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 .
Formal Matters
Applicant's response, filed 22 June 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 7, 10, 15-21, and 23-40 are currently pending and have been examined.
Claims 1, 7, 10, 15-21, 23-26, and 28--36 have been amended.
Claims 2-6, 11-14, and 21-22 have been canceled.
Claims 37-40 have been added.
Claims 1, 7, 10, 15-21 and, 23-40 have been rejected.
Priority
The instant application does not claim the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c) to any prior applications. Accordingly, the effective filing date for the instant application is 05/25/2022.
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, 7, 10, 15-21 and, 23-40 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 – Statutory Categories of Invention:
Claims 1, 7, 10, 15-21 and, 23-40 are drawn to a system, method, or device, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system in part performing the steps of receiving an indication of a particular condition and of a clinical medical record (EMR); utilizing a data-model module to extract immune-response trigger formulation instances and condition instances, from a set of [electronic] structured databases, based on leveraging by the data-model module a web-based content extractor, a parser, a natural language processing module, and a field value extractor configured to extract field data from at least one field selected from a group of fields containing an allergy field in the clinical EMR, a medication field in the clinical EMR, and a status field in the clinical EMR; training, via the data-model module, a multiclass decision tree model (MDTM) based on the immune-response trigger formulation instances and the condition instances; matching, via the MDTM, the particular condition to a set of immune-response trigger formulations that are associated with a plurality of contraindications and that are specific to (i) a set of vaccine candidates and (ii) the particular condition; for a particular immune-response trigger formulation in the set of immune-response trigger formulations: identifying, utilizing the MDTM, a first contraindication of the plurality of contraindications, wherein identifying the first contraindication comprises: (a) inputting data associated with one or both of the set of immune-response trigger formulations and the particular condition to the MDTM; and (b) reading, in response to the inputting, information output from the MDTM that is associated with the plurality of contraindications; instructing the MDTM to pause conflict-processing based on detecting that a first EMR field in the group of fields lacks a value; instructing the MDTM to resume the conflict-processing based on detecting that the first EMR field has been populated with a newly-received value; resuming the conflict-processing by: comparing the newly-received value in the first EMR field with the first contraindication to detect via the data-model module whether a conflict exists, wherein the conflict is associated with a particular vaccine of the set of vaccine candidates and with the particular immune-response trigger formulation, wherein the comparing comprises applying, by the data-model module, Named Entity Recognition (NER) to identify entities in content extracted from a set of data sources and String Matching (SM) to identify corresponding entities in the clinical EMR, and wherein the data-model module recognizes a match between a first entity identified via NER and a second entity identified via SM as conflict regardless of formatting differences between the first entity and the second entity; in response to detecting the conflict via the data-model module, (a) determining to omit the particular vaccine from the set of vaccine candidates to produce a sub-set of the vaccine candidates and (b) ceasing and disregarding, by the data-model module, further conflict-processing for the particular immune-response trigger formulation; and writing the sub-set of the vaccine candidates to a data structure associated with the database; and refraining from writing the particular vaccine to the data structure.
Independent claims 10 and 18 recite the same abstract idea.
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note October 2019 Update: Subject Matter Eligibility on p. 5 and MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Dependent claim 7 recites, in part, wherein the first EMR field corresponds to a patient status field, and identifying, via the MDTM, (a) one or more medical treatments and (b) one or more patient statuses that are associated with a negative outcome from administration of a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 15 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the MDTM, wherein identifying the plurality of contraindications is based on a plurality of identified ingredients associated with a vaccine formulation of one of the set of vaccine candidates, and wherein detecting the conflict is based on a value in the allergy field in the clinical EMR and an ingredient of the vaccine formulation, the ingredient corresponding to one or more of the plurality of contraindications.
Dependent claim 16 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the MDTM, wherein for each immune-response trigger formulation in the set of immune-response trigger formulations, identifying the plurality of contraindications comprises: determining, by the MDTM, one or more medical treatments that are associated with a negative outcome from administration of a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 17 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the MDTM, wherein for each immune- response trigger formulation in the set of immune-response trigger formulations, identifying the plurality of contraindications comprises: determining, by the MDTM, one or more patient statuses that are associated with a negative outcome based on administration of a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 19 recites, in part, wherein the operations further comprise identifying, via the MDTM, a plurality of ingredients associated with a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 20 recites, in part, wherein detecting the conflict comprises comparing, via the data-model: a value in the allergy field in the clinical EMR and one or more ingredients of the vaccine formulation, the one or more ingredients corresponding to one or more of the plurality of contraindications; a value in the medication field in the clinical EMR and one or more of the plurality of contraindications; and a value of the status field in the clinical EMR and one or more of the plurality of contraindications.
