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 .
Response to Amendment
This is a Non-Final Office action in response to communications filed on June 23, 2026. Applicant amended claims 1-3, 6-8, 10-16, and 18-21 and cancelled claim 17. Claims 1-16 and 18-21 remain pending in this application.
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 1-16 and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes for claims 1-16 and 18-21.
Claims 1-14 and 21 are drawn to a method for determining a neurocognitive result (i.e., process). Claims 15-16 and 18-19 are drawn to a system for determining a neurocognitive result (i.e., a manufacture). Claim 20 is drawn to a non-transitory computer-readable media for determining a neurocognitive result (i.e., a manufacture)
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes, for claims 1-16 and 18-21.
Claim 1 recites:
A computer-implemented method comprising: generating, by one or more processors, inputs associated with a user profile for one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: receiving, by the one or more processors and from a user device, at least one data entry generated, via the user device, in association with the user profile by a user;
performing, by the one or more processors and using a trained content machine learning model, content analysis on the at least one data entry to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry;
determining, by the one or more processors and using one or more trained context machine learning models, one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data, handwriting data, speech data, or psychosocial and economic health data associated with the user profile;
and storing, by the one or more processors, the historical user data and the one or more baseline patterns in the modality data as the inputs associated with the user profile;
in response to receiving a request to initiate a memory recall session in association with the user profile, providing, by the one or more processors, at least the historical user data from the stored inputs to a trained interactive-generating machine learning model for processing;
determining, by the one or more processors and using the trained interactive-generating machine learning model, one or more profile-specific interactives based on the historical user data, wherein the one or more profile-specific interactives comprise one or more questions that include at least a portion of the historical user data;
transmitting, by the one or more processors and to the user device, instructions to cause the user device to display the one or more profile-specific interactives during the memory recall session;
determining, by the one or more processors, an interactive result based on one or more responses to the one or more profile-specific interactives and the historical user data;
providing, by the one or more processors, at least the one or more baseline patterns in the modality data from the stored inputs to one or more modality-specific machine learning models for processing with current modality data;
determining, by the one or more processors and using the one or more modality- specific machine learning models, a deviation from the one or more baseline patterns in the modality data based on the current modality data;
determining, by the one or more processors, a neurocognitive result based on the interactive result and the deviation;
and causing, by the one or more processors, the neurocognitive result to be displayed via a graphical user interface of the user device.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can obtain data (e.g. responses) associated with a user, analyze facial, handwriting, speech, and health results, retain and record user data and associated patterns, and determine a neurocognitive disorder. Applicant of claimed invention discloses “assessments for diagnosis and/or monitoring cognitive disorders are given to individuals in a clinical setting when they meet with a medical provider.” [0025]
Independent claims 15 and 20 describe nearly identical steps as claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Dependent claims 2-14, 16, 18-19, and 21 are directed towards mini-tasks (defining interactives, extracting and storing historical data, determining interactive results, etc.) for a method that determines a neurocognitive result. Each claim amounts to a form of collecting, generating, and analyzing information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). As such, the Examiner concludes that claims 2-14, 16, 18-19, and 21 recite an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer processor (independent claims 1, 15, and 20 and dependent claims 2-14, 16, 18-19, and 21) is equivalent to adding the words “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer.
Similarly, the limitations of a computer processor (independent claims 1, 15, and 20 dependent claims 2-14, 16, 18-19, and 21) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-14, 16, 18-19, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1, 15, and 20 and dependent claims 2-14, 16, 18-19, and 21 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2-14, 16, 18-19, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above.
Therefore, claims 1-16 and 18-21 are not eligible subject matter under 35 USC 101.
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 (i.e., changing from AIA to pre-AIA ) 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-16 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable under WO 2022067189 A1 (“Pascual-Leone”) in view of US 20210290149 A1 (“Mazza”).
In regards to claim 1, Pascual-Leone discloses the following limitations with the exception of the underlined limitations.
