Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
This communication is in response to the amendment filed 05/26/2026. Claims 1, 5-6, 9, 12-13, 16, 18-19 have been amended. Claims 1-20 have been presented for examination.
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 1-20 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 9 is drawn to a non-transitory computer-readable medium comprising computer-executable instructions which is within the four statutory categories (i.e., manufacture). Claim 16 is drawn to a system which is within the four statutory categories (i.e., machine).
Independent claim 16 (which is representative of independent claims 1, 9) recites… accessing first assessment data for a first patient, the first assessment data generated by a clinician associated with the first patient in a clinical setting; accessing characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting; generating a set of machine learning features based on performing feature extraction on the first assessment data and the characteristics of the first user; generating a first content selection, from a library of content, based on processing at least a subset of the set of machine learning features using a…model, comprising: identifying a subset of content by filtering the library of content based on the first assessment data; evaluating the subset of content using the…model; and refraining from evaluating at least one content item, from the library of content, using the…model in response to determining that the at least one content item is not included in the subset of content; and initiating delivery of the first content selection to a first user setting.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly (i.e., instructions on how to care for a patient) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps, as indicated supra. That is, other than reciting generic computer components (discussed infra), the claim amounts to managing personal behavior or relationships or interactions between people following rules or instructions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Claim 1 recites additional elements (i.e., a machine learning model). Claim 9 recites additional elements (i.e., A non-transitory computer-readable medium comprising computer-executable instructions; one or more processors of a processing system; a machine learning model). Claim 16 recites additional elements (i.e., A system, comprising: a memory comprising computer-executable instructions; and one or more processors; a machine learning model). Looking to the specifications, a computing system having a non-transitory computer-readable medium comprising computer-executable instructions, one or more processors, a memory comprising computer-executable instructions is described at a high level of generality (¶ 0039; ¶ 0189-0190; ¶ 0207), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a machine learning model” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The 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. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computing system having a non-transitory computer-readable medium comprising computer-executable instructions, one or more processors, a memory comprising computer-executable instructions) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “a machine learning model” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-8, 10-15, 17-20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-7, 10-14, 17-20 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 8, 15 further recites the additional elements of “processing natural language text in the first assessment data using one or more natural language processing (NLP) operations, the one or more NLP operations comprising at least one of: (i) keyword identification, or (ii) sentiment analysis,” which is described at a high level of generality, such that using NLP to compare data amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent App. Pub. No. US 2022/0344050 A1 (hereinafter referred to as "MCNAIR").
Regarding (currently amended) claim 1, MCNAIR teaches a method, comprising:
accessing first assessment data for a first patient, the first assessment data generated by a clinician associated with the first patient in a clinical setting (MCNAIR: ¶ 0056, i.e., “system 2130 may receive patient data 2111 in the form of a natural language narrative, such as a physician's note”; ¶ 0061, i.e., “patient data 2111 can include lab results, real time or near real time information such as data provided by a physician, including information based on observation or a patient's explanation”);
accessing characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting (MCNAIR: ¶ 0051, i.e., “input may be received, including patient information 2110 (described below) and one or more sets, thresholds, or ranges of variables, from parameters 2120 (described below), such as for example blood pressure, blood oxygen, temperature, or other variables used in the process for detecting sepsis described herein. Such variable sets, threshold(s), or range(s) may be received from one or more health care providers or from an agent, and, in some embodiments, may be specified in one or more content tables 2124 (described below). In some embodiments, the received variable set(s), threshold(s), or ranges may differ based on differing opinions, strategies, or condition-detection theories of the health care providers”; ¶ 0064);
