DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/04/2026 has been entered.
Status of Claims
In the amendment filed 05/04/2026 the following occurred: Claims 1-2 and 11-12 were amended. Claims 1-20 are presented for examination.
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 1-20 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 1-20 are drawn to a method and a system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claim 1 recites receiving input data comprising unstructured text representing one or more sequences of terms; for each respective sequence of terms: generating, using regular expression rules, a corresponding line of therapy (LoT) pseudo-label indicating whether the respective sequence of terms comprises LoT information, wherein the corresponding LoT pseudo-label comprises a binary classification that indicates a first ground-truth value when the respective sequence of terms comprises LoT information and indicates a second ground-truth value when the respective sequence of terms does not comprise LoT information; generating a corresponding LoT indicator predicting whether the respective sequence of terms comprises LoT information, wherein the corresponding LoT indicator comprises a binary classification that indicates a first value when the respective sequence of terms comprises LoT information and indicates a second value when the respective sequence of terms does not comprise LoT information; determining a corresponding LoT indication loss based on the corresponding LoT pseudo-label and the corresponding LoT indicator; and fine-tuning based on the LoT indication losses determined for the one or more sequences of terms; for each respective sequence of terms: generating, using the regular expression rules, a corresponding classification pseudo-label indicating a classification of the respective sequence of terms; generating a corresponding LoT classification predicting the classification of the respective sequence of terms; and aggregating a plurality of labels from a plurality of sources, the plurality of labels including the corresponding classification pseudo-label and the corresponding LoT classification; for each label of the plurality of labels, determining a confidence value associated with the label; selecting a corresponding aggregated label for the respective sequence of terms as the label from the plurality of labels having the highest confidence value based on a maximum vote received by the label having the highest confidence values; and fine-tuning based on the corresponding aggregated labels selected for the one or more sequences of terms to perform LoT classification.
Independent claim 11 recites receiving input data comprising unstructured text representing one or more sequences of terms; for each respective sequence of terms: generating, using regular expression rules, a corresponding line of therapy (LoT) pseudo-label indicating whether the respective sequence of terms comprises LoT information, wherein the corresponding LoT pseudo-label comprises a binary classification that indicates a first ground-truth value when the respective sequence of terms comprises LoT information and indicates a second ground-truth value when the respective sequence of terms does not comprise LoT information; generating a corresponding LoT indicator predicting whether the respective sequence of terms comprises LoT information, wherein the corresponding LoT indicator comprises a binary classification that indicates a first value when the respective sequence of terms comprises LoT information and indicates a second value when the respective sequence of terms does not comprise LoT information; and determining a corresponding LoT indication loss based on the corresponding LoT pseudo-label and the corresponding LoT indicator; fine-tuning based on the LoT indication losses determined for the one or more sequences of terms; for each respective sequence of terms: generating, using the regular expression rules, a corresponding classification pseudo-label indicating a classification of the respective sequence of terms; generating a corresponding LoT classification predicting the classification of the respective sequence of terms; and aggregating a plurality of labels from a plurality of sources, the plurality of labels including the corresponding classification pseudo-label and the corresponding LoT classification; for each label of the plurality of labels, determining a confidence value associated with the label; selecting a corresponding aggregated label for the respective sequence of terms as the label from the plurality of labels having the highest confidence value based on a maximum vote received by the label having the highest confidence value; and fine-tuning based on the corresponding aggregated labels selected for the one or more sequences of terms to perform LoT classification.