A computer-implemented method comprising: generating, by one or more processors ([0136], “computer system … may include … one or more processors”), inputs associated with ([0054], “data are … from … user input”) a user profile for ([0093], “groups may be based on age, gender, or any … features”) one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs ([0032], “the present disclosure provides … methods … for machine-learning-assisted determination of … patient cognitive health”), wherein generating the inputs includes ([0054], “data are … from direct user input”): receiving, by the one or more processors ([0136], “computer system … may include … one or more processors”) and from a user device, at least one data entry generated, via the user device ([0044], “devices may be configured to record data regarding a user”), in association with the user profile ([0093], “groups may be based on age, gender, or any … features”);
performing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using a trained content machine learning model ([0033], “data … may be provided to a machine learning system”), content analysis on the at least one data entry to determine ([0048], “the interpretation of … data inputs is … relative to analysis”) historical user data, ([0100], “historical data of these interventions can be used”) the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry ([0113], “a combination of questions … may be used to assess mood”);
determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using one or more trained context machine learning models ([0033], “data … may be provided to a machine learning system”), one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), handwriting data ([0125], “The subject is asked to trace a line with both their dominant and nondominant hand”), speech data ([0033], “the … assessments may include … speech elicitation tasks”), or psychosocial and economic health data associated with ([0072], “frailty can be … characterized by … decreases in physical, psychological and social functioning. Utilizing the deficit-accumulation clinical model, routinely collected items … such as medical history and functional abilities … can be used to compute a frailty index”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
and storing ([0035], “the collected information may … be … stored”), by the one or more processors ([0136], “The … computer system … may include … one or more processors”), the historical user data ([0100], “historical data of these interventions can be used”) and the one or more baseline patterns in the modality data as the inputs associated with ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
in response to receiving a request to initiate a memory recall session in association with ([0059], “features … include … immediate recall; delayed recall; the time taken to recall each word; the accuracy of words recalled”) the user profile ([0093], “groups may be based on age, gender, or any … features”), providing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), at least the historical user data from the stored inputs to ([0100], “historical data of these interventions can be used”) a trained interactive-generating machine learning model for processing ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”);
determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using the trained interactive-generating machine learning model ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”), one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives based on the historical user data ([0100], “historical data of these interventions can be used”), wherein the one or more profile-specific interactives comprise one or more questions that include at least a portion of the historical user data ([0033], “tasks and/or assessments may include … a lifestyle/health history questionnaire”);
transmitting, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and to the user device ([0044], “devices may be configured to record data regarding a user”), instructions to cause ([0009], “program instructions are executable by a processor”) the user device to display the one or more profile-specific interactives during the memory recall session ([0141], “Computer system … may … communicate with … a display”);
determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), an interactive result based on the one or more responses ([0044], “data inputs may include … user interactions … responses to … stimulus”) to the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives and the historical user data ([0100], “historical data of these interventions can be used”);
providing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), at least the one or more baseline patterns in the modality data from the stored inputs to ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) one or more modality-specific machine learning models for processing with current modality data ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”);
determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using the one or more modality-specific machine learning models ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”), a deviation from the one more baseline patterns in the modality data based on the current modality data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”);
determining, by the one or more processors, a neurocognitive result based on the interactive result ([0044], “the system may combine … features with specific medical information obtained from … the user (e.g., … neuropsychological tests) … the system may combine these features to gain additional insights as to the role … neurological, and psychological subsystems play in contributing to changes in brain health and disease development”) and the deviation ([0093], “data … using the … model to determine if … the new subject … deviates from normal neurological functioning”);
and causing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), the neurocognitive result to be displayed via a graphical user interface of the user device ([0141], “Computer system … may … communicate with … a keyboard, a pointing device, a display”).
Mazza discloses
interactives ([0034], “neurological … tests … may include interactive tests, such as games” Examiner notes that in claimed invention “at least one interactive includes one or more games” [0032].)
Pascual-Leone and Mazza are considered analogous to the claimed invention because they are in the field of cognitive health and neurological impairment detection systems. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a computer-implemented method comprising: generating, by one or more processors, inputs associated with a user profile for one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: receiving, by the one or more processors and from a user device, at least one data entry generated, via the user device, in association with the user profile; performing, by the one or more processors and using a trained content machine learning model, content analysis on the at least one data entry to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry; determining, by the one or more processors and using one or more trained context machine learning models, one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data, handwriting data, speech data, or psychosocial and economic health data associated with the user profile; and storing, by the one or more processors, the historical user data and the one or more baseline patterns in the modality data as the inputs associated with the user profile; in response to receiving a request to initiate a memory recall session in association with the user profile, providing, by the one or more processors, at least the historical user data from the stored inputs to a trained interactive-generating machine learning model for processing; determining, by the one or more processors and using the trained interactive-generating machine learning model, one or more profile-specific based on the historical user data, wherein the one or more profile-specific comprise one or more questions that include at least a portion of the historical user data; transmitting, by the one or more processors and to the user device, instructions to cause the user device to display the one or more profile-specific during the memory recall session; determining, by the one or more processors, an interactive result based on the one or more responses to the one or more profile-specific and the historical user data; providing, by the one or more processors, at least the one or more baseline patterns in the modality data from the stored inputs to one or more modality-specific machine learning models for processing with current modality data; determining, by the one or more processors and using the one or more modality-specific machine learning models, a deviation from the one more baseline patterns in the modality data based on the current modality data; determining, by the one or more processors, a neurocognitive result based on the interactive result and the deviation;
and causing, by the one or more processors, the neurocognitive result to be displayed via a graphical user interface of the user device, as disclosed by Pascual-Leone, interactives, as disclosed by Mazza, to provide interactive tests, such as games, for a system for generating indications of neurological impairment. One skilled in the art would understand and recognize the value of the addition of neurological tests, interactive tests, and games to improve a system that generates indications of neurological impairment.
In regards to claim 2, Pascual-Leone discloses the following limitations with
wherein the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives include at least one multiple choice question, free response question, true-false question, typing test, speaking test, or narration test ([0132], “Lifestyle questionnaires … a patient may be asked a series of questions relating to their lifestyle” Examiner notes that lifestyle questionnaires may include multiple choice and true-false questions.).