generating a set of machine learning features based on performing feature extraction on the first assessment data (MCNAIR: ¶ 0056, i.e., “invoke a data-extraction agent, from solvers library 2122, to extract discretized data from the note”; ¶ 0080, i.e., “using a data-extraction agent for extracting discrete data items from a physician's note written in natural language”) and the characteristics of the first user (MCNAIR: ¶ 0052, i.e., “for each of the variable set(s), range(s) or threshold(s), an agent 2135 may be invoked for determining likelihood of a condition, including conditions or other clinical decision support event(s), or for monitoring the patient for likelihood of the condition or event. In some embodiments the agents work in parallel, such that each agent operates with different set, range, or threshold values, thereby resulting in multiple evaluations for the likelihood of the condition or event being carried out. In some embodiments, the results of the evaluations by the agents are compared to determine which set(s), range(s), or threshold(s) performs better for determining likelihood of the condition or event. Further, in some embodiments, multi-agent system 2130 learns the set(s), range(s), threshold(s) or other parameters 2120 and patient information 2110 that are more likely to result in an accurate diagnosis or detection of the condition or event. In some embodiments, the particular set(s), range(s), threshold(s), or other parameters 2120 which yield a more accurate determination of likelihood of the condition or event are weighted, biased, or otherwise noted for future use in evaluating a patient for risk of the condition or even”);
generating a first content selection, from a library of content, based on processing at least a subset of the set of machine learning features using a machine learning model (MCNAIR: ¶ 0056, i.e., “System 2130 may then use the discretized data, or coded concepts, and content tables 2124 to instantiate and apply another solver agent, such as a type of healthcare agent, from solvers library 2122 to determine a patient's condition and recommended treatments”; ¶ 0070, i.e., “the agent solvers implement a clinical condition program”; ¶ 0072; ¶ 0076-0077; ¶ 0080), comprising:
identifying a subset of content by filtering the library of content based on the first assessment data (MCNAIR: ¶ 0063, i.e., “a high probability of detection of a condition or event, can be determined based on variables V1 and V2 alone…and that variables V3-V5 are unnecessary”; ¶ 0076, i.e., “a content table may specify parameters relating to diabetes including what factors in patient information indicate that the patient is in hypoglycemia, what factors in patient information indicate that the patient is in hyperglycemia, contra-indications, treatments such as drugs and drug dosages that should be administered, or additional testing that should be ordered. In another example, content tables specify the set(s), range(s), and/or threshold(s) of variables for detecting likelihood of a condition, such as sepsis. In some embodiments, rows of a content table correspond to different sets, ranges, or thresholds of variables”);
evaluating the subset of content using the machine learning model (MCNAIR: ¶ 0063, i.e., “the range of variable V1 should be RX-RY for a certain patient type and within a specified time window, and the threshold of variable V2 should be T1”; ¶ 0076, i.e., “a first agent can perform its analysis using content specified in a first row A”); and
refraining from evaluating at least one content item, from the library of content, using the machine learning model in response to determining that the at least one content item is not included in the subset of content (MCNAIR: ¶ 0063, i.e., “variables V3-V5 are unnecessary”; ¶ 0076, i.e., “rows of a content table correspond to different sets, ranges, or thresholds of variables”); and
initiating delivery of the first content selection to the first user (MCNAIR: ¶ 0059; ¶ 0076, i.e., “the results of the row that correspond to the most effective analysis may be provided to the health care provider…as the result of the determination for whether the patient has the condition”).
Regarding (original) claim 2, MCNAIR teaches the method of claim 1, further comprising:
accessing outcome data subsequent to delivery of the first content selection to the first user (MCNAIR: ¶ 0112, i.e., “a condition program for determining a patient's likelihood of CHF readmission might include COPD and diabetes as risk factors…suppose a newly related concept, such as a particular clinical variable, is discovered to be statistically associated with diabetes via the process described in FIG. 3A. This concept related to diabetes (which may have its machine-determined relation audited or vetted, in some embodiments) may become a new risk factor for diabetes…Where the condition program is currently in use, such as at a particular client site”); and
generating an updated machine learning model based on updating one or more parameters of the machine learning model based on the outcome data (MCNAIR: ¶ 0112, i.e., “As new risk factors are identified, such as by identifying concepts related to a targeted condition or concept, as described in connection to service 3100 of FIG. 3A, the condition program can be updated…A condition program for diabetes, or a content table 2124 (FIG. 1C) used by an agent dynamically assembling a condition program that includes diabetes, can be updated to include the new risk factor. Where the condition program is currently in use, such as at a particular client site, the program may be dynamically updated to include the new risk factor”).