The respective dependent claims 2-10 and 12-20, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover mathematical concepts. In particular, the recited limitations concern determining a LoT indication loss from the generated pseudo-label and the corresponding indicator based on a binary classification, determining a confidence value associated with each label, aggregating labels, selecting the aggregated label having the highest confidence value based on maximum vote, and fine-turning based on the LoT indication losses, each which are recitations of mathematical concepts. The specification supports this by stating that the present invention is to a training process for “fine-tuning” a model such as “based on the LoT indication losses determined for the one or more sequences of terms” (see: specification paragraph 4 and 56). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, or mathematical formulas or equations, or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The recited limitations at issue cover mathematical concepts because the “fine-tuning stage [] fine-tunes the BioBert model [] (e.g., updates parameters of the BioBert model []) based on the LoT indication losses [] determined for the one or more sequences of terms…to detect whether sequences of terms [] include LoT information or not” (see: specification paragraph 37 and 39), wherein the parameters are “weight[ed]” (see: specification paragraph 36). Further, the plain meaning of an LoT indicator comprising “a binary classification” is a mathematical operation in view of the specification, paragraph 37, which teaches a binary value of 1 or 0 representing true or false; hence, the specification shows the binary values as a numerical operands, which are mathematical calculations under MPEP 2106.04(a)(2)(I)(C). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “computer-implemented…executed on data processing hardware causes the data processing hardware to perform operations comprising…using a pre-trained transformer model…the pre-trained transformer model determines…the pre-trained transformer model determines…the pre-trained transformer model…using the pre-trained transformer model…using a weak supervision labeling model…using the weak supervision labeling model…the pre-trained transformer model…” (claim 1), “the pre-trained transformer model…” (claim 2 and 12), “the pre-trained transformer model comprises a Bidirectional Encoder from Transformers for Biomedical Text Mining (BioBERT) model…” (claim 6 and 16), “the pre-trained BioBERT model comprises a stack of multi-headed self-attention layers” (claim 7 and 17), “the pre-trained transformer model…by the pre-trained transformer model” (claim 8 and 18), “storing the fine-tuned transformer model in memory hardware in communication with the data processing hardware” (claim 9 and 19), “transmitting, via a network, the fine-tuned transformer model to one or more computing devices” (claim 10 and 20), and “data processing hardware; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising…using a pre-trained transformer model…the pre-trained transformer model determines…the pre-trained transformer model determines…the pre-trained transformer model…using the pre-trained transformer model…using a weak supervision labeling model…using the weak supervision labeling model…the pre-trained transformer model…” (claim 11), which are additional elements that are recited at a high level of generality (e.g., the “data processing hardware” is configured to perform functions through no more than a statement than that said data processing hardware is “to perform operations”; the “pre-trained transformer model” is configured though no more than a statement than that functions are performed “using” said pre-trained transformer model, where the pre-trained transformer model may be a “pre-trained BioBERT model” configured though no more than a statement than that the said BioBERT model is pre-trained “on” a corpus of biomedical text data and “comprises” a stack of multi-headed self-attention layers; the “weak supervision labeling model” is configured though no more than a statement than that functions are performed “using” said weak supervision labeling model; the “memory hardware” is configured to store information though no more than a statement than that such function is performed by being “in communication with” the data processing hardware; the “network” is configured though no more than a statement than that transmitted is performed “via” said network “to” one or more computing devices) such that they amount to no more than mere instruction to apply the exception using generic computer elements. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer elements. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
Step 2B
The claims 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(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic elements. Mere instructions to apply an exception using generic elements cannot provide an inventive concept. See MPEP 2106.05(f).
