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 .
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 .
Response to Amendment
This Action is responsive to the Applicant’s Amendment/Remarks filed on 05/22/2026. In the Amendment, applicant amended claims 1, 7 and 15. As necessitated by the Amendment, Examiner hereby respectfully withdraws 35 U.S.C § 101 rejections to claims 1-20.
As to Arguments and Remarks filed in the Amendment, please see Examiner’s responses shown after Rejections - 35 U.S.C § 103.
Please note claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 03/16/2026 has been considered (see form-1449, MPEP 609).
Examiner Notes:
Case would be allowable if the applicant provide the detailing of the features of “confidence prediction in selecting a prompt generator when receiving any set of future training prompts to interact and respond to a user providing said set of prompts”.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Clement et al. (US PGPUB 2022/0398462, hereinafter Clement), in view of Lyman et al. (US PGPUB 2022/0051114, hereinafter Lyman) and further in view of Jain et al. (US PGPUB 2021/0241139, hereinafter Jain).
As per as claim 1, Clement discloses:
A computer-implemented method comprising:
causing generating, by a trained model, a training prompt response to a training prompt in a set of training prompts (Clement, e.g., [0032], [0037-0038], “...cloud platform offers various configurations of neural transformer models with attention. Neural transformers models are one type of deep learning that utilizes an attention mechanism....focus on a subset of features or tokens in an input sequence thereby learning different representations from the different positions of the tokens in an input sequence...”);
training, using the training prompt and the training prompt response, an attribution model, the training resulting in a trained attribution model (Clement, e.g., [0044-0046], “...uses bi-directional attention which enables the encoder to learn the relationships of the tokens/subtokens in an input sequence both before and after their occurrence. Classifiers are trained to interpret a model's internal representation into a class label. Since bi-directional attention allows the model's internal representation to depend on all other tokens...”); and
attributing, using the trained attribution model and a first prompt response generated by a fine-tuned model in response to a prompt, the fine-tuned model to a foundation model (Clement, e.g., [0042], [0050-0052] and [0076-0081], “...generating input sequences of tokens. The pre-processing script may use a tokenizer to turn the user's fine-tuning dataset into a sequence of tokens having the same token base used by the pre-trained deep learning model... the fine-tuning script replaces the output layer of the pre-trained model with a classification layer specific for the task-specific embeddings while reusing all encoder blocks...”).
generating an attribution confidence prediction which is based on said classifier (Clement, e.g., [0036-0037], “…learn from data and to predict future decisions… machine learning since it uses multiple stages of data processing… generating more accurate predictions…” [0044-0045], “…Classifiers are trained to interpret a model's internal representation into a class label… predicts an ordered sequence of tokens where the order depends on the preceding tokens in the sequence…”); and
using said classifier and said attribution confidence prediction in selecting a prompt generator when receiving any set of future training prompts to interact and respond to a user providing said set of prompts.
To make records clearer regarding to the language of “using the training prompt and the training prompt response an attribution model” (although as stated above, Clement functional disclose the feature of “using the training prompt and the training prompt response an attribution model” (Clement, e.g., 0044-0046])).
However Lyman, in an analogous art, discloses “using the training prompt and the training prompt response an attribution model” (Clement, e.g., [0067-0072], “...data attributes of an entry 352, 354, 356, and/or 358 can refer to data included in the entry itself or that is otherwise mapped to an identifier included in the entry and can be retrieved from, added to, modified... in training sets used to train processes used by one or more subsystems such as the medical scan image analysis system...” and [0097], “...the training set data can indicate one or more training set identifiers 491 indicating one or more medical scan analysis functions that utilized the medical scan in their training set, and/or indicating a particular version identifier... based on model parameter data 623 of the corresponding medical scan analysis functions...”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Lyman and Clement for generate inference process visualization data for a medical scan indicating an inference process flow of plurality of sub-models applied to the medical scan and further indicating a plurality of inference data for the medical scan generated by applying the plurality of sub-models in accordance with the inference process flow (Lyman, e.g., [abstract]).
To further clarify the features of “using said classifier and said attribution confidence prediction in selecting a prompt generator when receiving any set of future training prompts to interact and respond to a user providing said set of prompts”.