In regards to claim 3, Pascual-Leone discloses the following limitations with the exception of the underlined limitations.
wherein the one or more responses include one or more answers to ([0105], “A battery of assessments are administered to a patient and … data collected on their responses”) the one or more questions of ([0132], “a patient may be asked a series of questions”) the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives
Mazza discloses
interactives ([0034], “neurological … tests … may include interactive tests, such as games”).
Pascual-Leone and Mazza are considered analogous to the claimed invention because they are in the field of cognitive health and neurological impairment detection systems. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a computer-implemented method comprising: generating, by one or more processors, inputs associated with a user profile for one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: receiving, by the one or more processors and from a user device, at least one data entry generated, via the user device, in association with the user profile; performing, by the one or more processors and using a trained content machine learning model, content analysis on the at least one data entry to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry; determining, by the one or more processors and using one or more trained context machine learning models, one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data, handwriting data, speech data, or psychosocial and economic health data associated with the user profile; and storing, by the one or more processors, the historical user data and the one or more baseline patterns in the modality data as the inputs associated with the user profile; in response to receiving a request to initiate a memory recall session in association with the user profile, providing, by the one or more processors, at least the historical user data from the stored inputs to a trained interactive-generating machine learning model for processing; determining, by the one or more processors and using the trained interactive-generating machine learning model, one or more profile-specific based on the historical user data, wherein the one or more profile-specific comprise one or more questions that include at least a portion of the historical user data; transmitting, by the one or more processors and to the user device, instructions to cause the user device to display the one or more profile-specific during the memory recall session; determining, by the one or more processors, an interactive result based on the one or more responses to the one or more profile-specific and the historical user data; providing, by the one or more processors, at least the one or more baseline patterns in the modality data from the stored inputs to one or more modality-specific machine learning models for processing with current modality data; determining, by the one or more processors and using the one or more modality-specific machine learning models, a deviation from the one more baseline patterns in the modality data based on the current modality data; determining, by the one or more processors, a neurocognitive result based on the interactive result and the deviation;
and causing, by the one or more processors, the neurocognitive result to be displayed via a graphical user interface of the user device, wherein the one or more responses include one or more answers to the one or more questions of the one or more profile-specific, as disclosed by Pascual-Leone, interactives, as disclosed by Mazza, to provide interactive tests, such as games, for a system for generating indications of neurological impairment. One skilled in the art would understand and recognize the value of the addition of neurological tests, interactive tests, and games to improve a system that generates indications of neurological impairment.
In regards to claim 4, Pascual-Leone discloses
wherein the interactive result is a measure of how accurate the one or more responses are relative to a pre-determined correct response ([0058], “health data may be transcribed to words … and metrics may be calculated such as: number of words the subject was able to recall; and whether the words in the correct order”).
In regards to claim 5, Pascual-Leone discloses
wherein the neurocognitive result includes an average or a weighted average of the interactive result and the current modality data ([0060], “moving averages, decay functions, and/or smoothing functions may be applied to the raw health data” Examiner notes that the raw health data is modality data that may include facial analysis, handwriting analysis, speech analysis, and psychosocial analysis results.).
In regards to claim 6, Pascual-Leone discloses
wherein the one or more of the named entity, the mood, the topic, the summary, or the data entry metadata are extracted by applying natural language processing (NLP) analysis to the at least one data entry ([0081], “the … model may include an index across all of the features along with metadata associated with all of the correlations … the metadata may include machine learning models” Examiner notes that machine learning models use natural language processing.).
In regards to claim 7, Pascual-Leone discloses
wherein the data entry metadata includes at least one of user data, activity data, event data, location data, date data, time data, season data, or memory-type data ([0081], “the metadata may include statistical models, such as Bayesian models of … probability distributions” Examiner notes that statistical models may use activity, event, location, date, time, season, and memory-type data.).
In regards to claim 8, Pascual-Leone discloses
wherein determining the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives comprises: determining one or more target responses based on the historical user data ([0047], “data inputs may include … requiring the patient to … respond to … stimulus … historically captured during day to day activity”);
and determining the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives based on the one or more target responses ([0047], “data from … health devices that can record … responses … may be provided as input”).
In regards to claim 9, Pascual-Leone discloses
wherein determining the interactive result comprises: comparing the one or more responses to the one or more target responses ([0060], “For each of those samples …, statistics (e.g., mean, min, max, and standard deviation) may be determined” Examiner notes that the statistical method may also include t-test and analysis of variance, and z-test for comparing responses to target responses.);
and generating a score based on the comparison ([0060], “values can be z-scored to provide robust information” Examiner notes that a z-score is based on a comparison.).
In regards to claim 10, Pascual-Leone discloses
further comprising: determining, via the one or more processors, a neurocognitive decline risk based on the neurocognitive result and at least a portion of the historical user data ([0043], “labels such as Alzheimer’s, Parkinson’s, … cognitive impairment … can be assigned to samples … machine learning models such as linear regression, deep learning, random forests … may be used to produce a prediction model for that clinical label” Examiner notes that Alzheimer’s and Parkinson’s are labels associated with neurocognitive decline.);
and causing, by the one or more processors, the neurocognitive decline risk to be displayed via the graphical user interface of at least one of the user device or a medical provider device ([0106], “when displaying the model output, … the clinician-generated clusters may be provided to the user”).
