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 .
Status of Claims
Claims 1-9 and 21-31 are currently pending and have been examined.
Claims 1, 4, 21, 25, and 29 have been amended.
Claims 10-20 were canceled previously.
Claims 1-9 and 21-31 have been rejected.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 and 21-31 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
There is no support in the specification for “reformatting….the text data… to numeric data… according to a compatible data format for training a machine learning model,” nor is there support for “train, based on the merged consumer preference and drug description data, a machine learning model,” wherein the merged consumer preference and drug description data is based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data wherein the numeric data for both is determined by applying a natural language processing transformation to the text data “to a compatible format for training a machine learning model.” as recited in independent claims 1, 21, 25, and 29. The specification merely discloses, “one or more NLP transformations may be applied to… transform the text data to numeric data for input in the predictive (machine learning) model,” it does not disclose using the numeric data (which is disclosed as being used as input to the model) in any way to merge the consumer preference data and the drug description data and then to train the machine learning model based on the merged data. The specification at para 52 discloses, “the drug feature data may be combined, or merged, with the consumer preference data, wherein this data may be used to train a predictive model,” however, it does not disclose merging the drug feature data (the drug description data) with the consumer preference data, “based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data,” wherein the numeric data is generated by applying natural language processing transformations to the text data of each.
The dependent claims are rejected as dependent on a rejected base claim.
Therefore, Claims 1-9 and 21-31 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 and 10-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 21, 25, and 29 disclose, “generating, based on an application of the trained machine learning model to active ingredient data associated with the prescription drug and the numeric data associated with each drug of the plurality of drugs, a drug design.” It is unclear whether the “numeric data associated with each drug of the plurality of drugs,” refers to the “numeric data associated with the plurality of drugs in the drug description data,” alone or if it refers to both the numeric data associated with the plurality of drugs in the drug description data and the, “numeric data associated with the prescription drug in the consumer preference data.” Appropriate correction is required.
The dependent claims are rejected as dependent on a rejected base claim.
Thus, Claims 1-9 and 10-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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 1-9 and 21-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 1-9 and 21-31 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 discloses a method comprising: receiving, by a computing device, consumer preference data, comprising text data indicative of one or more drug attributes for a prescription drug and drug description data comprising text data indicative of one or more drug attributes for a plurality of drugs; reformatting, based on an application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug, the text data of the consumer preference data to numeric data associated with the prescription drug in the consumer preference data according to a compatible data format for training a machine learning model; reformatting, based on an application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs, the text data of the drug description data to numeric data associated with the plurality of drugs in the drug description data according to a compatible data format for training a machine learning model; merging, based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data, the consumer preference data with the drug description data; training, based on the merged consumer preference and drug description data, a machine learning model to output a drug design based on the consumer preference data; and generating, based on an application of the trained machine learning model to active ingredient data associated with the prescription drug and the numeric data associated with each drug of the plurality of drugs, a drug design comprising one or more active ingredients of the prescription drug and one or more features associated with the one or more drug attributes for the plurality of drugs based on the consumer preference data.
Independent Claim 21 discloses an apparatus comprising: one or more processors; and a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to: [perform the method of claim 1].
Independent Claim 25 discloses one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: [perform the method of claim 1]
Independent Claim 29 discloses a system comprising: a first source configured to send consumer preference data associated with a prescription drug, wherein the consumer preference data comprises one or more drug attributes for the prescription drug associated with a National Drug Code (NDC); a second source configured to send drug description data comprising one or more drug attributes for a plurality of drugs and an NDC for each drug of the plurality of drugs, and a computing device configure to: [perform the method of claim 1].