However Jain, in an analogous art, discloses “using said classifier and said attribution confidence prediction in selecting a prompt generator when receiving any set of future training prompts to interact and respond to a user providing said set of prompts” (Jain, e.g., [0010-0011], “… models can be machine learning models, for example, a neural networks or classifiers…Different types of models can be used together as an ensemble or for making different types of predictions…” and [0083-0086], “…user interface that selects one of the multiple different analysis techniques. Selecting one of the multiple different analysis techniques includes selecting the analysis technique indicated by the user interaction with the user interface”, [0303], “… prompt the subject with the interface after the subject attempts or successfully completes a challenge or goal, after the subject has attempted or successfully completed all challenges or goals for a given assessment date, or after an assessment date has passed (e.g., responses may be requested from the subject the next day)…” and [0311-0313], “… interface displays a prediction that the subject…readiness score, risk score, and predications may be determined by the scoring and prediction module…determined by the machine learning models…selection of either of the options can result in the subject…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Jain, Lyman and Clement for use a machine learning model to predict when a subject will reach a certain level of capability to be able to perform a task or will achieve another result (Jain, e.g., [003-005]).
As per as claim 2, the combination of Jain, Lyman and Clement disclose:
The computer-implemented method of claim 1, further comprising:
generating, using the trained attribution model and a vocabulary, an additional training prompt (Clement, e.g., [0047-0048], “... training procedure, data normalization and vocabulary encoding procedures are hyperparameters that are tailored to meet a particular objective. The parameters of the model are the values of the model, such as the weights (e.g., Q, K, V), biases, subtoken and positional...”) and (Jain, e.g., [0303], and [0311-0313); and
adding, to the set of training prompts, the additional training prompt (Clement, e.g., [0067-0070], “... inputs to the decoder block 334 are added with the positional embeddings...”) and (Lyman, e.g., [0067-0072]) and (Jain, e.g., [0303], and [0311-0313).
As per as claim 3, the combination of Jain, Lyman and Clement disclose:
The computer-implemented method of claim 1, wherein the trained model comprises a trained fine-tuned model (Clement, e.g., [abstract], [0042], [0050-0052] and [0076-0081], “...generating input sequences of tokens. The pre-processing script may use a tokenizer to turn the user's fine-tuning dataset into a sequence of tokens having the same token base used by the pre-trained deep learning model... the fine-tuning script replaces the output layer of the pre-trained model with a classification layer specific for the task-specific embeddings while reusing all encoder blocks...”).
As per as claim 4, the combination of Jain, Lyman and Clement disclose:
The computer-implemented method of claim 1, wherein the trained model comprises a trained foundation model and a trained fine-tuned model, and the training prompt response comprises a response of the trained foundation model to the training prompt and a response of the trained fine-tuned model to the training prompt (Clement, e.g., [abstract], [0042], [0050-0052] and [0076-0081], “...generating input sequences of tokens. The pre-processing script may use a tokenizer to turn the user's fine-tuning dataset into a sequence of tokens having the same token base used by the pre-trained deep learning model... the fine-tuning script replaces the output layer of the pre-trained model with a classification layer specific for the task-specific embeddings while reusing all encoder blocks...” and further see [0096-0098]) and (Jain, e.g., [009-0011], “… use a machine learning model to predict when a subject will reach a certain level of capability to be able to perform a task or will achieve another result … models can be machine learning models, for example, a neural networks or classifiers. Other types of models that may be used include support vector machines, regression models, reinforcement learning models, clustering models, decision trees, random forest models, genetic algorithms, Bayesian models, and Gaussian mixture models…models can be trained to predict completion times using training data examples indicating the steps needed for similar tasks…”).
As per as claim 5, the combination of Jain, Lyman and Clement disclose:
The computer-implemented method of claim 1, wherein the attributing comprises generating a model attribution confidence score (Lyman, e.g., [0071-0074], “... confidence score data 460, display parameter data 470, similar scan data 480, training set data 490, and/or other data relating to the medical scan...” and further see [0077-0081], “...determined by comparing some or all of confidence score data 460 to a threshold, can be determined by comparing a probability value to a threshold, and/or can be determined by comparing another continuous or discrete value indicating a calculated likelihood ...”) and (Jain, e.g., [0247-0248], [0345], “… machine learning training…provide high accuracy and confidence in the scoring output…”).
