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 01/23/2026. Claims 1, 6, 14 have been amended. Claims 1-20 have been presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting, by a computing system, an optimized treatment plan for a first resident using the trained one or more machine learning models, comprising: extracting a first plurality of resident attributes from resident data for the first resident; determining a first goal for a first condition of the first resident; generating a first achievability score based on processing the first goal and the first plurality of resident attributes using a first machine learning model; generating a first approach to achieve the first goal; generating a first efficacy score by processing the first goal, the first approach, and the first plurality of resident attributes using a second machine learning model, wherein the first efficacy score indicates an amount of time that is predicted to elapse prior to achieving the first goal; and generating a first predicted recovery score based on the first achievability score and the first efficacy score.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 6, 14, claims 1, 6, 14 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-5, 7-13, 15-20 incorporate the allowable features of originally numbered independent claims 1, 6, 14, through dependency, respectively.
However, the claims are still rejected under 101.
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 6 is drawn to a method which is within the four statutory categories (i.e., method). Claim 14 is drawn to a non-transitory computer-readable storage medium which is within the four statutory categories (i.e., manufacture).
Independent claim 1 recites receiving treatment data describing a treatment plan for a resident of a residential care facility; generating a first recovery score based on the treatment data, wherein the first recovery score indicates success of the treatment plan in treating the resident, wherein the first recovery score is inversely related to an amount of time that elapsed while the resident used the treatment plan; …; and deploying the trained…models; and in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting…an optimized treatment plan for a first resident using the trained one or more machine learning models, comprising: extracting a first plurality of resident attributes from resident data for the first resident; determining a first goal for a first condition of the first resident; generating a first achievability score based on processing the first goal and the first plurality of resident attributes using a first machine learning model; generating a first approach to achieve the first goal; generating a first efficacy score by processing the first goal, the first approach, and the first plurality of resident attributes using a second machine learning model, wherein the first efficacy score indicates an amount of time that is predicted to elapse prior to achieving the first goal; and generating a first predicted recovery score based on the first achievability score and the first efficacy score; and automatically implementing, by the computing system, the optimized treatment plan for the first resident, comprising allocating increased resources to an area where the optimized treatment plan will be provided to the first resident.
Independent claim 6 (which is representative of independent claim 14) recites receiving resident data describing a first condition of a first resident of a residential care facility; in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting… an optimized treatment plan for the first resident, comprising: extracting a first plurality of resident attributes, from the resident data, for the first resident; determining a first goal for the first condition; generating a first achievability score based on processing the first goal and the first plurality of resident attributes using a first machine learning model; generating a first approach to achieve the first goal; generating a first efficacy score by processing the first goal, the first approach, and the first plurality of resident attributes…, wherein the first efficacy score indicates an amount of time that is predicted to elapse prior to achieving the first goal; and generating a first predicted recovery score based on the first achievability score and the first efficacy score; and automatically implementing, by the computing system, the optimized treatment plan for the first resident, comprising allocating increased resources to an area where the optimized treatment plan will be provided to the first resident.
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. That is, other than reciting “one or more computer processors” (claim 14), the claim encompasses rules or instructions followed to collect and analyze patient data and output a treatment plan accordingly. 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.