Dependent claim 21 recites, in part, wherein the training comprises automatically performing: extracting the content from a plurality of websites to train the MDTM, and wherein the MDTM classifies multiple vaccine formulation inputs across multiple output classes, corresponding to multiple contraindication categories, simultaneously.
Dependent claim 23 recites, in part, after writing the sub-set of the vaccine candidates, re-applying the MDTM based on additional content received.
Dependent claim 24 recites, in part, wherein the operations further comprise: automatically accessing the MDTM; and automatically analyzing, in response to the accessing, a first set of the EMR fields in the clinical EMR in relation to a second set of contraindications of the plurality of contraindications.
Dependent claim 25 recites, in part, wherein the operations further comprise, during a period associated with the analyzing: automatically initiating an evaluation of a vaccine formulation, associated with at least one of the set of vaccine candidates; and automatically identifying in response to the evaluation a particular conflict in relation to (i) the vaccine formulation and (ii) a subset of the set of immune-response trigger formulations.
Dependent claim 26 recites, in part, wherein the operations further comprise: in response to identifying the particular conflict, automatically initiating during the analyzing an operation corresponding to abstaining from continuing the evaluation of the vaccine formulation during the analyzing.
Dependent claim 27 recites, in part, wherein the operations further comprise: redirecting and reallocating resources to evaluating other remaining vaccine formulations, associated with at least one of the set of vaccine candidates, for which a conflict has not been identified at a time of the analyzing.
Dependent claim 28 recites, in part, wherein the abstaining from the continuing of the evaluation of the vaccine formulation during the analyzing increases an allocation and utilization of resources during the analyzing relative to an allocation and utilization of resources during a performance of the analyzing without the abstaining.
Dependent claim 30 recites, in part, writing a list identifying the sub-set of vaccine candidates to the data structure; and initiating display of information indicating that: after writing the list to the data structure, a particular first vaccine in the set of vaccine candidates identified in the list has been administered to a particular patient identified in the clinical EMR to treat the particular condition.
Dependent claim 31 recites, in part, wherein the operations further comprise: receiving documentation of the administration of the particular first vaccine to the particular patient.
Dependent 32 recites, in part, wherein the operations further comprise: re-training the MDTM, the re-training performed by the data-model module based on additional content accessed after the writing of the sub-set of the vaccine candidates to the data structure; re-applying the MDTM, based on the re-training; and identifying, via the data-model module and in response to the re-applying, one or both of an additional immune-response trigger formulation and an additional contraindication.
Dependent 33 recites, in part, wherein the operations further comprise: after writing the sub-set of the vaccine candidates to the data structure, receiving a signal containing one or both of (a) documentation of an administration of the a particular first vaccine, of the subset, to a particular patient and (b) an indication of the administration of the particular first vaccine to the particular patient in the clinical EMR; re-applying the MDTM, based on receiving the signal; and identifying, via the data-model module and based on the re-applying, one or both of an additional immune-response trigger formulation and an additional contraindication.
Dependent 34 recites, in part, wherein the operations further comprise: retrieving, after writing the sub-set of the vaccine candidates, additional content from a plurality of websites using Uniform Resource Locators (URLs); and re-applying the MDTM, based on the retrieving, to: identify an additional immune-response trigger formulation, and identify an additional contraindication.
Dependent 35 recites, in part, wherein the operations further comprise re-applying the MDTM after the refraining, the re-applying based on one or both of (a) additional content received following the writing of the sub-set of the vaccine candidates and (b) at least one additional immune-response trigger formulation.
Dependent 36 recites, in part, wherein the operations further comprise: calling a plurality of websites using Uniform Resource Locators (URLs), the calling performed after the identifying; accessing and ingesting additional content from the plurality of websites based on the calling; and re-training the MDTM (a) by the data-model module (b) based on the additional content.
Dependent 37 recites, in part, (a) presents an identifier for each vaccine candidate in the sub-set of vaccine candidates for which no conflict was identified and (b) omits presentation of an identifier for the particular vaccine.
