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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 01/06/2026, the following occurred: Claims 1, 13, 20 and 25 have been amended. Claims 9 and 11 have been canceled.
Claims 1-8, 10 and 12-25 are currently pending.
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 1-8, 10 and 12-25 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.
Claims 1, 13, 20 and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, a system and a non-transitory computer readable media (CRM) for generating clinical trial operation plan, which are within a statutory category.
Step 2A1
Regarding claims 1, 13 and 20, the limitation of (claim 1 being representative) redacting personally identifiable information (PII) in clinical trial context documents containing information related to execution of clinical trials; grounding in the clinical trial context documents, to connect language to content of the clinical trial context documents, that vectorizes the clinical trial context documents; receiving a request to generate a clinical trial operational plan; and responsive to the received request, generating the clinical trial operational plan such that the generated plan includes content based on the clinical trial context documents; providing the generated clinical trial operational plan as an output; receiving revisions to the generated clinical trial operational plan, wherein the revisions applied to the generated clinical trial operational plan correspond to a revised clinical trial operational plan; and grounding in the revised clinical trial operational plan and regarding claim 25- the limitation of receiving a clinical trial operational plan; generating a quality score indicative of a quality of the received clinical trial operational plan, including comparing the received clinical trial operational plan with template clinical trial operational plans, information indicative of standard operating procedures for a clinical trial enterprise, or both; updating the received clinical trial operational plan based on the generated quality score and the received revisions to produce a revised clinical trial operational plan; and grounding a natural language processing (NLP) model in the revised clinical trial operational plan, the NLP model having been previously grounded in clinical trial context documents containing information related to execution of clinical trials and configured to generate clinical trial operational plans responsive to prompts as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting at least one processor (in claims 1 and 25), at least one processor, memory (in claim 13) and one or more non-transitory computer readable media (CRM) and at least one processor (in claim 20), the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the at least one processor, memory and one or more CRM, the claims encompass receiving a request to generate clinical trial operational plan, , generating and providing the clinical trial operational plan, receiving revision to the clinical trial operational plan and grounding the NPL model in the revised clinical trial operational plan in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Note that the broadest reasonable interpretation of “a natural language processing (NLP) model” and in light of the disclosure, represent the creation of mathematical interrelationships between data. See Spec. Para. [0038]-[0040] and [0058] describing the NPL model as a mathematical concept. Thus given the broadest reasonable interpretation, the Examiner interprets the NPL model to be implemented using existing, known mathematical techniques. As such, the NPL model is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claims 1 and 25 recite the additional element of at least one processor. Claim 13 recites the additional element of at least one processor and memory. Claim 20 recites the additional element of one or more non-transitory computer readable media (CRM) and at least one processor. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for performing generic computer functions. See Spec at para. 5, 7 and 8) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 13, 20 and 25 further recite the additional element of a user interface. This additional element is recited at a high level of generality (i.e. a general means to provide/receive data) and amounts to extra solution activity. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Claims 1, 13, 20 and 25 further recites the additional element of using a natural language processing (NLP) model to generate the clinical trial operational plan and a grounding algorithm that is a spectral algorithm that vectorizes the clinical trial context documents. These represent mathematical concepts as described in the specification at para. [0038]-[0040] and [0058]. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the at least one processor, memory and one or more CRM to perform the noted steps amount 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”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
As discussed with respect to integration of the abstract idea into a practical application, the additional element of a user interface was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional in the field of healthcare. Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of NPL model to generate the clinical trial operational plan and a grounding algorithm that is a spectral algorithm that vectorizes the clinical trial context documents were determined to be the application of math to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-8, 10, 12, 14-19 and 21-24 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2-4, 14, 15, 16, 21, 22 and 23 further merely describe(s) generating a quality score. Claim(s) 5, 17 and 24 further merely describe(s) generating a prompt for the NPL model. Claim(s) 6, 7, 18 and 19 further merely describe(s) refining operation of the prompt engineering module. Claim(s) 5, 6, 7, 17, 18, 19 and 24 also include the additional element of “a prompt engineering module” which is interpreted the same as the NPL model and does not provide practical application or significantly more. Claim(s) 8 further merely describe(s) anonymizing the clinical trial context document. Claim(s) 10 further merely describe(s) redacting confidential information based on a clinical ontology. Claim(s) 12 further merely describe(s) the generated clinical trial operational plan.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8, 10 and 12-25 are rejected under 35 U.S.C. 103 as being unpatentable over Gnanasambandam (US 2023/0052573) and in further view of Molero Leon (US 2023/0215577).