In regards to claim 11, Pascual-Leone discloses
wherein, when the modality data includes the facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), the determining the deviation comprises ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”): receiving, as part of the current modality data, facial expression data associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), the facial expression data including at least one of eye appearance data, eyeball movement data, lip movement data, or head movement data captured ([0032], “a system can … capture raw health data regarding … eye motion”) in associated with the user profile ([0093], “groups may be based on age, gender, or any … features”);
and determining, using a trained facial analysis machine learning model from the one or more modality-specific machine learning models and based on the facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), a facial expression analysis result indicating a deviation from a baseline pattern in the facial expression data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”) associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), wherein the trained facial analysis machine learning model has been trained with facial analysis training data that includes at least one of eye appearance data, eyeball movement data, lip movement data, or head movement data associated with a plurality of user profiles ([0093], “groups may be based on age, gender, or any … features”) to infer the facial expression analysis result ([0077], “a … machine learning approach may be applied to train a model where the data … may be … Eye tracking data”).
In regards to claim 12, Pascual-Leone discloses
wherein, when the modality data includes the handwriting data, the determining the deviation comprises ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”): receiving, as part of the current modality data, handwriting data specific to the user, the handwriting data associated with the user profile ([0093], “groups may be based on age, gender, or any … features”) including at least one of writing pattern data, writing pressure data, or writing speed data captured ([0125], “The subject is asked to trace a line with both their dominant and nondominant hand” Examiner notes that tracing a line is a form of writing patterns.);
and determining, using a trained handwriting analysis machine learning model from the one or more modality-specific machine learning models and based on the handwriting data, the handwriting analysis result indicating a deviation from a baseline pattern in the handwriting data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”) associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), wherein the trained handwriting analysis machine learning model has been trained with handwriting analysis training data that includes at least one of writing pattern data, writing pressure data, or writing speed data associated with a plurality of user profiles ([0093], “groups may be based on age, gender, or any … features”)to infer the handwriting analysis result ([0077], “a … machine learning approach may be applied to train a model where the data … may be … Drawing assessment data”).
In regards to claim 13, Pascual-Leone discloses
wherein, when the modality data includes the speech data, the determining the deviation comprises ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”): receiving, as part of the current modality data, speech data associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), the speech data including at least one of speech pattern data, speech context data, word correctness data, or word relevancy data ([0033], “the … assessments may include … speech elicitation tasks” Examiner notes that speech elicitation tasks provide data for speech analysis.);
and determining, using a trained speech analysis machine learning model from the one or more modality-specific machine learning models and based on the speech data, a speech analysis result ([0033], “the … assessments may include … speech elicitation tasks” Examiner notes that speech elicitation tasks provide data for speech analysis.) indicating a deviation from a baseline pattern in the speech data for the user ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”), wherein the trained speech analysis machine learning model has been trained with speech analysis training data that includes at least one of speech pattern data, speech context data, word correctness data, or word relevancy data associated with a plurality of user profiles ([0093], “groups may be based on age, gender, or any … features”) to infer the speech analysis result ([0077], “a … machine learning approach may be applied to train a model where the data … may be … Voice recording data”).
In regards to claim 14, Pascual-Leone discloses
wherein, when the modality data includes the psychosocial and economic health data, the determining the deviation further comprises ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”): receiving, as part of the current modality data, psychosocial and economic health data associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), the psychosocial and economic health data including at least one of physical environment data, psychological environment data, social environment data, or economic environment data ([0072], “frailty can be … characterized by … decreases in physical, psychological and social functioning. Utilizing the deficit-accumulation clinical model, routinely collected items … such as medical history and functional abilities … can be used to compute a frailty index”);
and determining, using a trained psychosocial and economic health analysis machine learning model from the one or more modality-specific machine learning models and based on the psychosocial and economic health data, a psychosocial and economic health result indicating a deviation from a baseline pattern in the psychosocial and economic health data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”) associated with the user profile ([0093], “groups may be based on age, gender, or any … features”), wherein the trained psychosocial and economic health analysis machine learning model has been trained with psychosocial and economic health training data that includes at least one of physical environment data, psychological environment data, social environment data, or economic environment data associated with a plurality of user profiles ([0093], “groups may be based on age, gender, or any … features”) to infer the psychosocial and economic health result ([0064], “machine learning model can be used to predict a frailty measure for that subject”).
In regards to claim 15, Pascual-Leone discloses the following limitations with the exception of the underlined limitations.