Dependent 38 recites, in part, upon the MDTM resuming the conflict-processing following receipt of the input of the newly- received value into the first EMR field, automatically providing, via the data-model module and without human intervention, the newly-received value to the MDTM; and initiating, by the MDTM based on the newly-received value, conflict identification and conflict prediction for each immune-response trigger formulation in the set of immune-response trigger formulations against the clinical EMR with the newly-received value, wherein performing the conflict prediction comprises determining, via a trained probabilistic inference of the MDTM, whether the newly-received value is predicted to conflict with one or more of the plurality of contraindications for each respective immune-response trigger formulation.
Dependent 39 recites, in part, automatically providing, via the data-model module and without human intervention, the newly- received value to the MDTM; and performing, by the MDTM utilizing a trained probabilistic inference and based on the automatically providing, conflict prediction for each immune-response trigger formulation in the set of immune-response trigger formulations against the clinical EMR with the newly-received value, wherein performing the conflict prediction comprises of the MDTM, and wherein the conflict prediction and conflict identification are performed for each immune- response trigger formulation in the set of immune-response trigger formulations and not merely (a) for the particular immune-response trigger formulation and (b) against the newly-received value alone.
Dependent 40 recites, in part, wherein: the sub-set (a) includes a particular first vaccine and (b) is displayed along with an identification of a particular patient associated with the sub-set, after displaying the sub-set including the particular first vaccine on the GUI, the particular first vaccine is administered to the particular patient, and after the particular first vaccine is administered to the particular patient, the OMHPs receive electronic documentation of the administration of the particular first vaccine to the particular patient.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1, 10, or 18 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites one or more hardware processors (OMHPs). Claim 10 recites a computer and one or more hardware processors (OMHPs). Claim 18 recites a one more non-transitory computer-readable media having computer-executable instructions embodied thereon that, when executed via one or more hardware processors (OMHPs), perform a method. Claims 1, 10, and 18 recite performing certain operations by the one or more hardware processors (OMHPs). The instant specification does not have any specific hardware requirements for the computer, processors, memory, software application, and graphical user interface – only reciting generic exemplary embodiments (Detailed Description in ¶ 0017-19). The use of a computer, one or more hardware processors, one more non-transitory computer-readable media having computer-executable instructions, and an application with a graphical user interface, in this case to perform the steps of the abstract idea, only recites the hardware as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 30 recites a graphical user interface (GUI). Claim 37 recites a creating, at a graphical user interface (GUI) generator of the system, and causing display on an end-user computing device of, a GUI; wherein the GUI generator creates the GUI in real-time or near real-time based on the sub-set of the vaccine candidates written to the data structure. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claims 1, 10, and 19 recite an electronic medical record (EMR) associated with an electronic database, an electronic health record system, and the one or more hardware processors. The use of an electronic medical record (EMR) only recites the EMR as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 10, and 18 recite a hardware based multiclass decision tree model. The specification does not have any specific structure for the machine learning data model only providing that “[t]he data model 110 may be a flat-type data model, a hierarchal-type data model, a network-type data model, an entity-relationship-type data model, a dimensional data model, a relational data model, for example. In one example, the data model 110 is a multiclass decision tree model.” (see the instant disclosure in ¶ 0020). The use of a decision tree, in this case to determine associations between patient EHR data and immunization recommendations, only recites the machine-learning electronic data model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 10, 18, 23, 30, and 32-35 recite electronically writing to an electronic database via one or more hardware processors. Claim 31 recites a storing an indication of the administration of the particular patient in the clinical EMR. The limitations are only recited as a tool which only serves as output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claim 29 recites a utilizing of the MDTM is performed automatically and comprises locally executing a first portion of the data-model module at the electronic health record system concurrently or in parallel with remotely executing a second portion of the data-model module at a server. The use of two different servers to perform different operations amounts to applying data to an algorithm and reporting the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Finally, Examiner notes claims 1, 10, and 18 recite a web-based content extractor, a parser, a natural language processing module, and a field value extractor. These modules include software algorithm only embodiments (see the instant specification in ¶ 0061) and therefore are