REGARDING CLAIM 1
Gnanasambandam discloses a computer implemented method performed by at least one processor, the method comprising: grounding a natural language processing (NLP) model in the clinical trial context documents, the grounding comprising using a grounding algorithm to connect language to content of the clinical trial context documents, wherein the grounding algorithm is a spectral algorithm that vectorizes the clinical trial context documents ([0089] teaches a cognitive intelligence platform (interpreted by examiner as the natural language processing (NLP) model) integrates and consolidates data from various sources and entities and provides a population health management service. The cognitive intelligence platform has the ability to extract concepts, relationships, and draw conclusions from a given text posed in natural language (e.g., a passage, a sentence, a phrase, and a question) by performing conversational analysis which includes analyzing conversational context (interpreted by examiner as the grounding) [0094] teaches an artificial intelligence engine that may continuously learn based on input data (e.g., evidence-based guidelines, clinical trials, physician research, electronic medical records, etc.). . A logical structure (e.g., Nth order logic) may underlie the knowledge graph that uses the predicates to connect various individual elements. The knowledge graph and the logical structure may combine to form a language that recites facts, concepts, correlations, conclusions, propositions, and the like. The knowledge graph and the logical structure may be generated and updated continuously or on a periodic basis by an artificial intelligence engine with evidence-based guidelines, physician research, patient notes in EMRs, physician feedback, and so forth. The predicates and individual elements may be generated based on data that is input to the artificial intelligence engine. The data may include evidence-based guidelines that is obtained from a trusted source, such as a physician. The artificial intelligence engine may continuously learn based on input data (e.g., evidence-based guidelines, clinical trials, physician research, electronic medical records, etc.) and modify the individual elements and predicates and [0536] teaches a node representing “possible complication of” connected to nodes representing “Prediabetes” and “Obesity and Overweight”, and a node representing “prevented by” connected to a node representing “Metformin” and ([0576] teaches the updated care plan 6020.1 may be converted into natural language text by the cognitive intelligence platform 102 using the natural language database 122 according to the techniques disclosed herein. The cognitive intelligence platform 102 may generate action instructions pertaining to the health artifacts included in the care plan 6020.1 (interpreted by examiner as grounding a natural language processing (NLP) model in the clinical trial context documents, the grounding comprising using a grounding algorithm to connect language to content of the clinical trial context documents, wherein the grounding algorithm is a spectral algorithm that vectorizes the clinical trial context documents)); receiving, via a user interface, a request to generate a clinical trial operational plan ([0004] teaches receiving a selection of a type of the care plan to implement for the patient and generating a care plan based on the type selected. [0123] teaches the patient graph for the condition of the user may be compared (e.g., projected on) to the knowledge graph for the condition of the user to generate a care plan. The cognitive intelligence platform may generate the care plan based on the areas of the condition the user specified to manage (interpreted by examiner as receiving a request to generate a clinical trial operational plan) and the care plan may be transmitted to the user device for presentation in the patient viewer, the clinic viewer, and/or the administrator viewer (interpreted by examiner as the user interface)); by the NLP model and responsive to the received request, generating the clinical trial operational plan such that the generated plan includes content based on the clinical trial context documents ([0004] teaches the care plan includes an action instruction based on a patient graph of the patient and a knowledge graph including ontological medical data and receiving patient data that indicates health related information associated with the patient. [0269] teaches the health related information may correspond to known facts, concepts, and/or any suitable health related information that are discovered or provided by a trusted source (e.g., a physician having a medical license and/or a certified/accredited healthcare organization), such as clinical trials and the likes (interpreted by examiner as generating the clinical trial operational plan such that the generated plan includes content based on the clinical trial context documents). Fig. 9A-12 and [0302] teaches the action calendar is managed through the conversational stream between the cognitive agent 110 and the user. The action calendar aligns to care and wellness protocols, which are personalized to the risk condition or wellness needs of the user. The action calendar is also contextually aligned (e.g., what is being required or searched by the user) and hyper local (e.g., aligned to events and services provided in the local community specific to the user)); providing the generated clinical trial operational plan as an output via the user interface ([0123] teaches the care plan may be transmitted to the user device for presentation in the patient viewer, the clinic viewer, and/or the administrator viewer (interpreted by examiner as providing the generated clinical trial operational plan as an output via the user interface)); receiving, via the user interface, revisions to the generated clinical trial operational plan, wherein the revisions applied to the generated clinical trial operational plan correspond to a revised clinical trial operational plan ([0004] teaches modifying the care plan to generate a modified care plan in real-time or near real-time based on the patient data, and causing the modified care plan to be presented on a computing device of a medical personnel. [0567] teaches If the cognitive intelligence platform 102 receives the input data 6010, 6012, and/or 6014 when the care plan 6002 is presented to the user 6000 on the user device 104, and the cognitive intelligence platform 102 detects a negative emotion (e.g., angry) and/or tone (e.g., hostile), the cognitive intelligence platform 102 may modify the care plan 6002 to generate an updated care plan 6020 (interpreted by examiner as receiving, via the user interface, revisions to the generated clinical trial operational plan, wherein the revisions applied to the generated clinical trial operational plan correspond to a revised clinical trial operational plan)); and grounding, using the grounding algorithm, the NLP model in the revised clinical trial operational plan ([0576] teaches the updated care plan 6020.1 may be converted into natural language text by the cognitive intelligence platform 102 using the natural language database 122 according to the techniques disclosed herein. The cognitive intelligence platform 102 may generate action instructions pertaining to the health artifacts included in the care plan 6020.1 (interpreted by examiner as grounding the NLP model in the revised clinical trial operational plan)).