A system comprising: one or more non-transitory computer-readable media storing instructions ([0010], “system includes … a computer readable storage medium having program instructions embodied therewith”);
and one or more processors configured to execute the instructions to perform operations comprising ([0136], “computer system … may include … one or more processors”): generating inputs associated with ([0054], “data are … from … user input”) a user profile for ([0093], “groups may be based on age, gender, or any … features”) one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs ([0032], “the present disclosure provides … methods … for machine-learning-assisted determination of … patient cognitive health”), wherein generating the inputs includes ([0054], “data are … from direct user input”): receiving, from a user device, at least one data entry generated, via the user device ([0044], “devices may be configured to record data regarding a user”), in association with the user profile ([0093], “groups may be based on age, gender, or any … features”);
performing, using a trained content machine learning model ([0033], “data … may be provided to a machine learning system”), content analysis on the at least one data entry to determine ([0048], “the interpretation of … data inputs is … relative to analysis”) historical user data, ([0100], “historical data of these interventions can be used”) the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry ([0113], “a combination of questions … may be used to assess mood”);
determining, using one or more trained context machine learning models ([0033], “data … may be provided to a machine learning system”), one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), handwriting data ([0125], “The subject is asked to trace a line with both their dominant and nondominant hand”), speech data ([0033], “the … assessments may include … speech elicitation tasks”), or psychosocial and economic health data associated with ([0072], “frailty can be … characterized by … decreases in physical, psychological and social functioning. Utilizing the deficit-accumulation clinical model, routinely collected items … such as medical history and functional abilities … can be used to compute a frailty index”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
and storing ([0035], “the collected information may … be … stored”) the historical user data ([0100], “historical data of these interventions can be used”) and the one or more baseline patterns in the modality data as the inputs associated with ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
in response to receiving a request to initiate a memory recall session in association with ([0059], “features … include … immediate recall; delayed recall; the time taken to recall each word; the accuracy of words recalled”) the user profile ([0093], “groups may be based on age, gender, or any … features”), providing at least the historical user data from the stored inputs to ([0100], “historical data of these interventions can be used”) a trained interactive-generating machine learning model for processing ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”);
determining, using the trained interactive-generating machine learning model ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”), one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives based on the historical user data ([0100], “historical data of these interventions can be used”), wherein the one or more profile-specific interactives comprise one or more questions that include at least a portion of the historical user data ([0033], “tasks and/or assessments may include … a lifestyle/health history questionnaire”);
transmitting, to the user device ([0044], “devices may be configured to record data regarding a user”), instructions to cause ([0009], “program instructions are executable by a processor”) the user device to display the one or more profile-specific interactives during the memory recall session ([0141], “Computer system … may … communicate with … a display”);
determining an interactive result based on the one or more responses ([0044], “data inputs may include … user interactions … responses to … stimulus”) to the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives and the historical user data ([0100], “historical data of these interventions can be used”);
providing at least the one or more baseline patterns in the modality data from the stored inputs to ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) one or more modality-specific machine learning models for processing with current modality data ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”);
determining, using the one or more modality-specific machine learning models ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”), a deviation from the one more baseline patterns in the modality data based on the current modality data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”);
determining a neurocognitive result based on the interactive result ([0044], “the system may combine … features with specific medical information obtained from … the user (e.g., … neuropsychological tests) … the system may combine these features to gain additional insights as to the role … neurological, and psychological subsystems play in contributing to changes in brain health and disease development”) and the deviation ([0093], “data … using the … model to determine if … the new subject … deviates from normal neurological functioning”);
and causing the neurocognitive result to be displayed via a graphical user interface of the user device ([0141], “Computer system … may … communicate with … a keyboard, a pointing device, a display”).
Mazza discloses
interactives ([0034], “neurological … tests … may include interactive tests, such as games” Examiner notes that in claimed invention “at least one interactive includes one or more games” [0032].)
Pascual-Leone and Mazza are considered analogous to the claimed invention because they are in the field of cognitive health and neurological impairment detection systems. Therefore, it would have been obvious to a system comprising: one or more non-transitory computer-readable media storing instructions; and one or more processors configured to execute the instructions to perform operations comprising: generating inputs associated with one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: receiving, from a user device, at least one data entry generated, via the user device, in association with the user profile; performing, using a trained content machine learning model, content analysis on the at least one data entry to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry; determining, using one or more trained context machine learning models, one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data, handwriting data, speech data, or psychosocial and economic health data associated with the user profile; and storing the historical user data and the one or more baseline patterns in the modality data as the inputs associated with the user profile; in response to receiving a request to initiate a memory recall session in association with the user profile, providing at least the historical user data from the stored inputs to a trained interactive-generating machine learning model for processing; determining, using the trained interactive-generating machine learning model, one or more profile-specific based on the historical user data, wherein the one or more profile-specific comprise one or more questions that include at least a portion of the historical user data; transmitting, to the user device, instructions to cause the user device to display the one or more profile-specific during the memory recall session; determining an interactive result based on the one or more responses to the one or more profile-specific and the historical user data; providing at least the one or more baseline patterns in the modality data from the stored inputs to one or more modality-specific machine learning models for processing with current modality data; determining, using the one or more modality-specific machine learning models, a deviation from the one more baseline patterns in the modality data based on the current modality data; determining a neurocognitive result based on the interactive result and the deviation; and causing the neurocognitive result to be displayed via a graphical user interface of the user device, as disclosed by Pascual-Leone, interactives, as disclosed by Mazza, to provide interactive tests, such as games, for a system for generating indications of neurological impairment. One skilled in the art would understand and recognize the value of the addition of neurological tests, interactive tests, and games to improve a system that generates indications of neurological impairment.