Though not relied upon here for the purposes of rejection here, it is noted that generic concepts related to receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-10 and 12-20, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
Applicant’s arguments from the response filed on 05/04/2026 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because, “Regarding the "Mathematical Concepts" grouping, the Examiner previously argued that fine-tuning based on determined losses is a mathematical calculation. However, the 2025 Guidance Memo directly addresses this in its discussion of Example 39 versus Example 47. The Memo clarifies that a limitation like "training the neural network" involves techniques that rely on mathematical concepts, but it does not recite a mathematical concept unless it sets forth mathematical relationships, calculations, or formulas using words or symbols (such as explicitly claiming a backpropagation or gradient descent algorithm). Amended Claim 1 recites fine-tuning the pre-trained transformer model based on the corresponding aggregated labels. Amended Claim 1 does not set forth a specific mathematical equation, formula, or calculation using words or symbols. It merely involves the application of a training process. Therefore, under the 2025 Guidance Memo, the claim does not recite a mathematical concept. Regarding the "Certain Methods of Organizing Human Activity" grouping, the Examiner previously argued that the claims address the problem of managing clinical trial documents and doctor guidelines. However, these applications are described in the background of the specification (e.g., Paragraphs [0025]-[0026]) to provide context; they are not claimed. The fact that the trained model may ultimately be used in a clinical context does not transform the claimed training methodology into a method of organizing human activity. Amended Claim 1 does not recite any steps of managing patients, conducting clinical trials, or organizing any human interactions. Rather, amended Claim 1 recites a highly specific, computer-implemented process for training a machine learning model using a weak supervision labeling model. Therefore, amended Claim 1 does not recite a method of organizing human activity.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The rejection has been edited in view of the amendments and for clarity. It is explained in the MPEP that, when a claim recites multiple abstract ideas that fall in the same or different groupings, Examiners should consider the limitations together as a single abstract idea, rather than as a plurality of separate abstract ideas to be analyzed individually. See MPEP 2106.04, subsection II.B. Therefore, the rejection has been altered to reflect that the claims as a whole are directed to mathematical concepts as the present invention further addresses a training process for “fine-tuning” a model such as “based on the LoT indication losses determined for the one or more sequences of terms” (see: specification paragraph 4 and 56). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, or mathematical formulas or equations, or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Using Office provided Example 48, claim 2, as an example, the present claim is shown to reasonably include mathematical concepts. In Example 48, claim 2, Step (e) requires applying binary masks to the clusters. The plain meaning of “applying a binary mask” to a person of ordinary skill in the art is a mathematical operation of using a binary matrix to indicate which portions of a representation should be turned on or off. Such masking can be performed in any way known in the art, for example, by performing bitwise operations on two numbers or multiplying a binary matrix with another numerical representation, etc. Similarly, in present claim 1, for example, a training process is claimed for “fine-tuning” a model such as “based on the LoT indication losses determined for the one or more sequences of terms” (see: specification paragraph 4 and 56). The claim indicates that the “LoT indicator comprises a binary classification that indicates a first value when the pre-trained transformer model determines the respective sequence of terms comprises LoT information and indicates a second value when the pre-trained transformer model determines the respective sequence of terms does not comprise LoT information”. The plain meaning of an LoT indicator comprising “a binary classification that indicates a first value” under a particular condition and “a second value” under another particular condition to a person of ordinary skill in the art is a mathematical operation in view of specification paragraph 37, which teaches a binary value of 1 or 0 representing true or false. Specification paragraph 37 shows the binary value as a numerical operand: determining the LoT indication loss by comparing the indicator against the pseudo-label serving as ground truth, and the fine-turning stage updates the model parameters based on those losses. In other words, the binary value feeds a calculation. Hence, the loss determination and parameter update are mathematical calculations under MPEP 2106.04(a)(2)(I)(C). This is further confirmed by the specification, which describes the problems the claims address and the solutions the claims represent. Particularly, the invention addresses a problem where a “model requires large amounts of labeled training data…labeling large amounts of data is time consuming and expensive as it requires manual annotation by subject matter experts” (see: specification paragraph 40). The present claims address this problem with a “fine-tuning stage [] of the training process [which] utilizes weakly annotated data to train (e.g., semi-supervised training data) to train the BioBert model” (see: specification paragraph 40), where fine-tuning process is mathematical because the “fine-tuning stage [] fine-tunes the BioBert model [] (e.g., updates parameters of the BioBert model []) based on the LoT indication losses [] determined for the one or more sequences of terms…to detect whether sequences of terms [] include LoT information or not” (see: specification paragraph 37 and 39). In other words, the claim generates a LoT indicator to compare with a pseudo-label to get a loss in order to update model parameters thereby terminating in a trained model with no output applied to anything outside the training loop.
