241
265
media_image1.png
Greyscale
Response to Remarks
Applicant amendment remarks fully considered but unfortunately not fully persuasive.
Examiner thanks Attorney for the amendment to advance prosecution.
112 withdrawn
103
Applicant amendment met with new rejection below.
CLAIM REJECTIONS - 35 USC § 103
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.
MPEP 2123: “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for ALL they contain.” In re Heck, 699 F.2d 1331 (Fed. Cir. 1983) A reference may be relied upon for ALL that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).”
Claims 1-2 4-5, 7-11,13-14,16-22 rejected under 35 USC 103 over
1 Applicant Sheltzerzoom (Inventor Cheng-Shorland, WO 2020123464) in view of
2 McMillon (ML Models Via Non-Fungible Tokens On A Digital Ledger) US 20230315877in view of
3 Kartoun US 20240428127
CLAIM 1 9 20
1. A method of providing a computer model as a tradeable asset, comprising:
Applicant Sheltzerzoom (Inventor Cheng-Shorland, WO 2020123464) at least ¶ 10
O preparing a trained computer model, comprising developing and training a computer model using training data
Although McMillon shows
iterating a process of feature selection, algorithm selection, model building and model testing until the acceptance criteria are met.
MACHINE-LEARNING MODELS VIA NON-FUNGIBLE TOKENS ON A DIGITAL LEDGER
US 20230315877
[0034] In some examples, the online portal 126 can include an online marketplace for buying, licensing, and selling the machine-learning model version 114 or the training data 115. The marketplace may be searchable by users to locate the machine-learning model version 114 or the training data 115 that matches their search criteria.
NOT EXPLICIT IN Sheltzerzoom is all of the following
O determining acceptance criteria for the computer model such that the computer model is determined to be a trained computer model when the acceptance criteria are met, wherein the developing and training comprises iterating a process of feature selection, algorithm selection, model building using the training data, and model testing using test data, and
evaluating results of the model testing against the acceptance criteria
Kartoun US 20240428127 Fig 3 Fig 6 + text
Abstract
A training process a predictive model uses a dataset of features and an outcome. The method generates a table for a dataset comprising multiple features, the table contains values for each pair of features in the dataset, randomly selects features from the dataset, thereby creating a first subset of features, operates a propensity score matching using the randomly selected features to identify cases and controls using the outcome variable, rewards one or more features of a second subset of features in the multiple features that were not selected randomly, each feature of the second subset addresses a statistical significance criteria, updating each entry in the table with a reward distance between each pair of features, calculates a cumulative reward measure, iterating the steps until convergence, selects a final subset of features when a variability criteria of the cumulative reward measure addresses convergence criteria, and trains the predictive model.
Background/Summary
[0001] The present disclosure relates to machine learning, and more specifically, to improving the computerized performance of subpopulation-based feature selection by iteratively assessing convergence level.
[0003] Feature selection methods are useful for identifying the most informative features in a dataset. Under current methodologies, two problems may arise associated with the pre-defined arbitrary number of iterations. First, the method may stop iterating before identifying all the informative features, resulting in under-selection. Second, the method may continue to run unnecessarily, even after all informative features have been identified, leading to unnecessary computational processing. To avoid these problems, it is important to incorporate convergence assessment methodologies and criteria into feature selection methods.
[0004] According to one embodiment of the present invention, a feature selection method ranks features according to level of importance. A subset of these features could be used for a variety of purposes, including to train a predictive model. A plurality of subsets of features are randomly selected from a dataset comprising a plurality of cases and controls and a plurality of features. Cases and controls are matched to select a plurality of case-control subsets for each subset of features, each case-control subset having similar values for the corresponding subset of features. For each case-control subset, a statistical significance of each feature of the plurality of features absent from the subset of features used to match the case-control subset is identified and rewarded numerically. Subsets are continuously generated randomly and the cases and controls are matched in each iteration. The computer system includes a convergence function configured to determine when to cease the iterative process once a convergence criteria has been determined. If the method runs iterations that are found to result only a minor or no change in determining a final list of selected informative features then it reaches convergence and stops. The most important features are then used for a variety of computational purposes, such as to train a predictive model, for clustering, and to serve as an input for a foundation model.
It would have been obvious to combine Shelterzoom, Kartoun. This is simply -- Combining Prior Art Elements According to Known Methods. All the claimed elements were known in the prior art and one skilled in the art could have combined the elements by known methods with no change in their functions to yield predictable results using feature selection.