treated as part of the abstract idea under step 2A prong 1 and not as additional elements. Furthermore, if treated as additional elements, the specification does not provide any specific structure for the algorithms, only reciting their intended use by the abstract idea. The use of a web-based content extractor, a parser, a natural language processing module, and a field value extractor would therefore amount to applying data to an algorithm and reporting the results if considered additional elements (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Examiner notes that the recitation in the independent claims of “so as to conserve processor and memory resources of the system and to reduce latency in generating the sub-set of the vaccine candidates” while not reciting an abstract idea, does not amount to an additional element but instead merely non-functional description of an intended outcome of performing the abstract idea on a general purpose computer.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites one or more hardware processors (OMHPs). Claim 10 recites a computer and one or more hardware processors (OMHPs). Claim 18 recites a one more non-transitory computer-readable media having computer-executable instructions embodied thereon that, when executed via one or more hardware processors (OMHPs), perform a method. Claims 1, 10, and 18 recite performing certain operations by the one or more hardware processors (OMHPs). Claims 1, 10, and 19 recite an electronic medical record (EMR) associated with an electronic database, an electronic health record system, and the one or more hardware processors. Claims 1, 10, and 18 recite a hardware based multiclass decision tree model. Claim 29 recites a utilizing of the data-model module is performed automatically and comprises locally executing a first portion of the data-model module at the electronic health record system concurrently or in parallel with remotely executing a second portion of the data-model module at a server.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Examiner notes claims 1, 10, and 18 recite a web-based content extractor, a parser, a natural language processing module, and a field value extractor would also be considered mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”) if treated as additional elements.
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 30 recites a graphical user interface (GUI). Claim 37 recites a creating, at a graphical user interface (GUI) generator of the system, and causing display on an end-user computing device of, a GUI; wherein the GUI generator creates the GUI in real-time or near real-time based on the sub-set of the vaccine candidates written to the data structure. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Claims 1, 10, 18, 23, 30, and 32-35 recite electronically writing to an electronic database via one or more hardware processors. Claim 31 recites a storing an indication of the administration of the particular patient in the clinical EMR. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 7, 10, 15-21 and, 23-40 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant first outlined informal understandings that the treatment may constitute a practical application or treatment-like methods as other meaningful limitations. Examiner notes no such understanding was reached and the statements under II. Examiner Interview are an inaccurate representation of the conversation held 15 June 2026 (see Examiner’s Interview Summary mailed 18 June 2026 wherein Examiner notes that the amendments do not overcome the subject matter eligibility rejection).
Applicant's arguments filed with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant’s arguments are addressed in the order in which the claims are to analyzed under MPEP § 2016(III) Summary of Analysis and Flowchart and not necessarily in the order presented by Applicant.
Step 2A – Judicial Exception Analysis, Prong 1:
Applicant first asserts that the test for determining if a claim recites the judicial exception of organizing human activity is not whether “humans can perform” a set of claim limitations, but if humans “do in fact perform” the set of claim limitations. Examiner is unsure of the basis for this analysis and requests Applicant please cite the basis for said test so that Examiner may respond accordingly. Nevertheless, Examiner sustains that humans do perform the tasks outlined in the abstract idea of reading websites to learn new information regarding vaccination pharmacovigilance, analysis a patient record, and make vaccination recommendations according to a decision tree model. The use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG. 4 (January 7, 2019) at p. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1).
Applicant then asserts under sections A. 1-2 that a multiclass decision tree model is not a “black box generic model” is a “specific technical process performed entirely by the MDTM module and hardware processors. First, a human mind is capable of designing and training a multiclass decision tree. Unlike a model such a neural network, there is no evidence of such complexity in a decision tree that handles classification – that is, a human mind can generate logical sequence of rules to perform a pharmacovigilance decision regarding vaccines. Applicant’s assertion that performing a computational inference operation across a learned representational space encoding between hundreds of conditions, dozens of vaccines, and their contraindications is not performable mentally. As indicated in numerous actions prior, human doctors follow inference “operations” to assess hundreds of conditions and dozens of vaccinations for contraindications daily. Merely applying the abstract idea to a computer environment does limit a claim from reciting an abstract idea.