In regards to claim 16, Pascual-Leone discloses
wherein the one or more of the named entity, the mood, the topic, the summary, or the data entry metadata are extracted by applying natural language processing (NLP) analysis to the at least one data entry ([0081], “the … model may include an index across all of the features along with metadata associated with all of the correlations … the metadata may include machine learning models” Examiner notes that machine learning models use natural language processing.), wherein the data entry metadata includes at least one of user data, activity data, event data, location data, date data, time data, season data, or memory-type data ([0081], “the metadata may include statistical models, such as Bayesian models of … probability distributions” Examiner notes that statistical models may use activity, event, location, date, time, season, and memory-type data.).
In regards to claim 18, Pascual-Leone discloses
wherein determining the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives comprises: determining one or more target responses based on the historical user data ([0047], “data inputs may include … requiring the patient to … respond to … stimulus … historically captured during day to day activity”);
and determining the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives based on the one or more target responses ([0047], “data from … health devices that can record … responses … may be provided as input”).
In regards to claim 19, Pascual-Leone discloses
wherein the operations further comprise: determining a neurocognitive decline risk based on the neurocognitive result and at least a portion of the historical user data ([0043], “labels such as Alzheimer’s, Parkinson’s, … cognitive impairment … can be assigned to samples … machine learning models such as linear regression, deep learning, random forests … may be used to produce a prediction model for that clinical label” Examiner notes that Alzheimer’s and Parkinson’s are labels associated with neurocognitive decline.);
and causing the neurocognitive decline risk to be displayed via the graphical user interface of at least one of the user device or a medical provider device ([0106], “when displaying the model output, … the clinician-generated clusters may be provided to the user”).
In regards to claim 20, Pascual-Leone discloses the following limitations with the exception of the underlined limitations.
One or more non-transitory computer-readable media comprising instructions ([0010], “system includes … a computer readable storage medium having program instructions embodied therewith”);
that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising ([0136], “computer system … may include … one or more processors”): generating inputs associated with ([0054], “data are … from … user input”) a user profile for ([0093], “groups may be based on age, gender, or any … features”) one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs ([0032], “the present disclosure provides … methods … for machine-learning-assisted determination of … patient cognitive health”), wherein generating the inputs includes ([0054], “data are … from direct user input”): receiving, from a user device, at least one data entry generated, via the user device ([0044], “devices may be configured to record data regarding a user”), in association with the user profile ([0093], “groups may be based on age, gender, or any … features”);
performing, using a trained content machine learning model ([0033], “data … may be provided to a machine learning system”), content analysis on the at least one data entry to determine ([0048], “the interpretation of … data inputs is … relative to analysis”) historical user data, ([0100], “historical data of these interventions can be used”) the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry ([0113], “a combination of questions … may be used to assess mood”);
determining, using one or more trained context machine learning models ([0033], “data … may be provided to a machine learning system”), one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), handwriting data ([0125], “The subject is asked to trace a line with both their dominant and nondominant hand”), speech data ([0033], “the … assessments may include … speech elicitation tasks”), or psychosocial and economic health data associated with ([0072], “frailty can be … characterized by … decreases in physical, psychological and social functioning. Utilizing the deficit-accumulation clinical model, routinely collected items … such as medical history and functional abilities … can be used to compute a frailty index”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
and storing ([0035], “the collected information may … be … stored”) the historical user data ([0100], “historical data of these interventions can be used”) and the one or more baseline patterns in the modality data as the inputs associated with ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) the user profile ([0093], “groups may be based on age, gender, or any … features”);
in response to receiving a request to initiate a memory recall session in association with ([0059], “features … include … immediate recall; delayed recall; the time taken to recall each word; the accuracy of words recalled”) the user profile ([0093], “groups may be based on age, gender, or any … features”), providing at least the historical user data from the stored inputs to ([0100], “historical data of these interventions can be used”) a trained interactive-generating machine learning model for processing ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”);
determining, using the trained interactive-generating machine learning model ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”), one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives based on the historical user data ([0100], “historical data of these interventions can be used”), wherein the one or more profile-specific interactives comprise one or more questions that include at least a portion of the historical user data ([0033], “tasks and/or assessments may include … a lifestyle/health history questionnaire”);
transmitting, to the user device ([0044], “devices may be configured to record data regarding a user”), instructions to cause ([0009], “program instructions are executable by a processor”) the user device to display the one or more profile-specific interactives during the memory recall session ([0141], “Computer system … may … communicate with … a display”);
determining an interactive result based on the one or more responses ([0044], “data inputs may include … user interactions … responses to … stimulus”) to the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) interactives and the historical user data ([0100], “historical data of these interventions can be used”);
providing at least the one or more baseline patterns in the modality data from the stored inputs to ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) one or more modality-specific machine learning models for processing with current modality data ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”);
determining, using the one or more modality-specific machine learning models ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”), a deviation from the one more baseline patterns in the modality data based on the current modality data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”);
determining a neurocognitive result based on the interactive result ([0044], “the system may combine … features with specific medical information obtained from … the user (e.g., … neuropsychological tests) … the system may combine these features to gain additional insights as to the role … neurological, and psychological subsystems play in contributing to changes in brain health and disease development”) and the deviation ([0093], “data … using the … model to determine if … the new subject … deviates from normal neurological functioning”);
and causing the neurocognitive result to be displayed via a graphical user interface of the user device ([0141], “Computer system … may … communicate with … a keyboard, a pointing device, a display”).