Independent claim 1 further recites… training one or more machine learning models based on the first recovery score.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. For example, with regards to training machine learning models, the specification mentions: “the machine learning system 135 may process the historical data 105 for a given resident at a given time as input to the machine learning model 140, and compare the generated likelihood of recovery using the historical care plan 130 to a ground-truth (e.g., a recovery score indicating whether the resident actually recovered). The difference between the generated and actual recovery scores can be used to refine the weights of the machine learning model 140, and the model can be iteratively refined (e.g., using data from multiple residents and/or multiple points in time) to accurately evaluate care plans” (¶ 0044); “During training, this score can then be compared against a ground-truth associated with the selected exemplar (e.g., an indication as to whether the resident did, in fact, recover or otherwise reach the goal(s) using the care plan). In some embodiments, this comparison includes determining how much time elapsed between the start of the care plan and the eventual recovery (or successful reaching of the goal(s)). Based on this comparison, the parameters of the machine learning model can be updated” (¶ 0116). In light of the disclosure, the claim encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., one or more machine learning models). Claim 6 recites additional elements (i.e., a first machine learning model; a second machine learning model). Claim 14 recites additional elements (i.e., a non-transitory computer-readable storage medium comprising computer-readable program code…executed using one or more computer processors; a first machine learning model; a second machine learning model). Looking to the specifications, a computing device having a non-transitory computer-readable storage medium comprising computer-readable program code, one or more computer processors is described at a high level of generality (¶ 0096; ¶ 0209), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “machine learning models” are only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “generating a first achievability score” and “generating a first efficacy score” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). 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 device having a non-transitory computer-readable storage medium comprising computer-readable program code, one or more computer processors) 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, “machine learning models” are only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “generating a first achievability score” and “generating a first efficacy score” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). 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 2-5, 7-13, 15-20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-5, 7, 9-13, 15-20 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 further recites the additional elements of “wherein outputting the first approach comprises displaying the first approach and the first predicted recovery score on a-the graphical user interface,” which only invokes the graphical user interface merely as a tool in its ordinary capacity to perform an existing process (i.e., displaying data), which 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 claimed invention to a particular technological environment or field of use, 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 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.
Response to Arguments
Applicant's arguments filed 01/23/2026 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 01/23/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“the present claims cover methods and operations for generating treatment plans, but do not recite any "instructions" for a user to follow. Nearly any process (e.g., a method, which is a statutorily eligible category) could be broadly described as a sequence of steps to follow, yet a vast assortment of such processes remain eligible... some elements such as "training the neural network in a first stage using the first training set" from Example 39 do not recite a judicial exception at all… the present elements are significantly more similar to those in Example 39, and do not recite any judicial exceptions”;
“the system may "indicate which caregivers will be using new plans, which residents or portions of the facility have a large number of new plans, and the like" which can allow the system to "take a variety of actions, including reallocating resources (e.g., allocating increased resources and/or staff to areas where the new care plans will involve extra work)." [0158]. Applicant respectfully submits that this is a clear practical application, enabling improved resource allocation in specific physical areas… the system may "regenerate care plans [] and/or recovery scores [] during specified times (e.g., off-peak hours, such as overnight) to provide improved load balancing on the underlying computational systems." [0095]. For example, "rather than requiring caregivers to retrieve and review resident data for a facility each morning to determine if anything occurred overnight or the previous day that may require a new care plan," the system can "automatically identify such changes, and use the machine learning model(s) to regenerate care plans [] and recovery scores [] before the shift begins." Id. This can "transfer the computational burden, which may include both processing power of the storage repositories and access terminals, as well as bandwidth over one or more networks, to off-peak times, thereby reducing congestion on the system during ordinary (e.g., daytime) use and taking advantage of extra resources that are available during the non-peak (e.g., overnight) hours." Id.”
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 cover methods and operations for generating treatment plans, but do not recite any "instructions" for a user to follow. Nearly any process (e.g., a method, which is a statutorily eligible category) could be broadly described as a sequence of steps to follow, yet a vast assortment of such processes remain eligible... some elements such as "training the neural network in a first stage using the first training set" from Example 39 do not recite a judicial exception at all… the present elements are significantly more similar to those in Example 39, and do not recite any judicial exceptions”:
It is respectfully submitted that per broadest reasonable interpretation of the claim in light of the specification, the claims of the present invention which describe “generating treatment plans” to which Applicant seems to refer encompass the activity of (to paraphrase) rules or instructions followed to rules or instructions followed to collect and analyze patient data and output a treatment plan accordingly, 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. Put another way, the claimed invention amounts to a series of rules or steps that a user (i.e., doctor) would follow to analyze a patient’s data and determine an appropriate treatment plan for the patient’s recovery. This is an abstract idea. That the steps are performed on one or more well-known, general purpose computer (i.e., a computing device having a non-transitory computer-readable storage medium comprising computer-readable program code, one or more computer processors) does not remove the invention from being directed to an abstract idea.