Regarding (original) claim 3, MCNAIR teaches the method of claim 2, wherein the outcome data comprises at least one of:
(i) a proportion of the first content selection that the first user consumed,
(ii) a number of users that have consumed the first content selection,
(iii) a rating indicated by the first user for the first content selection, or
(iv) second assessment data for the first patient (MCNAIR: ¶ 0095; ¶ 0112, i.e., “a newly related concept, such as a particular clinical variable, is discovered to be statistically associated with diabetes via the process described in FIG. 3A”).
Regarding (original) claim 4, MCNAIR teaches the method of claim 2, further comprising:
accessing second assessment data for the first patient (MCNAIR: ¶ 0095, i.e., “Mapping service 3100 receives data from one or more data sources 3110”; ¶ 0112); and
generating a second content selection based on the second assessment data and the updated machine learning model (MCNAIR: ¶ 0112, i.e., “The resulting update may cause a particular patient's risk score or probability for the condition to change or may result in prompting the caregiver to provide patient information related to the new risk factor”).
Regarding (currently amended) claim 5, MCNAIR teaches the method of claim 1, wherein generating the first content selection further comprises:
identifying the subset of content, from the library of content, based on evaluating the first assessment data using a set of content rules comprising at least one condition-based rule, at least one timeline-based rule, and at least one characteristic- based rule (MCNAIR: ¶ 0056; ¶ 0080; ¶ 0112, i.e., “a content table may specify parameters relating to diabetes including what factors in patient information indicate that the patient is in hypoglycemia, what factors in patient information indicate that the patient is in hyperglycemia, contra-indications, treatments such as drugs and drug dosages that should be administered, or additional testing that should be ordered”);
evaluating the subset of content using the machine learning model to predict outcome data for each element of content in the subset of content (MCNAIR: ¶ 0112, i.e., “a first agent can perform its analysis using content specified in a first row A, and a second agent working in parallel (or the first agent at a later time) can perform its analysis using content from a row B…with each agent using a different set of parameters specified in one row, the results of the row that correspond to the most effective analysis may be provided to the health care provider or otherwise published to the outside world as the result of the determination for whether the patient has the condition, even though in fact there may be multiple separate results from the different analyses”); and
selecting a first content element from the subset of content based on the outcome data (MCNAIR: ¶ 0112, i.e., “rows of a content table correspond to different sets, ranges, or thresholds of variables…the results of the row that correspond to the most effective analysis may be provided to the health care provider or otherwise published to the outside world as the result of the determination for whether the patient has the condition, even though in fact there may be multiple separate results from the different analyses”).
Regarding (currently amended) claim 6, MCNAIR teaches the method of claim 1, wherein the characteristics of the first user comprise a preferred format for guidance content (MCNAIR: ¶ 0051, i.e., “the received variable set(s), threshold(s), or ranges may differ based on differing opinions, strategies, or condition-detection theories of the health care providers”; ¶ 0064).
Regarding (original) claim 7, MCNAIR teaches the method of claim 1, wherein the first assessment data comprises at least one of:
(i) natural language text authored by the clinician to describe at least one of the first patient or the first user in the clinical setting (MCNAIR: ¶ 0056, i.e., “patient data 2111 in the form of a natural language narrative, such as a physician's note”; ¶ 0061; ¶ 0080), or
(ii) one or more tags selected by the clinician to describe at least one of the first patient or the first user in the clinical setting.
Regarding (original) claim 8, MCNAIR teaches the method of claim 7, wherein performing feature extraction on the first assessment data comprises processing natural language text in the first assessment data using one or more natural language processing (NLP) operations, the one or more NLP operations comprising at least one of: (i) keyword identification (MCNAIR: ¶ 0056, i.e., “invoke a data-extraction agent, from solvers library 2122, to extract discretized data from the note”; ¶ 0080, i.e., “when patient information indicates sepsis”), or (ii) sentiment analysis.