Mazza discloses
interactives ([0034], “neurological … tests … may include interactive tests, such as games” Examiner notes that in claimed invention “at least one interactive includes one or more games” [0032].)
Pascual-Leone and Mazza are considered analogous to the claimed invention because they are in the field of cognitive health and neurological impairment detection systems. Therefore, it would have been obvious to one or more non-transitory computer-readable media comprising instructions; that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising: generating inputs associated with one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: receiving, from a user device, at least one data entry generated, via the user device, in association with the user profile; performing, using a trained content machine learning model, content analysis on the at least one data entry to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry; determining, using one or more trained context machine learning models, one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data, handwriting data, speech data, or psychosocial and economic health data associated with the user profile; and storing the historical user data and the one or more baseline patterns in the modality data as the inputs associated with the user profile; in response to receiving a request to initiate a memory recall session in association with the user profile, providing at least the historical user data from the stored inputs to a trained interactive-generating machine learning model for processing; determining, using the trained interactive-generating machine learning model, one or more profile-specific based on the historical user data, wherein the one or more profile-specific comprise one or more questions that include at least a portion of the historical user data; transmitting, to the user device, instructions to cause the user device to display the one or more profile-specific during the memory recall session; determining an interactive result based on the one or more responses to the one or more profile-specific and the historical user data; providing at least the one or more baseline patterns in the modality data from the stored inputs to one or more modality-specific machine learning models for processing with current modality data; determining, using the one or more modality-specific machine learning models, a deviation from the one more baseline patterns in the modality data based on the current modality data; determining a neurocognitive result based on the interactive result and the deviation; and causing the neurocognitive result to be displayed via a graphical user interface of the user device, as disclosed by Pascual-Leone, interactives, as disclosed by Mazza, to provide interactive tests, such as games, for a system for generating indications of neurological impairment. One skilled in the art would understand and recognize the value of the addition of neurological tests, interactive tests, and games to improve a system that generates indications of neurological impairment.
In regards to claim 21, Pascual-Leone discloses
wherein the at least one data entry is a data entry guided by an interactive virtual agent executing on the user device ([0086], “the ‘digital twin’ ... can serve several purposes including but not limited to: 1. Using the patient data model states and variables as input to predicting disease conditions; 2. Using patient data model states as the inputs to an optimization algorithm for recommending interventions; 3. Using the patient data model states and some assessment of their value as an objective function in reinforcement learning for recommending interventions; 4. Using the patient data model to predict or detect effects of an intervention such as drug administration when only limited data modalities for measurement are available” Examiner notes that a digital twin can function as a virtual agent.), wherein the interactive virtual agent is configured to generate and display ([0141], “Computer system … may … communicate with … a display”) one or more guiding questions ([0033], “tasks and/or assessments may include … a lifestyle/health history questionnaire”).
Response to Arguments
Applicant's arguments filed June 23, 2026 have been fully considered but they are not persuasive. Claims 1-16 and 18-21 remain pending in this application. With respect to Section 101 rejections, Applicant submits “the claims are not directed to an abstract idea because the claims as a whole integrate the alleged abstract idea into a practical application under Prong Two of Revised Step 2A” (See REPLY TO FINAL OFFICE ACTION, REMARKS, II. Section 101 Rejections, page 16, paragraph 4) and “the claims nonetheless transform the nature of the claim into a patent-eligible application under Step 2B, and thus qualify as eligible subject matter” (See REPLY TO FINAL OFFICE ACTION, REMARKS, II. Section 101 Rejections page 24, paragraph 1). Examiner acknowledges Applicant’s remarks.
Regarding the 101 rejection, in prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer processor (independent claims 1, 15, and 20 and dependent claims 2-14, 16, 18-19, and 21) is equivalent to adding the words “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer. Similarly, the limitations of a computer processor (independent claims 1, 15, and 20 dependent claims 2-14, 16, 18-19, and 21) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-14, 16, 18-19, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1, 15, and 20 and dependent claims 2-14, 16, 18-19, and 21 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself. Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2-14, 16, 18-19, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1-16 and 18-21 are not eligible subject matter under 35 USC 101.