Applicant argues “the present claims cover methods and operations for generating treatment plans, but do not recite any "instructions" for a user to follow. Nearly any process (e.g., a method, which is a statutorily eligible category) could be broadly described as a sequence of steps, yet a vast assortment of such processes remain eligible. Respectfully, Applicant submits that the Office's characterization of the present claims as mere "steps" or "instructions" is excessively broad, and plainly does not align with current case law and guidance.” However, the claims do not need to recite “"instructions" for a user to follow,” as Applicant now argues, as long as the claim recites an abstract idea, which it does, but for the recitation of generic computer components, as explained previously in Office Action dated 10/23/2025 and above. Per MPEP § 2106.04(a), Examiner has “(1) [identified] the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) [determined] whether the identified limitations(s) fall within at least one of the groupings of abstract ideas” in Office Action dated 10/23/2025 and above, which is what is required to “conclude that the claim recites an abstract idea in Step 2A Prong One.”
Applicant argues “the present claims, at most, "involve" an exception, and do not "recite" such an exception.” However, Applicant fails to specify how “the present claims, at most, "involve" an exception, and do not "recite" such an exception.” Regardless, independent claim 1 recites receiving treatment data describing a treatment plan for a resident of a residential care facility; generating a first recovery score based on the treatment data, wherein the first recovery score indicates success of the treatment plan in treating the resident, wherein the first recovery score is inversely related to an amount of time that elapsed while the resident used the treatment plan; …; and deploying the trained…models; and in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting…an optimized treatment plan for a first resident using the trained one or more machine learning models, comprising: extracting a first plurality of resident attributes from resident data for the first resident; determining a first goal for a first condition of the first resident; generating a first achievability score based on processing the first goal and the first plurality of resident attributes using a first machine learning model; generating a first approach to achieve the first goal; generating a first efficacy score by processing the first goal, the first approach, and the first plurality of resident attributes using a second machine learning model, wherein the first efficacy score indicates an amount of time that is predicted to elapse prior to achieving the first goal; and generating a first predicted recovery score based on the first achievability score and the first efficacy score; and automatically implementing, by the computing system, the optimized treatment plan for the first resident, comprising allocating increased resources to an area where the optimized treatment plan will be provided to the first resident. Independent claim 6 (which is representative of independent claim 14) recites receiving resident data describing a first condition of a first resident of a residential care facility; in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting… an optimized treatment plan for the first resident, comprising: extracting a first plurality of resident attributes, from the resident data, for the first resident; determining a first goal for the first condition; generating a first achievability score based on processing the first goal and the first plurality of resident attributes using a first machine learning model; generating a first approach to achieve the first goal; generating a first efficacy score by processing the first goal, the first approach, and the first plurality of resident attributes…, wherein the first efficacy score indicates an amount of time that is predicted to elapse prior to achieving the first goal; and generating a first predicted recovery score based on the first achievability score and the first efficacy score; and automatically implementing, by the computing system, the optimized treatment plan for the first resident, comprising allocating increased resources to an area where the optimized treatment plan will be provided to the first resident. Under its broadest reasonable interpretation, the limitations noted above, as drafted, encompasses rules or instructions followed to collect and analyze patient data and output a treatment plan accordingly, which 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. Independent claim 1 further recites… training a machine learning model to predict resident recovery based on the first recovery score. Under the broadest reasonable interpretation, the limitations noted above, as drafted, encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships, but for the recitation of generic computer components.