Gnanasambandam does not explicitly disclose, however Molero Leon discloses:
redacting personally identifiable information (PII) in clinical trial context documents containing information related to execution of clinical trials; redacting personally identifiable information (PII) in the revised clinical trial operational plan (Molero Leon at [0049] teaches if it is determined that responding to a query risks revealing personally identifiable information, the cloud-based application may determine whether a user is authorized to view the information for subjects that would be represented in the data. If not, the cloud-based application may reject the query or potentially modify the query to include less restrictive constraints and [0048] teaches the cloud-based application may operate to implement data-privacy protocols that enable an entity to transmit and/or receive one or more data records or other information characterizing subjects (e.g., experiencing medical symptoms and/or having a possible or confirmed diagnosis of a medical condition) with external entities, while satisfying the constraints imposed by data-privacy rules across various jurisdictions. The cloud-based application can be configured to algorithmically assess data-privacy violations and automatically omit, obfuscate or otherwise modify data records to comply with data-privacy rules (interpreted by examiner as means for redacting personally identifiable information (PII)));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the system and method for generating care plans of Gnanasambandam to incorporate redacting personally identifiable information (PII) as taught by Molero Leon, with the motivation of allowing systems to exchange subject information with external entities without violating data-privacy rules (Molero Leon at [0050]).
REGARDING CLAIM 2
Gnanasambandam and Molero Leon disclose the limitation of claim 1.
Gnanasambandam further discloses:
The method of claim 1, further comprising generating a quality score indicative of a quality of the generated clinical trial operational plan (Fig. 62 and [0594] teaches training data may be generated by collecting the care plans for medical conditions that received scores in the second range (high scores, positive feedback) and the care plans for medical conditions that received scores in the first range (low scores, positive feedback), and determining the differences in the care plans that resulted in the scores in the first range and the second range (interpreted by examiner as generating a quality score indicative of a quality of the generated clinical trial operational plan)).
REGARDING CLAIM 3
Gnanasambandam and Molero Leon disclose the limitation of claims 1 and 2.
Gnanasambandam further discloses:
The method of claim 2, comprising generating the quality score based on a comparison between the generated clinical trial operational plan and template clinical trial operational plans (Fig. 61 and [0586] teaches at block 6104, responsive to the comparing, the processing device may generate the care plan including another subset of the set of health artifacts. The subset of the health artifacts may correspond with actions already performed by the patient, and another subset of the set of the health artifacts may correspond with actions that have not yet been performed by the patient. The comparing may include projecting the second data structure onto the first data structure (interpreted by examiner as generating the quality score based on a comparison between the generated clinical trial operational plan and template clinical trial operational plans)).
REGARDING CLAIM 4
Gnanasambandam and Molero Leon disclose the limitation of claims 1 and 2.
Gnanasambandam further discloses:
The method of claim 2, comprising generating the quality score based on a relationship between the generated clinical trial operational plan and information indicative of standard operating procedures for a clinical trial enterprise (Fig. 62 and [0594] teaches at block 6204, the processing device may update a machine learning model based on the net promoter score being below a threshold value to obtain an updated machine learning model that outputs different health artifacts for subsequent patients having the condition. For example, training data may be generated by collecting the care plans for medical conditions that received scores in the second range (high scores, positive feedback) and the care plans for medical conditions that received scores in the first range (low scores, positive feedback), and determining the differences in the care plans that resulted in the scores in the first range and the second range (interpreted by examiner as generating the quality score based on a relationship between the generated clinical trial operational plan and information indicative of standard operating procedures for a clinical trial enterprise)).