With respect to section 103 rejections, Applicant submits that “Pascual-Leone and Mazza, individually or in combination, do not teach or suggest at least, ‘generating, by one or more processors, inputs associated with a user profile for one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs, wherein generating the inputs includes: … performing, by the one or more processors and using a trained content machine
learning model, content analysis on the at least one data entry [generated, via the user device, in association with the user profile] to determine historical user data, the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry,’ and/or, in response to receiving a request to initiate a memory recall session in association with the user profile, ‘determining, by the one or more processors and using the trained interactive-generating machine learning model, one or more profile-specific interactives based on the historical user data, wherein the one or more profile-specific interactives comprise one or more questions that include at least a portion of the historical user data.’ as recited in independent claim 1” (See REPLY TO NON-FINAL OFFICE ACTION, REMARKS, III. Section 103 Rejections, page 27, paragraph 1). Examiner acknowledges Applicant’s remarks. Regarding claim 1, Pascual-Leone discloses a computer-implemented method comprising: generating, by one or more processors ([0136], “computer system … may include … one or more processors”), inputs associated with ([0054], “data are … from … user input”) a user profile for ([0093], “groups may be based on age, gender, or any … features”) one or more machine learning models configured to evaluate a neurocognitive ability based on the inputs ([0032], “the present disclosure provides … methods … for machine-learning-assisted determination of … patient cognitive health”), wherein generating the inputs includes ([0054], “data are … from direct user input”): receiving, by the one or more processors ([0136], “computer system … may include … one or more processors”) and from a user device, at least one data entry generated, via the user device ([0044], “devices may be configured to record data regarding a user”), in association with the user profile ([0093], “groups may be based on age, gender, or any … features”); performing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using a trained content machine learning model ([0033], “data … may be provided to a machine learning system”), content analysis on the at least one data entry to determine ([0048], “the interpretation of … data inputs is … relative to analysis”) historical user data, ([0100], “historical data of these interventions can be used”) the content analysis including extracting, as the historical user data, one or more of a named entity, a mood, a topic, a summary, or data entry metadata from the at least one data entry ([0113], “a combination of questions … may be used to assess mood”); determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using one or more trained context machine learning models ([0033], “data … may be provided to a machine learning system”), one or more baseline patterns in modality data tracked by the user device, the modality data including one or more of facial expression data ([0130], “testing captures full face video recordings which will be kept … at the trial site”), handwriting data ([0125], “The subject is asked to trace a line with both their dominant and nondominant hand”), speech data ([0033], “the … assessments may include … speech elicitation tasks”), or psychosocial and economic health data associated with ([0072], “frailty can be … characterized by … decreases in physical, psychological and social functioning. Utilizing the deficit-accumulation clinical model, routinely collected items … such as medical history and functional abilities … can be used to compute a frailty index”) the user profile ([0093], “groups may be based on age, gender, or any … features”); and storing ([0035], “the collected information may … be … stored”), by the one or more processors ([0136], “The … computer system … may include … one or more processors”), the historical user data ([0100], “historical data of these interventions can be used”) and the one or more baseline patterns in the modality data as the inputs associated with ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) the user profile ([0093], “groups may be based on age, gender, or any … features”); in response to receiving a request to initiate a memory recall session in association with ([0059], “features … include … immediate recall; delayed recall; the time taken to recall each word; the accuracy of words recalled”) the user profile ([0093], “groups may be based on age, gender, or any … features”), providing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), at least the historical user data from the stored inputs to ([0100], “historical data of these interventions can be used”) a trained interactive-generating machine learning model for processing ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”); determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using the trained interactive-generating machine learning model ([0034], “features may be provided to other pre-trained machine learning algorithms trained for other tasks”), one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) based on the historical user data ([0100], “historical data of these interventions can be used”), wherein the one or more profile-specific comprise one or more questions that include at least a portion of the historical user data ([0033], “tasks and/or assessments may include … a lifestyle/health history questionnaire”); transmitting, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and to the user device ([0044], “devices may be configured to record data regarding a user”), instructions to cause ([0009], “program instructions are executable by a processor”) the user device to display the one or more profile-specific during the memory recall session ([0141], “Computer system … may … communicate with … a display”); determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), an interactive result based on the one or more responses ([0044], “data inputs may include … user interactions … responses to … stimulus”) to the one or more profile-specific ([0093], “groups may be based on age, gender, or any … features”) and the historical user data ([0100], “historical data of these interventions can be used”); providing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), at least the one or more baseline patterns in the modality data from the stored inputs to ([0124], “subject is instructed to connect a set of circles as quickly as possible according to a pre-established pattern”) one or more modality-specific machine learning models for processing with current modality data ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”); determining, by the one or more processors ([0136], “The … computer system … may include … one or more processors”) and using the one or more modality-specific machine learning models ([0064], “machine learning models are trained on the multimodal input data to predict the frailty target variable”), a deviation from the one more baseline patterns in the modality data based on the current modality data ([0093], “one or more machine learning models may … analyze … data and determine where values deviate from the norm”); determining, by the one or more processors, a neurocognitive result based on the interactive result ([0044], “the system may combine … features with specific medical information obtained from … the user (e.g., … neuropsychological tests) … the system may combine these features to gain additional insights as to the role … neurological, and psychological subsystems play in contributing to changes in brain health and disease development”) and the deviation ([0093], “data … using the … model to determine if … the new subject … deviates from normal neurological functioning”); and causing, by the one or more processors ([0136], “The … computer system … may include … one or more processors”), the neurocognitive result to be displayed via a graphical user interface of the user device ([0141], “Computer system … may … communicate with … a keyboard, a pointing device, a display”) and Mazza discloses interactives ([0034], “neurological … tests … may include interactive tests, such as games” Examiner notes that in claimed invention “at least one interactive includes one or more games” [0032].)
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claim 1 and dependent claims 2-14 and 21, as obvious by Pascual-Leone in view of Mazza, are maintained. Independent claims 15 and 20 set forth similar elements as claim 1. Therefore, the rejections of independent claims 15 and 20 and dependent claims 16 and 18-19, as obvious by Pascual-Leone in view of Mazza, are maintained.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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