Applicant argues “the present elements are significantly more similar to those in Example 39, and do not recite any judicial exceptions.” However, Applicant fails to specify how “the present elements are significantly more similar to those in Example 39” than claim 2 of Example 47. Regardless the claim limitations of the present invention are different from the claim limitations of Example 39. Even if the claim limitations of the present invention are similar to that of the claims found eligible (and they are not similar), the claimed inventions are fundamentally different in scope and examples should be interpreted based on the asserted fact patterns; as previously stated above, other fact patterns may have different eligibility outcomes, as is the case with the claims of the present invention. Unlike the claims found eligible in Example 39, the claims of the present invention recite “training one or more machine learning models based on the first recovery score,” which is described in the specification as “During training, this score can then be compared against a ground-truth associated with the selected exemplar (e.g., an indication as to whether the resident did, in fact, recover or otherwise reach the goal(s) using the care plan). In some embodiments, this comparison includes determining how much time elapsed between the start of the care plan and the eventual recovery (or successful reaching of the goal(s)). Based on this comparison, the parameters of the machine learning model can be updated” (¶ 0116), which encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers the sub-grouping of mathematical relationships in the “Mathematical Concepts” grouping of abstract ideas, as stated previously in Office Action dated 10/23/2025 and above.
Thus, the claims recite an abstract idea.
“the system may "indicate which caregivers will be using new plans, which residents or portions of the facility have a large number of new plans, and the like" which can allow the system to "take a variety of actions, including reallocating resources (e.g., allocating increased resources and/or staff to areas where the new care plans will involve extra work)." [0158]. Applicant respectfully submits that this is a clear practical application, enabling improved resource allocation in specific physical areas… the system may "regenerate care plans [] and/or recovery scores [] during specified times (e.g., off-peak hours, such as overnight) to provide improved load balancing on the underlying computational systems." [0095]. For example, "rather than requiring caregivers to retrieve and review resident data for a facility each morning to determine if anything occurred overnight or the previous day that may require a new care plan," the system can "automatically identify such changes, and use the machine learning model(s) to regenerate care plans [] and recovery scores [] before the shift begins." Id. This can "transfer the computational burden, which may include both processing power of the storage repositories and access terminals, as well as bandwidth over one or more networks, to off-peak times, thereby reducing congestion on the system during ordinary (e.g., daytime) use and taking advantage of extra resources that are available during the non-peak (e.g., overnight) hours." Id”:
Applicant argues “improved resource allocation in specific physical areas.” However, “improved resource allocation in specific physical areas” only addresses administrative problems, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Even if the claims improved resource allocation in specific physical areas, any alleged benefits of the invention are at best, an improvement to the abstract idea. However, an improved abstract idea is still an abstract idea.
Applicant argues “provide improved load balancing on the underlying computational systems” and “transfer the computational burden, which may include both processing power of the storage repositories and access terminals, as well as bandwidth over one or more networks, to off-peak times, thereby reducing congestion on the system during ordinary (e.g., daytime) use and taking advantage of extra resources that are available during the non-peak (e.g., overnight) hours.” However, the claim limitations to which Applicant seem to refer as reflecting the alleged improvements (i.e., “in response to determining that a current time is within a specified off-peak time when additional computational resources are available as compared to a specified peak time, automatically selecting… an optimized treatment plan for the first resident”) are interpreted as rules or instructions followed to collect and analyze patient data and output a treatment plan accordingly (i.e. when), which is the abstract idea, and not additional elements to be interpreted in Step 2A, Prong Two. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea. However, an improved abstract idea is still an abstract idea.
Furthermore, 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. Applicant’s claims do not recite the invention of improvements to computer functionality, technology, or any other technological field, but the use of generic computer components (i.e., a computing device having a non-transitory computer-readable storage medium comprising computer-readable program code, one or more computer processors) to perform the abstract idea, but for the recitation of generic computer components. Examiner cannot find and Appellant has not identified any problem caused by the technological environment to which the claims are confined (i.e., a well-known, general purpose computer). While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement to computer technology, a physical improvement to the computer, or any other technical improvement. See MPEP § 2106.04(d)(1) and 2106.05(a).
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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683