The examiner is interpreting the above bolded limitations as additional elements as
further discussed below. The remaining un-bolded above limitations are merely directed to rules a user would follow to generate a drug design based on consumer preference data and drug description data of a plurality of drugs. The series of steps recited above describe managing personal behavior or relationships or interactions between people and thus are grouped as certain methods of organizing human activity which is an abstract idea. The limitations are considered together as a single abstract idea for further analysis. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claims 1 and 29 disclose the following additional elements:
A computing device
An application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug… to a compatible data format for training a machine learning model for training a machine learning model
An application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs... to a compatible data format for training a machine learning model for training a machine learning model
Training, based on the merged consumer preference and drug description data, a machine learning model
Independent Claim 21 discloses the following additional elements:
An apparatus comprising one or more processors and a memory storing processor-executable instructions
A computing device
An application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug… to a compatible data format for training a machine learning model for training a machine learning model
An application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs... to a compatible data format for training a machine learning model for training a machine learning model
Training, based on the merged consumer preference and drug description data, a machine learning model
Independent Claim 25 discloses the following additional elements:
One or more non-transitory computer-readable media storing processor-executable instructions
A computing device
An application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug… to a compatible data format for training a machine learning model
An application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs... to a compatible data format for training a machine learning model for training a machine learning model
Training, based on the merged consumer preference and drug description data, a machine learning model
In particular, the computing device, the application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug… to a compatible data format for training a machine learning model for training a machine learning model, the application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs... to a compatible data format for training a machine learning model for training a machine learning model, training, based on the merged consumer preference and drug description data, a machine learning model (of claims 1, 21, 25, and 29), the apparatus comprising one or more processors and a memory storing processor-executable instructions (of claim 21), and the one or more non-transitory computer-readable media storing processor-executable instructions (of claim 25) are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Applicant’s specification generally states - As will be appreciated by one skilled in the art, hardware, software, or a combination of software and hardware may be implemented. Furthermore, a computer program product on a computer-readable storage medium (e.g., non-transitory) having processor-executable instructions (e.g., computer software) embodied in the storage medium. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, memresistors, Non- Volatile Random Access Memory (NVRAM), flash memory, or a combination thereof. These processor-executable instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the processor-executable instructions which execute on the computer or other programmable data processing apparatus create a device for implementing the functions specified in the flowchart block or blocks (Applicant’s specification para 16-17).
Thus, a general computer utilizing any suitable computer-readable storage medium is performing as expected. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the reformatting steps are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception because there is no connection to a practical application for reformatting the consumer preference data and the drug description data. The reformatting is done solely to train the machine learning model. Merely training a model without pointing to a solution to a problem presented by said model or simply applying a model does not provide a practical application.
Further, in regards to example 42 and the reasons for eligibility of the claims discussed in example 42, the abstract idea was integrated into a practical application by allowing sharing of information in real time regardless of format when input by user, it wasn’t integrated into a practical application based on the specific act of converting information from non-standardized to standardized format. The amended claims disclose reformatting data for training purposes and thus would not be comparable to example 42 as there is no comparable practical application.
Accordingly, claim(s) 1, 21, 25, and 29 are directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does 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 into a practical application, the additional elements of the computing device, the application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug… to a compatible data format for training a machine learning model for training a machine learning model, the application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs... to a compatible data format for training a machine learning model for training a machine learning model, training, based on the merged consumer preference and drug description data, a machine learning model (of claims 1, 21, 25, and 29), the apparatus comprising one or more processors and a memory storing processor-executable instructions (of claim 21), and the one or more non-transitory computer-readable media storing processor-executable instructions (of claim 25) amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claims 1, 21, 25, and 29 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 2-9, 22-24, 26-28, and 30-31 further define the abstract idea that is presented in the respective Independent 1, 21, 25, and 29 are abstract for the same reasons and basis as presented above. Specifically:
Dependent claims 2-4, 5-6, 8-9 further narrow the abstract idea presented in independent claim 1 and/or claims it depends on with no recitation of any further additional elements.
Dependent claim 7 further narrows the abstract idea presented in independent claim 1 and/or claims it depends on while further disclosing the additional element(s) a neural network (claim 7).
Dependent claims 22-24 further narrow the abstract idea presented in independent claim 21 and/or claims it depends on with no recitation of any further additional elements.
Dependent claims 26-28 further narrow the abstract idea presented in independent claim 25 and/or claims it depends on with no recitation of any further additional elements.
Dependent claims 30-31 further narrow the abstract idea presented in independent claim 29 and/or claims it depends on with no recitation of any further additional elements.