Regarding (currently amended) claim 9, MCNAIR teaches a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation (MCNAIR: ¶ 0022; ¶ 0044) comprising:
accessing characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting (MCNAIR: ¶ 0051, i.e., “input may be received, including patient information 2110 (described below) and one or more sets, thresholds, or ranges of variables, from parameters 2120 (described below), such as for example blood pressure, blood oxygen, temperature, or other variables used in the process for detecting sepsis described herein. Such variable sets, threshold(s), or range(s) may be received from one or more health care providers or from an agent, and, in some embodiments, may be specified in one or more content tables 2124 (described below). In some embodiments, the received variable set(s), threshold(s), or ranges may differ based on differing opinions, strategies, or condition-detection theories of the health care providers”; ¶ 0064);
generating a set of machine learning features based on performing feature extraction on the first assessment data (MCNAIR: ¶ 0056, i.e., “invoke a data-extraction agent, from solvers library 2122, to extract discretized data from the note”; ¶ 0080, i.e., “using a data-extraction agent for extracting discrete data items from a physician's note written in natural language”) and the characteristics of the first user (MCNAIR: ¶ 0052, i.e., “for each of the variable set(s), range(s) or threshold(s), an agent 2135 may be invoked for determining likelihood of a condition, including conditions or other clinical decision support event(s), or for monitoring the patient for likelihood of the condition or event. In some embodiments the agents work in parallel, such that each agent operates with different set, range, or threshold values, thereby resulting in multiple evaluations for the likelihood of the condition or event being carried out. In some embodiments, the results of the evaluations by the agents are compared to determine which set(s), range(s), or threshold(s) performs better for determining likelihood of the condition or event. Further, in some embodiments, multi-agent system 2130 learns the set(s), range(s), threshold(s) or other parameters 2120 and patient information 2110 that are more likely to result in an accurate diagnosis or detection of the condition or event. In some embodiments, the particular set(s), range(s), threshold(s), or other parameters 2120 which yield a more accurate determination of likelihood of the condition or event are weighted, biased, or otherwise noted for future use in evaluating a patient for risk of the condition or even”);
generating a first content selection, from a library of content, based on processing at least a subset of the set of machine learning features using a machine learning model (MCNAIR: ¶ 0056, i.e., “System 2130 may then use the discretized data, or coded concepts, and content tables 2124 to instantiate and apply another solver agent, such as a type of healthcare agent, from solvers library 2122 to determine a patient's condition and recommended treatments”; ¶ 0070, i.e., “the agent solvers implement a clinical condition program”; ¶ 0072; ¶ 0076-0077; ¶ 0080), comprising:
identifying a subset of content by filtering the library of content based on the first assessment data (MCNAIR: ¶ 0063, i.e., “a high probability of detection of a condition or event, can be determined based on variables V1 and V2 alone…and that variables V3-V5 are unnecessary”; ¶ 0076, i.e., “a content table may specify parameters relating to diabetes including what factors in patient information indicate that the patient is in hypoglycemia, what factors in patient information indicate that the patient is in hyperglycemia, contra-indications, treatments such as drugs and drug dosages that should be administered, or additional testing that should be ordered. In another example, content tables specify the set(s), range(s), and/or threshold(s) of variables for detecting likelihood of a condition, such as sepsis. In some embodiments, rows of a content table correspond to different sets, ranges, or thresholds of variables”);
evaluating the subset of content using the machine learning model (MCNAIR: ¶ 0063, i.e., “the range of variable V1 should be RX-RY for a certain patient type and within a specified time window, and the threshold of variable V2 should be T1”; ¶ 0076, i.e., “a first agent can perform its analysis using content specified in a first row A”); and
refraining from evaluating at least one content item, from the library of content, using the machine learning model in response to determining that the at least one content item is not included in the subset of content (MCNAIR: ¶ 0063, i.e., “variables V3-V5 are unnecessary”; ¶ 0076, i.e., “rows of a content table correspond to different sets, ranges, or thresholds of variables”); and
initiating delivery of the first content selection to the first user (MCNAIR: ¶ 0059; ¶ 0076, i.e., “the results of the row that correspond to the most effective analysis may be provided to the health care provider…as the result of the determination for whether the patient has the condition”).