O packaging the trained computer model for use by a third party and storing the trained computer model securely
WO 2020123464 at least ¶ 13 encrypt the document, and store an encrypted version
O establishing a token corresponding to the trained computer model , wherein establishing the token comprises applying a hash function to the trained computer model and signing a hash result with a model creator private key
WO 2020123464 at least ¶ 13 the token server system may also generate a document token corresponding to the document and transmit the document token to the digital wallet
O posting the token and transactions in the token to a blockchain such that the token is adapted for use as a tradeable asset and
WO 2020123464 at least ¶ 13 the token server system may publish the link and the cryptographic has to a blockchain using one or more smart contract functions
O providing access to the trained computer model to a third party who has acquired rights to use the trained computer model through obtaining rights in the token
WO 2020123464 at least
¶ 14 the document token may be deposited in a digital wallet indicating ownership of each of the one or more documents
¶ 105 at 335, token server system 110 may transmit or transfer the document token to a third digital wallet to provide interaction with the second portion according to the second permission. In this manner, different users may interact with different portions of the document based on the distributed document token
NOT EXPLICT IN WO 2020123464
O preparing a trained computer model, comprising developing and training a computer model using training data
O determining acceptance criteria for the computer model such that the computer model is determined to be a trained computer model when the acceptance criteria are met
MACHINE-LEARNING MODELS VIA NON-FUNGIBLE TOKENS ON A DIGITAL LEDGER
US 20230315877
[0034] In some examples, the online portal 126 can include an online marketplace for buying, licensing, and selling the machine-learning model version 114 or the training data 115. The marketplace may be searchable by users to locate the machine-learning model version 114 or the training data 115 that matches their search criteria.
The difference between primary reference and secondary reference is document versus model, both workpieces. Combining the references is obvious and a simple substitution. It is further Combining Prior Art Elements According to Known Methods.
CLAIM 2
NOT EXPLICT IN primary references is ML
2. The method of claim 1, wherein the
O trained computer model is a machine learning model.
MACHINE-LEARNING MODELS VIA NON-FUNGIBLE TOKENS ON A DIGITAL LEDGER
US 20230315877
[0034] In some examples, the online portal 126 can include an online marketplace for buying, licensing, and selling the machine-learning model version 114 or the training data 115. The marketplace may be searchable by users to locate the machine-learning model version 114 or the training data 115 that matches their search criteria.
The difference between primary reference and secondary reference is document versus model. Both are mere workpieces. Combining the references is obvious and a simple substitution. It is further Combining Prior Art Elements According to Known Methods.
CLAIM 4 10 13
4. The method of any preceding claim 1, wherein
O packaging the trained computer model further comprises a model creator digitally signing the trained computer model.
WO 2020123464 ¶ 55 signed doc encrypted and stored
CLAIM 5 14 21
5. (Currently Amended) The method of any preceding claim 1, wherein
O storing the trained computer model securely comprises storing the trained computer model in encrypted form encrypted by a key controlled by a model creator or model owner.
WO 2020123464 ¶ 55 signed doc encrypted and stored
CLAIM 7 16 18 22
7. (Currently Amended) The method of any preceding claim 1, wherein providing access to the trained computer model to the third party comprises establishing a shared secret between the third party and a model owner or model creator, and encrypting means of access to the trained computer model using the shared secret.
WO 2020123464 ¶ 89 KEY
CLAIM 8 17 19
8. (Original) The method of claim 7, wherein the
O shared secret is established using Diffie-Hellman Key Exchange.
WO 2020123464 ¶ 89 KEY
POC
Pertinent prior art
US 20140304086 buy model
WO 2020123464
WO 2021248214
EP 3 786 872 A1
PNG
media_image2.png
182
345
media_image2.png
Greyscale
During prosecution, applicant has an opportunity and a duty to amend ambiguous claims to clearly and precisely define the metes and bounds of the claimed invention The claim places the public on notice of the scope of the patentee’s right to exclude See, eg, Johnson & Johnston Assoc Inc v RE Serv Co, 285 F3d 1046, 1052, 62 USPQ2d 1225, 1228 (Fed Cir 2002) (en banc) As stated in Halliburton Energy Servs, Inc v M-I LLC, 514 F3d 1244, 1255, 85 USPQ2d 1654, 1663 (CAFC 2008):
“We note that the patent drafter is in the best position to resolve the ambiguity in the patent claims, and it is highly desirable that patent examiners demand that applicants do so in appropriate circumstances so that the patent can be amended during prosecution rather than attempting to resolve the ambiguity in litigation”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BREFFNI X BAGGOT whose telephone number is (571)272-7154. The examiner can normally be reached M-F 8a-10a, 12p-6p.
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, Waseem Ashraf can be reached at 571-270-3948. 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.
BREFFNI BAGGOT
Primary Examiner
Art Unit 3621
/BREFFNI BAGGOT/Primary Examiner, Art Unit 3621
Prosecution Insights