The neural network (claim 7) merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(1) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does 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 into a practical application, the additional element of the neural network (claim 7) were considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the ‘significantly more’ analysis and has been found insufficient to provide significantly more. MPEP2106.05 (A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide an inventive concept (‘significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more.
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-9 and 21-31 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free of Prior Art
Claims 1-9 and 21-31 contain subject matter free of prior art and art thus eligible under 35 U.S.C. 102 and 103. Below are the move relevant prior art:
Cooper (US PG Pub 2014/0156295 A1) Para 46-47 discloses once the bar code, RFID, QR code or product brand and name information [identifier associated with prescription drug] is received by the data receiving module 106, the control module 112 uses the Product table 136 in the substance information database 116 to retrieve the corresponding list of product ingredients 126 [drug attributes of prescription drug]… the control module 112 uses the Interaction table 138 to determine whether information in the user profile 120 matches any one or more of the incompatible ingredients 128, incompatible allergies, intolerances or health conditions 130 or an incompatible pregnancy or breastfeeding state 150 corresponding to the ingredient 126 linked to the array of the medicine or health supplement 104 selected by the user. In addition, the user selected medicine or health supplement 104 is filtered via a number of other patient specific criteria, saved in the user profile 120. For example, user's age, schedule requirements (prescription only), cost, location (source) and dosage form (e.g. patient can only swallow capsules) [consumer preference of drug attribute = drug shape].)
Snopek (US 11,593,710 B1); While Column 1, lines 50-67 discloses the system may be trained using various machine learning techniques… and by obtaining previously filled prescriptions and identifying a set of characteristics… such as a medical diagnosis associated with the prescription, the prescription dosage… the drug type, the drug class and/or subclass, the drug name [identifier], the dosage form [merged data set of identifier and drug attributes], Snopek does not fully disclose, “merging, based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data, the consumer preference data with the drug description data; and training, based on the merged consumer preference and drug description data, a machine learning model to output a drug design based on the consumer preference data.”
Kraft (US PG Pub 2012/0041778 A1); While Para 18 discloses, “the present disclosure provides a system for producing a drug product for an individual patient that includes a computer processor that is configured to receive information relating to the patient and to predict, based on the received patient information, an optimal drug selection, combination and dosage for the patient,” and Para 85 discloses, “the prescribing clinician and patient may select a custom size and shape of and color or pattern markings of a specific patient specific pill (ie, shape, size, colors, pattern) For example, pediatric patient might choose a “Mickey Mouse” size, shape, coloring, or other markings to personalize and also differentiate from others in similar locality,” Kraft does not train the machine learning on the merged consumer preference and drug description data, wherein the merged data is based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data, wherein the numeric data of each grouping is generated by applying a natural language processing transformation to the text data.
DeCiccio (US PG Pub 2016/0342769 A1) Para 17 discloses custom flavoring can further increase adherence and improve medical outcomes. Para 65-68 discloses the dispenser 132 would be a 3D printer with interchangeable pods containing the gummy gel components, flavoring components and active pharmaceutical ingredient (API)… 3D printing of gummy formulations for pediatric drugs will offer rapid production of custom doses and drug release profiles in a palatable format that should increase medication adherence in a hard-to-treat population… Para 70 discloses various pods can provide different flavors and sweetness so that dose flavor can be based on feedback from each parent/child… Automating and reducing time for individualized preparations will increase accessibility and reduce costs for custom dosing. After fabrication, the delivery system 120 evaluates the mass of each gummy to ensure proper amount of gel is extruded.
Vamvourellis (US Patent 12,517984 B1); While Column 6, lines 26-34 discloses, “TF-IDF model 206A is a natural language processing model that may determine importance of each word in the received text data using a statistical analysis. TF-IDF model 206A may receive text data, e.g., pre-processed text from a document or an investment strategy description from pre-processing layer 204. To determine the importance of each word, TF-IDF model 206A may determine a TF-IDF score for each word in the text and then construct document vectors using the TF-IDF scores,” it does not disclose utilizing the TF-IDF model on the data to be able to merge data from different sources for training a machine learning model. Further, it does not specifically disclose applying a natural language processing model to text data indicative of one or more drug attributes for a prescription drug and text data indicative of one or more drug attributes for a plurality of drugs.
