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 25 November 2025, 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, and 12-31 are currently pending and have been examined.
Claims 1, 5, 10, 12-18, and 20-31 have been amended.
Claims 1-7, 10, and 12-31 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 25 May 2022.
Objections
Examiner acknowledges that appropriate correction to the previous objections have been made and withdraws the objections accordingly.
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.
Claim 21 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims 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, at the time the application was filed, had possession of the claimed invention. Claim 21 has been amended to state wherein the training comprises automatically and autonomously performing, by the one or more hardware processors: extracting the content from a plurality of websites to train the machine machine-learning electronic data model. The specification provides no support for unsupervised or autonomous machine learning training.
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, and 12-31 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, and 12-31 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 data model based on the immune-response trigger formulation instances and the condition instances; matching, via the data model, 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, via the data model, 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 data model; and (b) reading, in response to the inputting, information output from the data model that is associated with the plurality of contraindications; instructing the data model to pause conflict-processing based on detecting that a first EMR field in the group of fields lacks a value; instructing the data model to resume the conflict-processing based on detecting that the first EMR field has received an input of the value; resuming the conflict-processing by: comparing the 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; in response to detecting the conflict via the data-model module: determining to omit the particular vaccine from the set of vaccine candidates; and writing, to a data structure associated with the database, a list of immune-response trigger formulations, wherein the list identifies the set of vaccine candidates.
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 2 recites, in part, wherein the first EMR field corresponds to a patient status field.
Dependent claim 3 recites, in part, wherein detecting the conflict is based on: a value in the allergy field and one or more ingredients of the particular vaccine, the one or more ingredients corresponding to one or more of the plurality of contraindications.
Dependent claim 4 recites, in part, herein detecting the conflict is based on: a value in the medication field and one or more of the plurality of contraindications.
Dependent claim 5 recites, in part, wherein detecting the conflict is based on: the patient status field and one or more of the plurality of contraindications.
Dependent claim 6 recites, in part, wherein the operations further comprise identifying, via the data model, a plurality of ingredients associated with a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 7 recites, in part, identifying, via the data model, (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 vaccines.
Dependent claim 12 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the data model, wherein detecting the conflict is based on a value in the allergy field in the clinical EMR and one or more ingredients of a vaccine formulation of one of the set of vaccine candidates, the one or more ingredients corresponding to one or more of the plurality of contraindications.
Dependent claim 13 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the data model, wherein detecting the conflict is based on a value in the medication field in the clinical EMR and one or more of the plurality of contraindications for a vaccine formulation of one of the set of vaccine candidates.
Dependent claim 14 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the data model, wherein detecting the conflict is based on the status field in the clinical EMR and one or more of the plurality of contraindications for 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 data model, 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.
Dependent claim 16 recites, in part, further comprising automatically extracting the content from a plurality of websites to train the data model, wherein for each immune- response trigger formulation in the set of immune-response trigger formulations, identifying the plurality of contraindications comprises: determining, by the data model, 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 data model, wherein for each immune- response trigger formulation in the set of immune-response trigger formulations, identifying the plurality of contraindications comprises: determining, by the data model, 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 data model, 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 and autonomously performing: extracting the content from a plurality of websites to train the data model.
Dependent claim 22 recites, in part, wherein the data model comprises a multiclass decision tree based data model.
Dependent claim 23 recites, in part, after the identifying, re-applying the data model based on additional content ingested from a plurality of websites retrieved using Uniform Resource Locators (URLs).
Dependent claim 24 recites, in part, wherein the operations further comprise: automatically accessing the data model; 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 an allocation and utilization of resources during a performance of the analyzing without the abstaining.
Dependent claim 30 recites, in part, wherein the operations further comprise initiating the display of information indicating that: after writing the list to the data structure, a particular vaccine in the set of vaccine candidates identified in the list of immune-response trigger formulations 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 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. Claim 10 recites a computer and one or more hardware processors. 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, perform a method. Claims 1, 10, and 18 recite performing certain operations by the one or more hardware processors. 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).
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 machine-learning electronic data 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 machine-learning electronic data model, 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, and 30 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 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. 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).
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. Claim 10 recites a computer and one or more hardware processors. 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, perform a method. Claims 1, 10, and 18 recite performing certain operations by the one or more hardware processors. 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 machine-learning electronic data 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).
Claims 1, 10, 18, and 30 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, and 12-31 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 25 November 2025 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the claims cannot recite a method of organizing human activity as the claims do not recite a human. Examiner disagrees. The claims recite a method of determining a patient’s vaccination recommendation. This is similar to “iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)” recited directly in 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.
Next, Applicant asserts that the claims amount to a practical application as they amount to more than generally linking the use of a judicial exception to a particular technology similar to Example 42. Examiner disagrees. Example 42's abridged background provides the technical problem of latency and incompatibility between remote devices stating "medical providers must continually monitor a patient’s medical records for updated information, which is often-times incomplete since records in separate locations are not timely or readily-shared or cannot be consolidated due to format inconsistencies as well as physicians who are unaware that other physicians are also seeing the patient for varying reasons" wherein the technical solution is reflected in the claim language. The instant application, however, present a non-technical problem –determining and reporting contraindications for vaccinations from analyzing a patient’s EHR (see the instant Specification in ¶ 0015). The solution to the problem is 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.”).
Next, Applicant draws parallels to Example 40 and the instant claims and the technological improvement. This position is not persuasive. Example 40 is directed towards computer networking monitoring. There is no monitoring steps of a computer system in the instant claims and no discussion in the instant specification regarding the failures in the functioning of a computer or other technology similar to the background of Example 40.
Next Applicant asserts that the instant claims do not merely claim improved speed or efficiency inherent with applying the abstract idea on a generic computer. Examiner is not persuaded. There is no evidence in the instant specification or claims that support the position that the instant claims are not applied on a general purpose computer performing in its known capacity.
Applicant then asserts again that the additional elements are not conventional. Examiner cannot reasonably respond to said arguments as Applicant has not outline which additional element, alone or in combination, are not well understood, routine, or conventional. The fact that claim 1 is not rejected under 35 USC 102 or 103 has no bearing on the outcome of the subject matter eligibility rejection – that is, 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).
Applicant again asserts that stopping and/or starting the algorithm when a contraindication is detected is an improvement to computers/technology. Examiner refers Applicant to the office action mailed 30 Jan 2025 ¶ 056, a conditional statement to terminate a process when a decision is made (while abstract in nature) is nonetheless not an unconventional solution — this position is supported by Control! Statements, Rochester Institute of Technology (Jan 31, 2001) teaching on exiting an algorithm if a condition is met. Examiner notes that the detailed nature of the conditional logic is abstract in nature.
Applicant finally asserts that Examiner has failed to provide Berkheimer evidence for the additional elements that are considered well understood, routine, and conventional. Examiner directs Applicant to MPEP § 2106.05(d)(I) regarding the evidentiary requirements. Examiner has provided evidence necessary.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 3682