As per as claim 6, the combination of Jain, Lyman and Clement disclose:
The computer-implemented method of claim 1, wherein the attributing is performed using a pair of prompt responses, the pair of prompt responses comprising the first prompt response and a second prompt response, the second prompt response generated by the foundation model in response to the prompt (Lyman, e.g., [0058-0059], “...interface feature evaluator system 110 can be operable to generate an ordered image-to-prompt mapping by selecting a set of user interface features to be displayed with each of an ordered set of medical scans. The set of medical scans and the ordered image-to-prompt mapping can be transmitted to a set of client devices. A set of responses can be generated by each client device in response to sequentially displaying each of the set of medical scans in conjunction with a mapped user interface feature indicated in the ordered image-to-prompt mapping via a user interface...” and [0111], [0268], [0283-0284], “...The interface can prompt the user to indicate the appropriate scan category 1120 and/or prompt the user to confirm and/or edit the inferred scan category...”) (the examiner asserts, multiple input/prompts to select the data in the category = pair of prompt responses) and further see [0054], [0095], “...mapping pair in medical label alias database..”) and (Jain, e.g., [0303], and [0311-0313).
Claim 22 is essentially the same as claim 1 except that it set forth the claimed invention as a computer program product rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claim 1.
As per as claim 8, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (Clement, e.g., [0045], [0067-0068], [0104], [0147], “...stored and run locally, stored and run by another subsystem 101, and/or stored in the medical scan analysis function database 346, where the function and/or parameters of the function can be retrieved from the database by the medical scan diagnosing system...”).
As per as claim 9, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
program instructions to meter use of the program instructions associated with the request (Lyman, e.g., [0303], and [0309-0320], “...the lesion size, shape, diameter, and/or volume, and/or other characteristics of the lesion such as other abnormality classification data 445 can be determined for each scan, and the changes in these features over time can be measured and tracked...determined to shrink, grow, or disappear over subsequent medical scans, and/or new lesions can be detected to appear over subsequent medical scans. Performing such calculations automatically by utilizing the lesion tracking system 3002 can generate more precise measurements than those generated by a radiologist's visual inspection of one or more medical scans...”); and
program instructions to generate an invoice based on the metered use (Lyman, e.g., [0077-0080], [0088-0089], [0100], [0141], “generate quality score”) (the examiner asserts generating quality score which is equivalent to generate an invoice based on the metered use) .
As per as claim 10, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, further comprising:
generating, using the trained attribution model and a vocabulary, an additional training prompt (Clement, e.g., [0047-0048], “... training procedure, data normalization and vocabulary encoding procedures are hyperparameters that are tailored to meet a particular objective. The parameters of the model are the values of the model, such as the weights (e.g., Q, K, V), biases, subtoken and positional...”) and (Jain, e.g., [0303], and [0311-0313); and
adding, to the set of training prompts, the additional training prompt (Clement, e.g., [0067-0070], “... inputs to the decoder block 334 are added with the positional embeddings...”) and (Lyman, e.g., [0067-0072]) and (Jain, e.g., [0303], and [0311-0313).
As per as claim 11, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the trained model comprises a trained fine-tuned model (Clement, e.g., [abstract], [0042], [0050-0052] and [0076-0081], “...generating input sequences of tokens. The pre-processing script may use a tokenizer to turn the user's fine-tuning dataset into a sequence of tokens having the same token base used by the pre-trained deep learning model... the fine-tuning script replaces the output layer of the pre-trained model with a classification layer specific for the task-specific embeddings while reusing all encoder blocks...”).
As per as claim 12, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the trained model comprises a trained foundation model and a trained fine-tuned model, and the training prompt response comprises a response of the trained foundation model to the training prompt and a response of the trained fine-tuned model to the training prompt (Clement, e.g., [abstract], [0042], [0050-0052] and [0076-0081], “...generating input sequences of tokens. The pre-processing script may use a tokenizer to turn the user's fine-tuning dataset into a sequence of tokens having the same token base used by the pre-trained deep learning model... the fine-tuning script replaces the output layer of the pre-trained model with a classification layer specific for the task-specific embeddings while reusing all encoder blocks...” and further see [0096-0098]) and (Jain, e.g., [009-0011], “… use a machine learning model to predict when a subject will reach a certain level of capability to be able to perform a task or will achieve another result … models can be machine learning models, for example, a neural networks or classifiers. Other types of models that may be used include support vector machines, regression models, reinforcement learning models, clustering models, decision trees, random forest models, genetic algorithms, Bayesian models, and Gaussian mixture models…models can be trained to predict completion times using training data examples indicating the steps needed for similar tasks…”).