REGARDING CLAIM 5
Gnanasambandam and Molero Leon disclose the limitation of claim 1.
Gnanasambandam further discloses:
The method of claim 1, comprising based on the received request, generating, by a prompt engineering module, a prompt for the NLP model, and wherein the NLP model generates the clinical trial operational plan responsive to the prompt (Fig. 9-11 and 15 and [0317] teaches In response to the user-generated natural language medical information query, the artificial intelligence-based diagnostic conversation agent selects a diagnostic fact variable set relevant to generating a medical advice query answer for the user-generated natural language medical information query by classifying the user-generated natural language medical information query into one of a set of domain-directed medical query classifications associated with respective diagnostic fact variable sets (FIG. 15, block 1504) (interpreted by examiner as generating, by a prompt engineering module, a prompt for the NLP model, and wherein the NLP model generates the clinical trial operational plan responsive to the prompt)).
REGARDING CLAIM 6
Gnanasambandam and Molero Leon disclose the limitation of claims 1 and 5.
Gnanasambandam further discloses:
The method of claim 5, comprising refining operation of the prompt engineering module responsive to the received revisions to the generated clinical trial operational plan (Fig. 60A-E and [0570] teaches in some embodiments, the different set of health artifacts in the updated care plan 6020 may be selected based on the detected tone and/or emotion. For example, if the detected tone and/or emotion is positive, a machine learning model may be trained to generate updated care plans that include health artifacts with which the user 6000 is likely to interact due to the positive tone and/or emotion (interpreted by examiner as refining operation of the prompt engineering module responsive to the received revisions to the generated clinical trial operational plan)).
REGARDING CLAIM 7
Gnanasambandam and Molero Leon disclose the limitation of claims 1 and 5.
Gnanasambandam further discloses:
The method of claim 5, comprising refining operation of the prompt engineering module responsive to a quality score indicative of a quality of the generated clinical trial operational plan (Fig. 62 and [0594] teaches at block 6204, the processing device may update a machine learning model based on the net promoter score being below a threshold value to obtain an updated machine learning model that outputs different health artifacts for subsequent patients having the condition. For example, training data may be generated by collecting the care plans for medical conditions that received scores in the second range (high scores, positive feedback) and the care plans for medical conditions that received scores in the first range (low scores, positive feedback), and determining the differences in the care plans that resulted in the scores in the first range and the second range (interpreted by examiner as refining operation of the prompt engineering module responsive to a quality score indicative of a quality of the generated clinical trial operational plan)).
REGARDING CLAIM 8
Gnanasambandam and Molero Leon disclose the limitation of claim 1.
Gnanasambandam does not explicitly disclose anonymizing the clinical trial context documents prior to grounding the NLP model in the clinical trial context documents, however Molero Leon discloses:
The method of claim 1, comprising anonymizing the clinical trial context documents prior to grounding the NLP model in the clinical trial context documents (Molero Leon at [0050] teaches some embodiments of the present disclosure provide a technical advantage over conventional systems by providing a cloud-based application configured to exchange subject information with external entities without violating data-privacy rules. The data representations can further facilitate data queries, in that fields for which constraints can be identified can be identifiable. Queries for subject populations may then be performed capitalizing on logic operands and/or basic searches rather than performing natural-language-processing queries (interpreted by examiner as anonymizing the clinical trial context documents prior to grounding the NLP model in the clinical trial context documents)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the system and method for generating care plans of Gnanasambandam to incorporate anonymizing the clinical trial context documents as taught by Molero Leon, with the motivation of allowing systems to exchange subject information with external entities without violating data-privacy rules (Molero Leon at [0050]).
REGARDING CLAIM 10
Gnanasambandam and Molero Leon disclose the limitation of claims 1 and 8.
Gnanasambandam does not explicitly disclose wherein anonymizing the clinical trial context documents comprises redacting confidential information based on a clinical ontology, however Molero Leon discloses:
The method of claim 8, wherein anonymizing the clinical trial context documents comprises redacting confidential information based on a clinical ontology (Molero Leon at [0048] teaches the cloud-based application may operate to implement data-privacy protocols that enable an entity to transmit and/or receive one or more data records or other information characterizing subjects (e.g., experiencing medical symptoms and/or having a possible or confirmed diagnosis of a medical condition) with external entities, while satisfying the constraints imposed by data-privacy rules across various jurisdictions. The cloud-based application can be configured to algorithmically assess data-privacy violations and automatically omit, obfuscate or otherwise modify data records to comply with data-privacy rules (interpreted by examiner as anonymizing the clinical trial context documents comprises redacting confidential information based on a clinical ontology)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the system and method for generating care plans of Gnanasambandam to incorporate redacting confidential information as taught by Molero Leon, with the motivation of allowing systems to exchange subject information with external entities without violating data-privacy rules (Molero Leon at [0050]).