Applicant asserts under sections A. 3-5 that the EMR value detection, pause/resume mechanism, cross source conflict determination, and early stop resource optimization process are all computer specific and not abstract in nature. Examiner is unpersuaded – under Applicant’s rational, no computer software product could be rejected under 35 USC 101 as a software algorithm necessarily controls a computer. The complexity of the calculations is not a consideration under the judicial exception of methods of organizing human activity (see Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025) wherein the mere use of machine learning to complete a specific task does not automatically quality the claim as non-abstract under Step 2A Prong 1, stating “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved” (id. p. 15)). Furthermore, Although a general-purpose computer can perform calculations at a rate and accuracy that can far outstrip the mental performance of a skilled artisan, the nature of the activity is essentially the same, and constitutes an abstract idea. See Bancorp Serves., L.L. C. v. Sun Life Assur. Co. of Canada (U.S.) (holding that “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”); see also SiRF Tech., Inc. v. Int' l Trade Comm ' n, (Fed. Cir. 2010) (holding that: In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations).
Step 2A – Judicial Exception Analysis, Prong 2:
Applicant asserts under section B. 1 that training a pipeline improves clinical decision support, that the source of the training data and the goal of the model improve the model and machine learning itself. Examiner disagrees. As outlined in the previous responses, the instant application did not invent natural language parsing or web based scrapers for medical data collection. Nor did the instant application present the use of web scrapers utilizing natural language processing as an improvement to machine learning in the instant specification. There is no evidence in the written specification of the problem Applicant is purporting to solve. Instead, the specification descriped the problem and the solution as rooted in an improvement to the abstract idea itself and not a technical failure of a computer system. The additional elements can best be characterized as tools to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples., v.”). The instant application has not improved the method for saving data electronically, utilizing a web scraper, natural language processing, or machine learning algorithms in any capacity. The instant claims merely recite known technical solutions to solve a problem with the abstract idea.
Applicant asserts under section B. 2 that the decision tree model output is “a specific improved technical output having reduced characteristics of delay, oversight, and error”. Examiner notes as stated above – no such problem statement or solution is presented in the originally filed application. Furthermore the use of a classification algorithm trained on medical journal data is not a “unconventional technical solution” but instead a new application of a known solution to a different problem in the abstract idea.
Applicant asserts under section B. 3-5 the EMR field-value detection and pause/resume mechanism solves a data integrity problem, the NER+SM cross-source conflict detection improves a system’s conflict-detection capability, and an “early stop” mechanism improves computational efficiency. Again, Examiner notes no such problem statement or solution is presented in the originally filed application. Furthermore – stopping and starting an algorithm or identifying a conflict between datasets based on a conditional statement is not a problem in the prior art nor is the use of a conditional an improvement to technology. These conditional statements are abstract in nature. Humans may stop and resume data analysis or compare data sources that conflict when particular conditions are met. Examiner has addressed these arguments previously in the Office Action mailed 03/20/202612/01/2025 in ¶ 062.
Applicant asserts under section B. 6 that the generation of the output of saving data or not saving data is more than mere-extra solution activity. Examiner is not persuaded. Saving or not saving data to a data base is not enough to amount to significantly more than mere post-solution activity.
Applicant asserts under section B. 7 that the claim as a whole does not recite applying an abstract idea to a generic computer generally – stating the combination of “specific, named, and purpose-configured machine elements whose coordinated operation produces a technical results” amounts to more than generically applying an algorithm to a computer. Examiner disagrees – while the Applicant may have a novel combination of algorithmic designs, data algorithms employed on a generic computer, named or not, are not necessarily subject matter eligible. The test for eligibility as an improvement to computers under Step 2A Prong 2 includes: an indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art (MPEP § 2106.05(a)). Additionally, an important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03 (MPEP § 2106.05(a)(II)). The instant claims seem analogous to MPEP § 2106.05(a)(II) examples that the courts have indicated may not be sufficient to show an improvement to technology, example iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
Step 2B – Additional Elements that Amount to Significantly More:
Applicant asserts under section C. 1-4 that the (1) data-model module pipeline, (2) EMR field-value detection and pause/resume mechanism, (3) the NER+SM cross-source conflict detection, (4) an “early stop” mechanism, and (5) generation of the output of saving data or not saving data are not well understood, routine, and conventional. Applicant asserts under section C. 5 that the ordered combination of these limitations amounts to significantly more than the abstract idea. Examiner is not persuaded the totality of the arguments rely on the novelty of the abstraction. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bacarella et al. (US 20230065759 A1) teaching on adverse drug event prediction and treatment recommendation system utilizing a decision tree model in the Detailed Description in ¶ 0021, ¶ 0032-38, and ¶ 0044.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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