As per as claim 13, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the attributing comprises generating a model attribution confidence score (Lyman, e.g., [0071-0074], “... confidence score data 460, display parameter data 470, similar scan data 480, training set data 490, and/or other data relating to the medical scan...” and further see [0077-0081], “...determined by comparing some or all of confidence score data 460 to a threshold, can be determined by comparing a probability value to a threshold, and/or can be determined by comparing another continuous or discrete value indicating a calculated likelihood ...”) and (Jain, e.g., [0247-0248], [0345], “… machine learning training…provide high accuracy and confidence in the scoring output…”).
As per as claim 14, the combination of Jain, Lyman and Clement disclose:
The computer program product of claim 7, wherein the attributing is performed using a pair of prompt responses, the pair of prompt responses comprising the first prompt response and a second prompt response, the second prompt response generated by the foundation model in response to the prompt (Lyman, e.g., [0058-0059], “...interface feature evaluator system 110 can be operable to generate an ordered image-to-prompt mapping by selecting a set of user interface features to be displayed with each of an ordered set of medical scans. The set of medical scans and the ordered image-to-prompt mapping can be transmitted to a set of client devices. A set of responses can be generated by each client device in response to sequentially displaying each of the set of medical scans in conjunction with a mapped user interface feature indicated in the ordered image-to-prompt mapping via a user interface...” and [0111], [0268], [0283-0284], “...The interface can prompt the user to indicate the appropriate scan category 1120 and/or prompt the user to confirm and/or edit the inferred scan category...”) (the examiner asserts, multiple input/prompts to select the data in the category = pair of prompt responses) and further see [0054], [0095], “...mapping pair in medical label alias database..”) and (Jain, e.g., [0303], and [0311-0313).
Claims 15-20 are essentially the same as claims 1-6 except that they set forth the claimed invention as a system rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-6.
Response to Arguments
The Examiner respectfully reminds applicant of the broadest reasonable interpretation standard (See MPEP 2111), "During examination, the claims must be interpreted as broadly as their terms reasonably allow." In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) (The USPTO uses a different standard for construing claims than that used by district courts; during examination the USPTO must give claims their broadest reasonable interpretation.) In Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005), the court further elaborated on the “broadest reasonable interpretation" standard and recognized that “The Patent and Trademark Office (“PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction." Thus, when interpreting claims, the courts have held that Examiners should (1) interpret claim terms as broadly as their terms reasonably allows and (2) interpret claim phrases as broadly as their construction reasonably allows.
Applicant’s arguments filed 05/22/2026 with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record (See the new ground(s) of rejection set forth herein above).
The Examiner respectfully submits that, with respect to the totally newly amended subject matter, the Examiner respectfully cited proper paragraphs from cited reference to reject the claim in responsive to the newly amended, please refer to the corresponding section of the office action.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to machine learning model management which is generating, by a trained model, a training prompt response to a training prompt in a set of training prompts and using the trained attribution model and a first prompt response generated by a fine-tuned model in response to a prompt, the fine-tuned model to a foundation model.
a. Srinivasulu et al. (US PGPUB 2023/0124988, hereinafter Srinivasulu), “Data Quality Using Artificial Intelligence) discloses “improve data quality using artificial intelligence and includes a plurality of rows of data and a trained neural network that is configured to predict a data category for the incoming data can be received, where the neural network has been trained with training data including training features, and the training data includes labeled data categories”.
Srinivasulu also teaches “a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data” and “trained machine learning model as input, and the model can generate predictions to improve the quality of the incoming data” [0013-0014].
Srinivasulu further teaches “ a machine learning model by leveraging trends in groups of structured/labeled/categorized data to enhance the quality of an incoming group of data. In the absence of processing of the incoming/training data” [0021], predicting a class label for a new data entry” [0031].
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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