As per the argument against characterization in view of the context provided by the specification, eligibility isn’t decided on claim language in a vacuum. Alice asks what the claim is “directed to”, which requires understanding the claim’s character as a whole, which cannot be accomplished without knowing what the recited terms mean and what the invention actually accomplishes. The Federal Circuit has repeatedly said the specification informs this, such as in Enfish, which looked at the specification’s description of the self-referential table to determine the claims were directed to a database improvement rather than abstract data organization. The specification is the single best guide to claim meaning, and broadest reasonable interpretation must be consistent with the specification. The specification is not optional context; it’s the source of the technical-problem framing the claim is measured against.
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because “USPTO Example 47, Claim 3 is instructive. In Example 47, Claim 3 was found eligible because the additional elements (detecting source addresses, dropping malicious packets, blocking future traffic) "reflect[ed] the improvement described in the background" and "the claim as a whole integrates the judicial exception into a practical application because the claim improves the functioning of a computer or technical field." Similarly, amended Claim I recites a complete multistage weak supervision training pipeline that reflects the technological improvement described in the specification. Amended independent claim 1 has been significantly narrowed to recite a highly specific, multi-step technological solution to the technological problem of training deep learning models on unstructured text without requiring massive amounts of manually labeled data (a process that is computationally restrictive, time-consuming, and expensive; see Specification at Paragraph [0027]). Amended independent claim I no longer merely recites generating pseudo-labels and fine-tuning. It now requires an ordered combination of specific, technical steps, including: • "aggregating, using a weak supervision labeling model, a plurality of labels from a plurality of sources, the plurality of labels including the corresponding classification pseudo-label and the corresponding LoT classification;" • "for each label of the plurality of labels, determining a confidence value associated with the label;" • "selecting, using the weak supervision labeling model, a corresponding aggregated label for the respective sequence of terms as the label from the plurality of labels having the highest confidence value based on a maximum vote ... ;" and • "fine-tuning the pre-trained transformer model based on the corresponding aggregated labels ... " Claim 1 as amended now recites a multi-stage training pipeline that is not merely applying mathematics on a generic computer; rather, it is a specific technical training methodology that produces a tangibly improved machine learning model. These new limitations recite a particular way to achieve a desired outcome: utilizing a weak supervision labeling model to aggregate deterministic regex labels with non-deterministic transformer predictions, assigning confidence values, and applying a maximum voting mechanism to autonomously generate a highly accurate, aggregated training dataset. As detailed in the specification at Paragraphs [0043]-[0044] and [0052]-[0053], this specific architectural pipeline improves the functioning of the machine learning system itself, elevating the recall score to 0.97 and the F1 score to 0.88, outperforming traditional rule-based models. This is a clear improvement to computer technology and the technical field of natural language processing (NLP).”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
As per Example 47, claim 3, the claim is instructive and the argument supplies the most important aspect that distinguishes the example claim from the present claim, which is the argued “blocking future traffic” as part of the practical application, and without such a feature, the present claim is more like the example claim 2. The Example claim 3 was found eligible because the disclosed system detects network intrusions and takes real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses. The background section of the example further explains that the disclosed system enhances security by acting in real time to proactively prevent network intrusions. In contrast, the present claim generates a LoT indicator to compare with a pseudo-label to get a loss in order to update model parameters thereby terminating in a trained model with no output applied to anything outside the training loop.
As claimed, the functioning of the machine learning itself is not improved as argued. As argued, the invention “utilizing a weak supervision labeling model to aggregate deterministic regex labels with non-deterministic transformer predictions, assigning confidence values, and applying a maximum voting mechanism to autonomously generate a highly accurate, aggregated training dataset.” Here it is clear the “weak supervision labeling model” is configured such that it is “utilized” to preform the desired functions to achieve the desired output. The operation of the model itself is not the element improved upon, and instead “weak supervision labeling model” is claimed as configured though no more than a statement than that functions are performed “using” said weak supervision labeling model such that it amounts to no more than mere instruction to apply the exception using generic computer elements. It is argued that “it is a specific technical training methodology that produces a tangibly improved machine learning model”, but this is not persuasive as the model itself is not altered, but as argued “trained”, or fine-tuned, under a particular “methodology”; hence, the finding that the additional element of the model is applied to the abstract idea of the methodology at a high level.
