DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
This communication is in response to application filed 4/18/2025. It is noted that application is a continuation of 18/300,249 filed 04/13/2023 (now US Patent No. 12293835) and claims priority to provisional application 63/362,918 filed 4/13/2022. Claim 1 is pending.
Information Disclosure Statement
Information disclosure statement dated 2/26/26 has been acknowledged and considered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of U.S. Patent No. 12,293,835. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims teach substantially the same invention and the differences between the claimed subject matter and the patented subject matter would have been obvious to one of ordinary skill in the art at the time the invention was made.
As per instant claim 1, the claim recites: receiving a treatment authorization request for a treatment, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment; creating an extracted text of the historical record using optical character recognition on the historical record; determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the treatment; identifying authorization criteria for the treatment based on the treatment authorization guidelines; identifying a natural language record processing model corresponding to the treatment authorization guidelines; performing natural language processing on the extracted text of the record to identify relevant record data; determining whether the relevant record data meets the authorization criteria; and authorizing the treatment in response to determining that the relevant record data meets the authorization criteria. The patented claims of U.S. Patent No. 12,293,835 teach receiving a treatment authorization request including a historical record and treatment identifying information, generating extracted text from the historical record using optical character recognition, determining whether authorization analysis should be performed using a machine learning authorization process, identifying authorization criteria based on treatment authorization guidelines, identifying a natural language processing model corresponding to the treatment authorization guidelines, performing natural language processing on extracted text to identify relevant record data, determining whether the relevant record data satisfies the authorization criteria, and authorizing treatment when the criteria are satisfied. Accordingly, the patented claims teach the same machine-learning-based treatment authorization workflow recited in the instant claim. Any differences between the instant claim and the patented claims constitute at most obvious variations that would have been readily apparent to one of ordinary skill in the art because both sets of claims are directed to the same treatment authorization process utilizing OCR, machine learning, treatment authorization guidelines, and natural language processing to determine whether authorization criteria have been satisfied. Therefore, instant claim 1 is not patentably distinct from claim 1 of U.S. Patent No. 12,293,835. This rejection may be overcome by the filing of a terminal disclaimer in compliance with 37 CFR 1.321(c), if appropriate.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is drawn to a machine learning based method for authorizing the performance of a treatment, which is within the four statutory categories (i.e. process).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A machine learning based method for authorizing the performance of a treatment, comprising the steps of:receiving a treatment authorization request for a treatment, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment;creating an extracted text of the historical record using optical character recognition on the historical record;determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the treatment;in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process:
identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data conditional to authorization of performance of the treatment;
identifying a natural language record processing model corresponding to the treatment authorization guidelines;
performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record;
determining whether the relevant record data meets the authorization criteria;
and in response to a determination that the relevant record data meets the authorization criteria, authorizing the treatment.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). The limitations of receiving a treatment authorization request; creating an extracted text of the record; determining whether to analyze authorization performance of the treatment; identifying authorization criteria for the treatment ; identifying a model; performing procession on the extracted text; determining whether the data meets the criteria and authorizing treatment as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is directed to receiving information, analyzing the information and making an authorization based on the analyzed information. This is a concept relating to tracking or filtering information. Tracking information or filtering content has been found to be an abstract idea and a method of organizing human behavior. See MPEP 2106.04(a)(2)(II)(C). This is a method of tracking treatment authorization data thus falling into one category of abstract idea. That is other than reciting tools such as "optical character recognition"; "machine learning" and "natural language processing" nothing in the claim element precludes the steps from describing concepts related to receiving and analyzing treatment authorization data to authorize a treatment. If a claim limitation, under its broadest reasonable interpretation, covers concepts related to interpersonal and intrapersonal activities then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A machine learning based method for authorizing the performance of a treatment, comprising the steps of:receiving a treatment authorization request for a treatment, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment;creating an extracted text of the historical record using optical character recognition on the historical record;determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the treatment;in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process:
identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data conditional to authorization of performance of the treatment;
identifying a natural language record processing model corresponding to the treatment authorization guidelines;
performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record;
determining whether the relevant record data meets the authorization criteria;
and in response to a determination that the relevant record data meets the authorization criteria, authorizing the treatment.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitation of applying machine learning and performing natural language processing recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer. Paragraph [0049] of the Applicant’s Specification recite the machine learning module in a generic manner. Paragraphs [0034] and [0035] recite more specific training of the model, however it is not recited in the claim language. Applicant does not provide adequate evidence or technical reasoning on how the process improves the efficiency of the computer and is beyond conventional use of components, as opposed to the efficiency of the process, or of any other technological aspect of the computer.
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– for example, the recitations of optical character recognition; machine learning; and natural language processing merely limits the abstract idea the environment of a computer.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.” Therefore, the additional elements do not add significantly more to the at least one abstract idea.
Therefore, claim 1 is ineligible under 35 USC §101.
Subject Matter free from Prior Art
The closest prior art of record Elidan (2024/0153639) teaches an automated computerized system for predicting treatment efficacy, comprising: a system server configured to: communicate with external medical sources; store medical information from the external medical sources in a database; and analyze the medical information using Natural Language Processing (NLP) and artificial intelligence tools. However the closest prior art of record does not expressly teach:
determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the treatment;
in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process:
identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data conditional to authorization of performance of the treatment;
identifying a natural language record processing model corresponding to the treatment authorization guidelines;
performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record;
determining whether the relevant record data meets the authorization criteria;
and in response to a determination that the relevant record data meets the authorization criteria, authorizing the treatment.
No final decision on patentability has been as pending rejections remain.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rubenstein (WO 2017059022 A1), the closest foreign prior art of record, teaches systems and methods are provided for predicting treatment-regimen-related outcomes. A combination of machine learning prediction and patient preference assessment is provided for enabling informed consent and precise treatment decisions.
Pomares-Quimbaya et. al. "A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing." Revista Ingenieria y Universidad 22.1: 7(25). Pontificia Universidad Javeriana. (Jan 2018 – Jun 2018), the closest non-patent literature of record teaches strategy for prioritizing EMRs (SPIRE), using natural language processing in combination with the analysis of structured data to identify and rank EMRs that match queries intended to find patients with a specific disease posed by clinical researchers and health administrators.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 5/30/26
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