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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/6/25 has been entered.
Response to Amendment
Applicant's arguments filed 2/25/26, have been fully considered but they are not persuasive. Applicant did not point out exactly where in the disclosure the limitations of generating using the embedding model, an action embedding vector are supported or defined with closed-ended definitions. It is the examiner’s position that the limitations generating using the embedding model amount to mathematical concepts (PGPUB: “As used herein, the term “embedding model” refers to a computer-implemented model or algorithm that generates embeddings”, see 0037; “an embedding model (e.g., a neural network) to generate an action embedding vector”, 0016).
Applicant argued USC 101.
In regards to the limitations “digital actions” they are not further defined so limitations do no align with 101 example 39. Example 39 specifies only additional elements and applying one or more transformations to each digital facial image including mirroring, rotating, smoothing or contrast reduction to create a modified set of digital facial images.
A computer-implemented method of training a neural network for facial detection comprising:
• collecting a set of digital facial images from a database;• applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images;• creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images;• training the neural network in a first stage using the first training set;• creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and• training the neural network in a second stage using the second training set.
In response, it is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do").
The claims amount to data analysis/manipulation and using some form of AI as a tool.
Claiming AI on a high level can amount to using a black box without specifying any real details of how the AI operates or what’s in the black box. The claims need to specify the technical details of the AI.
Although the claims may specify an improvement they are only improving the abstract idea not a computer.
"The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea." MPEP § 2106.04(a)(2).III. "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." Id. For the purposes of this abstract idea, "[t]he courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
Regarding remarks pertaining to practical application, claims do not specify a clear practical application.
In order for an abstract idea to be integrated into a practical application, the improvement in a given technical field must be a byproduct of the additional elements. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself”, as stated in MPEP 2106.5 (1). Applicant should state where within the claim limitations such an improvement is made.
Practical applications must be additional elements, not abstract ideas. Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception.
"It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception." paragraph is on 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022].
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
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 - see MPEP 2106.05(e) and Vanda Memo.
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, 4-7, 9-10, 21, 24-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1, 4-7, 9-10, 21, 24-35 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 21, 28:
2A Prong 1:
determining, from the digital action space, a first set of sampled digital actions that were previously performed according to a logging policy and a second set of sampled digital actions performed according to a target policy, the second set of sampled digital actions including one or more digital actions that were unobserved under the logging policy (abstract idea of analyzing data. Mental process. A human-mind with pen and paper can determine data);
generating, a set of action embedding vectors (generating vectors is mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation) by
generating, using the embedding model, an additional action embedding vector within the embedding space from each digital action from the second set of sampled digital actions (abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate data; this process reads on performing math; user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
generating a projected value metric indicating the projected performance of the target policy utilizing the set of action embedding vectors (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation, process amounts to math, see e.g., applicant’s specification 0059-0070);
determining, for the set of action embedding vectors (generating vectors/cluster is read as a mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation) and using a probabilistic binary classifier (see below), a set of policy predictions that indicate each action embedding vector as corresponding to a digital action from the logging policy or the target policy (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data; digital actions are not defined).
2-3. (Canceled).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Processor, memory/device/system (claim 28), (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
Claim 1, a non-transitory computer-readable medium storing instructions that (adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
using an embedding model (adding 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 - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction)
the probabilistic binary classifier (adding 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 - see MPEP 2106.05(f)) having parameters generated over multiple iterations (the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning) to reduce an error (reads on training and/or retraining for the purpose of optimizing of trying to reach convergence) of the probabilistic binary classifier based on comparing predicted classifications for training digital actions and corresponding labels indicating whether each training digital action is from the target policy or the logging policy.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Processor, memory/device/system (claim 28), (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
Claim 1, a non-transitory computer-readable medium storing instructions that (adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
using the embedding model (adding 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 - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. The claim(s) is not patent eligible
the probabilistic binary classifier (adding 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 - see MPEP 2106.05(f)) having parameters generated over multiple iterations (the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning) to reduce an error (reads on training and/or retraining for the purpose of optimizing of trying to reach convergence) of the probabilistic binary classifier based on comparing predicted classifications for training digital actions and corresponding labels indicating whether each training digital action is from the target policy or the logging policy.