REGARDING CLAIM 12
Gnanasambandam and Molero Leon disclose the limitation of claim 1.
Gnanasambandam further discloses:
The method of claim 1, wherein the generated clinical trial operational plan is one or more of a protocol design, a trial participant recruitment or retention plan, a site selection or management plan, a data collection or analysis plan, or a regulatory compliance plan (Fig. 11 and [0302] teaches FIG. 11 illustrates aspects of an action calendar, in accordance with various embodiments. The action calendar is managed through the conversational stream between the cognitive agent 110 and the user. The action calendar aligns to care and wellness protocols, which are personalized to the risk condition or wellness needs of the user (interpreted by examiner as wherein the generated clinical trial operational plan is one or more of a protocol design)).
REGARDING CLAIMS 13-25
Claims 13-25 are analogous to Claims 1-7 and 12 thus Claims 13-25 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-7and 12.
Response to Arguments
Drawing Objections
Regarding the drawing objection(s), the Applicant has amended drawings 4, 6 and 7 to overcome the basis/bases of objection.
Rejection under 35 U.S.C. § 112(b)
Regarding the indefinite rejection of claims 1 and 25, the Applicant has amended the claims to overcome the bases of rejection.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-8, 10 and 12-25, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
A grounding algorithm, which connects language to the content of context documents as a part of a training process, provides increased accuracy of the NLP model compared to off the shelf models in that it allows the NLP model to gain more context for generation of future operational plans (Application, [0055].) The redaction engine can ensure that sensitive information remains secure before grounding the NLP model in the context documents (Application [0093]). Thus, the grounding process for the NLP model has the technical advantage of improving accuracy and security in "redacting personally identifiable information (PII) in clinical trial context documents containing information related to execution of clinical trials" and "grounding a natural language processing (NLP) model in the clinical trial context documents, the grounding comprising using a grounding algorithm to connect language to content of the clinical trial context documents, wherein the grounding algorithm is a spectral algorithm that vectorizes the clinical trial context documents" as recited in amended independent claim 1. Further, these algorithmic, computer-implemented redacting and grounding process cannot be equated to "managing personal behavior or interaction between people (i.e., rules or instructions)" (Office Action, page 5).
Regarding 1, The Examiner respectfully disagrees. The additional element of using a natural language processing (NLP) model to generate the clinical trial operational plan and a grounding algorithm that is a spectral algorithm that vectorizes the clinical trial context documents represent mathematical concepts as described in the specification at para. [0038]-[0040] and [0058]. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application and MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-8, 10 and 12-25, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Gnanasambandam does not disclose a method in which includes, among other features, "redacting personally identifiable information (PII) in clinical trial context documents containing information related to execution of clinical trials", "using a grounding algorithm to connect language to content of the clinical trial context documents, wherein the grounding algorithm is a spectral algorithm that vectorizes the clinical trial context documents", and "redacting personally identifiable information (PII) in the revised clinical trial operational plan," as recited in amended claim 1 and as discussed in the interview of December 16, 2025.
Regarding 1, The Examiner respectfully disagrees. Upon further consideration of the references, Gnanasambandam discloses grounding a natural language processing (NLP) model in the clinical trial context documents, the grounding comprising using a grounding algorithm to connect language to content of the clinical trial context documents, wherein the grounding algorithm is a spectral algorithm that vectorizes the clinical trial context documents, as Gnanasambandam teaches the ability to extract concepts, relationships, and draw conclusions from a given text posed in natural language and teaches for example a node representing “possible complication of” connected to nodes representing “Prediabetes” and “Obesity and Overweight”, and a node representing “prevented by” connected to a node representing “Metformin”. Moreover, Molero Leon discloses redacting personally identifiable information (PII) as it teaches automatically omit, obfuscate or otherwise modify data records to comply with data-privacy rules. Please refer to the rejection under for more detailed rejection. Given the broadest reasonable interpretation, the cited references in combination teach the claimed features.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP §706.07(a). 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.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Pooleery (US 2022/0036978) teaches systems and methods for management of clinical trial electronic health records and machine learning systems therefor. Zahlmann (US 2005/0251011) teaches clinical trial image and data processing system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683