Regarding (original) claim 10, claim 10 recites substantially similar limitations analogous to those already addressed in claim 2, and thus, claim 10 is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Regarding (original) claim 11, claim 11 recites substantially similar limitations analogous to those already addressed in claim 3, and thus, claim 11 is similarly analyzed and rejected in a manner consistent with the rejection of claim 3.
Regarding (currently amended) claim 12, claim 12 recites substantially similar limitations analogous to those already addressed in claim 5, and thus, claim 12 is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Regarding (currently amended) claim 13, claim 13 recites substantially similar limitations analogous to those already addressed in claim 6, and thus, claim 13 is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Regarding (original) claim 14, claim 14 recites substantially similar limitations analogous to those already addressed in claim 7, and thus, claim 14 is similarly analyzed and rejected in a manner consistent with the rejection of claim 7.
Regarding (original) claim 15, claim 15 recites substantially similar limitations analogous to those already addressed in claim 8, and thus, claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of claim 8.
Regarding (currently amended) claim 16, claim 16 recites substantially similar limitations analogous to those already addressed in claim 9, and thus, claim 16 is similarly analyzed and rejected in a manner consistent with the rejection of claim 9.
Regarding (original) claim 17, claim 17 recites substantially similar limitations analogous to those already addressed in claim 2, and thus, claim 17 is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Regarding (currently amended) claim 18, claim 18 recites substantially similar limitations analogous to those already addressed in claim 5, and thus, claim 18 is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Regarding (currently amended) claim 19, claim 19 recites substantially similar limitations analogous to those already addressed in claim 6, and thus, claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Regarding (original) claim 20, claim 20 recites substantially similar limitations analogous to those already addressed in claim 7, and thus, claim 20 is similarly analyzed and rejected in a manner consistent with the rejection of claim 7.
Response to Arguments
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/26/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“the present claims, which relate to techniques to efficiently use machine learning to deliver content, do not recite or relate to any method of organizing human activity… the present claims are explicitly directed to using machine learning to generate content selections and initiate delivery of the selected content. There is simply nothing in the claims that relates to controlling, managing, or organizing any sort of human activity”;
“the specification explains that evaluating content candidates using machine learning may be computationally expensive, and therefore provides a two-stage operation where the content is first filtered to a relatively smaller subset, and this smaller subset is evaluated. This is a clear technical improvement to the evaluation process itself. Further, the present claim amendments clearly reflect these improvements… the present claims at least reflect a "close call" such that unpatentability cannot be established by a preponderance of the evidence, as required… the present rejections similarly "evaluate [the] claims at a high level of generality" without adequate explanation.”
Regarding the 102 rejections, the cited prior art reference(s) fails to teach the amended claim limitations because “The cited reference is simply entirely unrelated to these aspects of the claims” and “there is a clear difference between evaluating patient data to determine whether the patient has a condition, as McNair describes, and evaluating "characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting" to generate and deliver content selections.”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 101 rejections,
“the present claims, which relate to techniques to efficiently use machine learning to deliver content, do not recite or relate to any method of organizing human activity… the present claims are explicitly directed to using machine learning to generate content selections and initiate delivery of the selected content. There is simply nothing in the claims that relates to controlling, managing, or organizing any sort of human activity”:
It is respectfully submitted that Applicant argues “the present claims, which relate to techniques to efficiently use machine learning to deliver content, do not recite or relate to any method of organizing human activity… the present claims are explicitly directed to using machine learning to generate content selections and initiate delivery of the selected content. There is simply nothing in the claims that relates to controlling, managing, or organizing any sort of human activity.” However, Applicant fails to specify how “techniques to efficiently use machine learning to deliver content, do not recite or relate to any method of organizing human activity” and “There is simply nothing in the claims that relates to controlling, managing, or organizing any sort of human activity.” Regardless, per broadest reasonable interpretation of the claim in light of the specification, the claims of the present invention encompass the activity of (to paraphrase) rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly (i.e., instructions on how to care for a patient) in the manner described in the identified abstract idea, supra, which covers the sub-grouping of managing personal behavior or relationships or interactions between people in the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. This is an abstract idea. That the steps are performed on a well-known, general purpose computer (i.e., a computing system having a non-transitory computer-readable medium comprising computer-executable instructions, one or more processors, a memory comprising computer-executable instructions) does not remove the invention from being directed to an abstract idea. Furthermore, the claim limitations to which Applicant seems to refer (i.e., “machine learning”) is not interpreted as part of the abstract idea, but an additional element to be interpreted in Step 2A, Prong Two, which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claims are directed to an abstract idea.