Bezdek (US PG Pub 2021/0074401 A1) Para 108 discloses the user interface 210 may display various options for indicating the package of the drug, and the user can select options corresponding to the package of the drug that the user wants. Such options can include, but is not limited to: generic vs. brand [substitution or no substitution], form, dosage, quantity, etc.
Bhatti (CA 2,944,105) Para 103-109 and Fig. 11 discloses today's date is June 2nd ; the date of when the product will run out is June 6th ; S=l ; and the buffer day value= 2 . Therefore, the latest date to place the order for the given product= June 6th - 2 - 1 = June 3rd [when to restock]. For example, a user may wish to restock every 30 days (e .g. A=30), or every two months (e .g . A=60).The number of units of the given product that should be ordered [quantity to restock] is computed by: C(A+x)- C(x). In other words, if A=30 and X=4, the server computes the predicted cumulative number of units of product that will be used 34 days from today, and subtracts from this number the cumulative number of units of product that will be used 4 days from today. A buffer quantity may be added to the above computation to account for unpredicted extra usage of product.
Shaya (US PG Pub 2014/0136362 A1) Para 38 discloses the data processing portion of the invention may include a neural network. The neural network is used to model the relationship(s) (typically non-linear) between the input variables of a served consumer's descriptive variables and the performance and/or preference responses of other consumers to products they have used in combination with the descriptive characterization of those consumers, and output variables of individual product performance and/or preference predictions. Para 171 discloses product recommendations ( e.g., predicted performance, predicted preference, and the like) and/or ancillary information for specific products and services obtained from a professional ( e.g., prescription drugs and the like) are reported to the professionals but not directly to the consumers.
Reasons for why the subject matter is free of the prior art under 35 U.S.C 102 and 103:
There is no obvious combination of prior art references that reads on the entire combination of the limitations of the independent claims, in particular, “receiving, by a computing device, consumer preference data comprising text data indicative of one or more drug attributes for a prescription drug and drug description data comprising text data indicative of one or more drug attributes for a plurality of drugs; reformatting, based on an application of one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the prescription drug, the text data of the consumer preference data to numeric data associated with the prescription drug in the consumer preference data according to a compatible data format for training a machine learning model: reformatting, based on an application of the one or more natural language processing transformations to the text data indicative of the one or more drug attributes for the plurality of drugs, the text data of the drug description data to numeric data associated with the plurality of drugs in the drug description data according to a compatible data format for training a machine learning model; merging, based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data, the consumer preference data with the drug description data; training, based on the merged consumer preference and drug description data, a machine learning model to output a drug design based on the consumer preference data; and generating, based on an application of the trained machine learning model to active ingredient data associated with the prescription drug and the numeric data associated with each drug of the plurality of drugs, a drug design comprising one or more active ingredients of the prescription drug and one or more features associated with the one or more drug attributes for the plurality of drugs based on the consumer preference data.” Further, while the above references disclose a couple of the limitations of the independent claims, there is no prior art that clearly trains the machine learning on the merged consumer preference and drug description data, wherein the merged data is based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data, wherein the numeric data of each grouping is generated by applying a natural language processing transformation to the text data. As such, claims 1-9 and 21-30 contain subject matter free of prior art.
Response to Arguments
Applicant’s arguments filed 2/12/2026 with respect to 35 U.S.C. § 112(a) have been fully considered, and are persuasive. Applicant argues that claims 1, 21, 25, and 29 have been amended accordingly in light of the previous 112(a) rejection. The Applicant has removed the previously rejected language, “causing a drug to be produced according to a drug design.” Thus, the 112(a) rejection has been withdrawn in light of the new amendments.
Applicant’s arguments filed 07/28/2025 with respect to 35 U.S.C. § 101 have been fully considered.