In the remarks, Applicant argues in substance that (3) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because the “amended claims do not merely say "apply weak supervision on a generic computer." Instead, they recite a granular, specific architecture wherein a deterministic regular expression module and a non-deterministic transformer model independently generate labels, which are then passed to a specific secondary model (the weak supervision labeling model) to calculate confidence values and execute a maximum vote selection to generate a new ground truth for a final fine-tuning stage. This is a bespoke machine learning pipeline. The hardware and the pre-trained models are not merely being used as generic tools to perform an existing human process; they are integral components of a novel technical method that purports to improve computer learning capabilities. In contrast to Example 47, Claim 3, Example 47, Claim 2 was found ineligible because it merely used a trained ANN to "detect anomalies" and "analyze anomalies" without specifying how the detection and analysis were accomplished; "the trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions." Claim 1 as amended is distinguishable because it specifies precisely how the training is accomplished through a detailed, multi-stage pipeline: regex-generated pseudo-labels serve as ground truth for LoT detection finetuning, then classification pseudo-labels and transformer-generated LoT classifications are aggregated by a weak supervision labeling model using a maximum vote technique, and the transformer model is further fine-tuned based on these aggregated labels. This is not "apply it" language; rather, it is a specific technical methodology The Examiner argued that BioBERT is a known model and that fine-tuning it is standard practice. See Office Action at Page 11. However, the claims are not directed to BioBERT per se (BioBERT is recited only in dependent claim 6). The novelty lies in the specific weak supervision training pipeline, namely, the combination of regex-based pseudo-labeling, transformer-based prediction, loss-based fine-tuning, and now the weak supervision aggregation with maximum vote label selection for further fine-tuning. Even if individual components are known, the specific ordered combination of these training stages represents a particular technical approach that improves the functioning of the model, analogous to how Example 47, Claim 3's combination of known network security steps reflected an improvement in network security technology.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
It is argued that the claims are to “a bespoke machine learning pipeline”, but such a pipeline is abstract and is not representative of the operation of the machine learning itself. The claims are to fine-tuning a model and terminate in a trained model with no output applied to anything outside the training loop. The model(s) itself is trained and fine-tuned at a high level, but there is no claiming how the model(s) itself, before or after training and fine-tuning, operates; therefore, the claims place no limitation on how it operates to predicting whether the respective sequence of terms comprises LoT information. To be significantly more, an additional element must amount to more than mere instruction to apply the exception using generic computer elements, but the “pre-trained transformer model” is configured though no more than a statement than that functions are performed “using” said pre-trained transformer model, where the pre-trained transformer model may be a “pre-trained BioBERT model” configured though no more than a statement than that the said BioBERT model is pre-trained “on” a corpus of biomedical text data and “comprises” a stack of multi-headed self-attention layers. Here the claimed “using” of the pre-trained transformer model is equivalent to “apply it”, and further, the model itself is one that is well-understood, routine, and conventional in the industry – it may be the BioBERT model, which is a known pre-trained language representation model designed for the biomedical field, and it is common and standard practice to fine-tune a BioBERT model for specific biomedical natural language processing (NLP) tasks. As a domain-specific model, BioBERT is designed to be further trained on smaller, specialized datasets (e.g., named entity recognition, relation extraction). Hence, the use of a pre-trained transformer model, while being an additional element, does not amount more than mere instruction to apply the exception using generic computer elements such that they amount to no more than mere instruction to apply the exception using generic computer elements. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found on the attached PTO-892 form, including:
U.S Patent Pub 2025/0111910 to Vidmar (see: para 21 “using multimodal EHR data and weak supervision”; para 60 “When used for classification, SVMs separate a given set of binary labeled data”; para 169 “the final structured phenotype can be overfit to a particular dataset after several iterations of fine-tuning.”; para 214 “a line of therapy engine…An example of a line of therapy engine is disclosed”; para 256 “attempt to classify each sample as having the medical disorder or not through a binary voting variable”).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682
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