“the specification explains that evaluating content candidates using machine learning may be computationally expensive, and therefore provides a two-stage operation where the content is first filtered to a relatively smaller subset, and this smaller subset is evaluated. This is a clear technical improvement to the evaluation process itself. Further, the present claim amendments clearly reflect these improvements… the present claims at least reflect a "close call" such that unpatentability cannot be established by a preponderance of the evidence, as required… the present rejections similarly "evaluate [the] claims at a high level of generality" without adequate explanation”:
Applicant argues “the specification explains that evaluating content candidates using machine learning may be computationally expensive, and therefore provides a two-stage operation where the content is first filtered to a relatively smaller subset, and this smaller subset is evaluated. This is a clear technical improvement to the evaluation process itself. Further, the present claim amendments clearly reflect these improvements.” However, “evaluating content candidates” does not address any technical problems to any specific devices, technology (i.e., machine learning), or computers for that matter, and thus, the claims do not provide a technical solution. For example, the computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea of rules or instructions to collect data, analyze the collected data, and output relevant data based on the analysis accordingly (i.e., instructions on how to care for a patient). However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement. Furthermore, the “machine learning” to which Applicant refers as providing the alleged improvements is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “the present claims at least reflect a "close call" such that unpatentability cannot be established by a preponderance of the evidence, as required.” However, Applicant fails to specify how “the present claims at least reflect a "close call" such that unpatentability cannot be established by a preponderance of the evidence, as required.” Regardless, Examiner made the rejection because “it is more likely than not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101” and “unpatentability [has been] established by a preponderance of the evidence,” as stated in Office Action dated 02/26/2026 and above.
Applicant argues “the present rejections similarly "evaluate [the] claims at a high level of generality" without adequate explanation.” However, Applicant fails to specify the “claims” to which Applicant refer and how “the present rejections similarly "evaluate [the] claims at a high level of generality" without adequate explanation.” Regardless, as stated in Office Action dated 02/26/2026 and previously above, the “machine learning” to which Applicant seems to refer as providing the alleged improvements is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination.
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1-20, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
In response to Applicant’s argument that (b) regarding the 102 rejections, the cited prior art reference(s) fails to teach the amended claim limitations because “The cited reference is simply entirely unrelated to these aspects of the claims” and “there is a clear difference between evaluating patient data to determine whether the patient has a condition, as McNair describes, and evaluating "characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting" to generate and deliver content selections”:
It is respectfully submitted that Applicant fails to specify how “The cited reference is simply entirely unrelated to these aspects of the claims” and what is the “clear difference between evaluating patient data to determine whether the patient has a condition, as McNair describes, and evaluating "characteristics of a first user, distinct from the clinician and the first patient, that cares for the first patient in the clinical setting" to generate and deliver content selections.”
Examiner has updated passages and citations to the amended claims and applied new passages and citations accordingly, at the present time, as addressed in the above Office Action, and the analogous independent claims and the remaining dependent claims have been taught by the applied/recited passages and citations, as addressed in the above Office Action.
Thus, the cited prior art references teach the aforementioned amended claim limitations.
Conclusion
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 extension fee 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 date of this final action.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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