The Applicant argues that the claims are not directed to certain methods of organizing human activity. The Examiner disagrees, and this argument is not persuasive. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the claims of the instant application are directed to a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to generate a drug design based on consumer preference data and drug description data of a plurality of drugs. Receiving the text data, reformatting the text data to numeric data according to a compatible data format and generating a drug design were found to be a part of the abstract idea. The machine learning model and natural language processing transformations were found to be recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Further, the Applicant states that machine learning model training is a technical process executed by computing hardware. The Examiner does not disagree that it is a technical process, however, it is recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to generate a drug design based on consumer preference data and drug description data of a plurality of drugs, the claimed invention is directed to an abstract idea. None of the additional elements were found to integrate the abstract idea into a practical application or significantly more as presented above.
The Applicant further argues that the claims integrate the judicial exception into a practical application. The Examiner respectfully disagrees. The Applicant argues that the claims provide an improvement to technology and/or a technical field, however, the Applicant does not give any explanation. The Applicant later cites the Rehearing Decision of Ex parte Desjardins et al, and then states, “This improvement is realized in the claims…” without clarifying what they believe the improvement is. It isn’t until the last paragraph that the Applicant argues that the claims recite “specific technical improvements to data processing and machine learning model training… the use of NLP-based transformation and merged data enables machine learning training that otherwise could not be performed using raw textual data.” This is not clearly defined in the specification, there is no information on why utilizing NLP on text data results in an improvement to the training of the machine learning model. Further, the Applicant did not invent natural language processing and is merely reciting it at an ‘apply it’ level to transform the text data to numeric data. Further, as presented above, there is no support in the specification for “train, based on the merged consumer preference and drug description data, a machine learning model,” wherein the merged consumer preference and drug description data is based on the numeric data associated with the prescription drug in the consumer preference data and the numeric data associated with the plurality of drugs in the drug description data wherein the numeric data for both is determined by applying a natural language processing transformation to the text data. The specification merely discloses, “one or more NLP transformations may be applied to… transform the text data to numeric data for input in the predictive (machine learning) model,” it does not disclose then using that numeric data in any way to merge data to train the machine learning model. As such, the application is merely applying NLP to text data to generate merged data to train the machine learning model as expected, there is no description on any alleged improvement to the training of the machine learning model nor any description on how machine learning models would not be able to perform the training using raw textual data. The reformatting steps are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception in regards to the natural language processing because there is no connection to a practical application for reformatting the consumer preference data and the drug description data. The reformatting is done solely to train the machine learning model. Merely training a model without pointing to a solution to a problem presented by said model or simply applying a model does not provide a practical application. As such, the Examiner respectfully disagrees that there is any improvement to the machine learning model or any other technology.
The Applicant argues that the additional elements identified above do not add the words ‘apply it’ (or an equivalent), but does not give any explanation as to why the Applicant believes they do not. The Applicant merely states that the claims do not merely recite applying an abstract idea on a generic computer. Applicant’s specification para 16-17 disclose, “Furthermore, a computer program product on a computer-readable storage medium (e.g., non-transitory) having processor-executable instructions (e.g., computer software) embodied in the storage medium. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, memresistors, Non- Volatile Random Access Memory (NVRAM), flash memory, or a combination thereof. These processor-executable instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the processor-executable instructions which execute on the computer or other programmable data processing apparatus create a device for implementing the functions specified in the flowchart block or blocks.”
Finally, the Applicant argues that the claims recite additional elements that amount to significantly more than any alleged judicial exception. The Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
As discussed above, there is no improvement to a computer or other technology / technical field. The technological environment to which the claims are confined (a general-purpose computer (see Spec, Paras 16-17) and the natural language processing and machine learning model are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(f)). None of the additional elements of the claim were found to represent extra-solution activity and thus no-well-understood, routine, conventional analysis is required. As such, this argument is not persuasive.
Applicant’s arguments filed 2/12/2026 with respect to 35 U.S.C. § 103 have been fully considered and are persuasive in light of the newly added amendments and the combination of the newly added amendments with the previously presented limitations. As such, a discussion of why the claims recite subject matter free of the prior art under 35 U.S.C. 102 and 103 is presented above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA J MORICE DE VARGAS whose telephone number is (703)756-4608. The examiner can normally be reached M-F 9:30-5:30 pm CST.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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