4, 24, 29. (Original) The non-transitory computer-readable medium of claim 1,the operations further comprise
determining, utilizing the set of action embedding vectors, a density ratio between the logging policy and the target policy (abstract idea of analyzing data. Mental process. A human- mind with pen and paper can determine data); and
generating the projected value metric indicating the projected performance of the target policy utilizing the set of action embedding vectors comprises generating the projected value metric utilizing the density ratio (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
5, 25, 30. (Original) The non-transitory computer-readable medium of claim 4, wherein determining, utilizing the set of action embedding vectors, the density ratio between the logging policy and the target policy comprises estimating the density ratio from the set of action embedding vectors (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation) utilizing a probabilistic binary classifier (adding 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 - see MPEP 2106.05(f)).
6, 26. (Original) The non-transitory computer-readable medium of claim 1, wherein identifying the target policy for the determination of the projected performance within the digital action space comprises identifying a recommendation policy for recommending digital content (abstract idea of analyzing data. Mental process. A human-mind with pen and paper can determine data).
7, 27. (Original) The non-transitory computer-readable medium of claim 1, wherein determining the first set of sampled digital actions performed according to the logging policy comprises generating the first set of sampled digital actions in response to a plurality of queries using the logging policy (abstract idea of analyzing data. Mental process. A human-mind with pen and paper can determine data).
9. (Original) The non-transitory computer-readable medium of claim 1, wherein generating, utilizing the embedding model (Adding 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 - see MPEP 2106.05(f)), the set of action embedding vectors by generating, and action embedding vector (generating vectors is mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation) for each digital action from the first set of sampled digital data action and for each digital action from the second set of sampled digital actions comprises generating, for a sampled digital action from the set of sampled digital actions, an action embedding vector having a lower dimensionality than the sampled digital action and that summarizes attributes of the sampled digital action(abstract idea of analyzing data. Mental process. A human-mind with pen and paper can determine data).
10. (Original) The non-transitory computer-readable medium of claim 1, wherein the operations further comprise implementing the target policy to perform the digital actions represented within the digital action space based on the projected value metric (abstract idea of analyzing data. Mental process. A human-mind with pen and paper can determine data).
31. (New) The non-transitory computer-readable medium of claim 4, wherein
determining the density ratio between the logging policy and the target policy comprises
determining the density ratio using an embedding permutation weighting estimator (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
32. (New) The non-transitory computer-readable medium of claim 1, wherein
generating the set of action embedding vectors using the embedding model comprises generating the set of action embedding vectors using a multi-modal embedding vector trained to generate action embedding vectors for multiple types of digital actions(generating vectors is mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
33. (New) The non-transitory computer-readable medium of claim 1, wherein determining the first set of sampled digital actions that were previously performed according to the logging policy comprises determining the first set of sampled digital actions for a previously used recommendation policy that recommends digital images from a set of digital images (abstract idea of analyzing data. Mental process. A human- mind with pen and paper can recommend/determine data).
34, (New) The method of claim 24, wherein determining the density ratio between the logging policy and the target policy comprises determining the density ratio using an embedding permutation weighting estimator(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
35. (New) The method of claim 21, wherein generating the set of action embedding vectors using the embedding model comprises generating the set of action embedding vectors using a multi-modal embedding vector trained to generate action embedding vectors for multiple types of digital actions(Adding 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 - see MPEP 2106.05(f)).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mudumba US (2022/0300585) discloses generating embedding vectors (0129).
Conclusion
THIS ACTION IS MADE FINAL. 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 DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
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, Alexey Shmatov can be reached at 5712703428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123