DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. The Amendment filed on January 23, 2026, has been entered. The examiner acknowledges the amendments to claims 1, 11, and 17.
Rejections under 35 U.S.C. § 101: Applicant argues that training in a streaming manner with a first and second machine learning model presents significantly more than an abstract idea. The Examiner notes that additional processing is not in and of itself a practical application and does not change the underlying abstract ideas presented in the claims. Additional argument in favor of using a trained ML model for reducing computational expense provides anecdotal evidence at best. The Examiner notes that since streaming data is generally a continuous process, a claim of reduced computational expense while processing continuously appears contradictory. Arguments suggest that streaming data may be more efficient in processing large sets of data, but the Examiner notes that the claims do not cite large datasets as an focus of the invention, and questions how large the set of caregivers under evaluation would be for each instantiation of the invention, and what data might be collected for processing. This continues to cast doubt on the utility of streaming as part of a practical application.
An additional argument leads one to believe that identifying a replacement for the caregiver is responsive to the electronic system placing a hiring announcement, suggesting a possible link between the invention and a hiring system. The Examiner notes that any connection to a hiring system is not disclosed in the claims and a hiring system is not an additional element.
It remains apparent that data collection and processing for purposes of improving caregiver retention and providing information and insight to managers and facilities is a goal of the invention. It is also apparent that the processer serves to more rapidly implement the abstract ideas noted. It does not appear that the invention goes beyond the stage of applying software on a computer with output to a human. Innovative application of additional elements is not apparent, nor is any improvement to a processor. The Examiner concludes that claim 1 does not recite more than the abstract idea an as a result, the rejections under 35 U.S.C § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s amendments to the independent claims overcome the prior art and were not overcome with additional search. In view of this, rejections to the independent claims 1, 11, and 17 will be withdrawn.
Claim Rejections – 35 U.S.C. § 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 1-2, 4-8, 10-15, 17-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-2, 4-8, 10-15, 17-22 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-2, 4-8, 10-15, 17-22 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-2, 4-8, 10-15, 17-22 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of analyzing the risk of a specific human behavior (the decision to continue to work for an employer) by collecting and processing data based on mental processes (observation, evaluation, judgement, opinion,) and methods of organizing human activity (fundamental economic principles- mitigating risk, managing personal behavior, following rules or instructions).
Claim 1 discloses a method, comprising: A method, comprising:
pre-processing, historical data into feature vectors comprising data for different characteristics related to a caregiver; (managing personal behavior, interactions between people, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
preparing at a first point in time, a first model and a second model, based on the feature vectors; (following rules or instructions, observation, evaluation, judgement, opinion),
predicting, an impact of one or more caregiver tasks characteristics on continued employment of the caregiver with a care provider, (managing personal behavior, interactions between people, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk), comprising:
determining, a plurality of intermediate prediction scores relating to the characteristics for the caregiver using the one or more to determine different intermediate prediction scores that each relate to the different characteristics for the caregiver; (managing personal behavior, interactions between people, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
determining, a retention prediction for the caregiver using the plurality of intermediate prediction scores, comprising:
predicting a most impactful intermediate prediction score, among the plurality of intermediate prediction scores, to continued employment of the caregiver, (managing personal behavior, interactions between people, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk), and
generating, at a second point in time that is different from the first point in time, using the second model, the retention prediction by providing the plurality of intermediate prediction scores to the second model trained to determine the retention prediction based on intermediate prediction scores; (following rules or instructions, observation, evaluation, judgement, opinion), and
based on the determined generated retention prediction and the predicted most
impactful intermediate prediction score, implementing one or more actions to improve treatment for a patient of the caregiver by at least one of: (i) adjusting employment hours of the caregiver, (ii) scheduling, tasks for the patient to a different caregiver, (iii) providing additional incentives for completing tasks for the patient, or (iv) identifying a replacement for the caregiver, wherein identifying the replacement for the caregiver is responsive placing a hiring announcement, (managing personal behavior, interactions between people, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk).
Additional limitations employ the method with intermediate prediction scores for a facility, a patient, caregiver performance, progress notes, or a task score, (managing personal behavior, interactions between people, observation, evaluation, judgement, opinion, mitigating risk - claim 2), determining intermediate prediction scores, (managing personal behavior, interactions between people, observation, evaluation, judgement, opinion – claim 4), predicting the likelihood of continuing employment for a period of time, one or more factors predicted to impact the likelihood of continuing employment, recommended actions to improve the likelihood the caregiver will continue employment with the employer, (managing personal behavior, interactions between people, observation, evaluation, judgement, opinion – claim 5), where retention prediction comprises all of the likelihood of continuing employment for a period of time, one or more factors predicted to impact the likelihood of continuing employment, recommended actions to improve the likelihood the caregiver will continue employment with the employer, (managing personal behavior, interactions between people, observation, evaluation, judgement, opinion – claim 6), calculating the likelihood of continuing employment for a period of time, one or more factors predicted to impact the likelihood of continuing employment, recommended actions to improve the likelihood the caregiver will continue employment with the employer, and updating the model using new caretaker characteristics, (managing personal behavior, interactions between people, following rules and instructions, observation, evaluation, judgement, opinion - claim7), identifying incompatibility between the caregiver and at least one of a patient, a healthcare facility, or a caregiver task, before completing the retention prediction, (managing personal behavior, interactions between people observation, evaluation, judgement, opinion – claim 8), and identifying data (information) prior to making the prediction, (managing personal behavior, interactions between people, observation, evaluation, judgement, opinion – claim 10), removing improperly formatted or duplicate data and preparing data for processing, (observation, evaluation, judgement, opinion - claim 21). Each of these claimed limitations employ organizing human activity, managing personal behavior, interactions between people, fundamental economic principles based on mitigating risk, or mental processes involving judgement, observation, evaluation and opinion.
Claims 11-20 and 22 recite similar abstract ideas as those identified with respect to claims 1-10 and 21.
Thus, the concepts set forth in claims 1-22 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-2, 4-8, 10-15, 17-22, recite additional limitations which are hardware or software elements such as a first machine learning (ML) model, a second ML model, an electronic system or healthcare facility relating to the caregiver, an apparatus, a memory, Natural Language Processing, a hardware processor communicatively coupled to the memory, a non-transitory computer-readable medium, and the ability to transmit an electronic alert, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-2, 4-8, 10-15, 17-22, are directed to abstract ideas.
Step 2B
Claims 1-2, 4-8, 10-15, 17-22, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-2, 4-8, 10-15, 17-22, that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Claims 1, 11, and 17, are not rejected by prior art under 35 U.S.C. § 103. Dependent claims 2, 4, 5-8, 10-15, 17-22 are not rejected because of their inherent dependency on claims
1, 11, and 17.
The closest prior art to the invention includes Yan (US 20190244152 A1) “Method of Using Machine Learning to Predict Problematic Actions Within an Organization”, Kobayashi, (US 20180018586 A1), “ Apparatus and Method for Managing Machine Learning,” and Lee, “Facilitators and Barriers Surrounding the Role of Administration in Employee Job Satisfaction in Long-Term Care Facilities: A Systematic Review,” Healthcare (Basel). 2020 Sep 24;8(4):360, None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding Claim 1, A method, comprising:
pre-processing, using a computing system in a streaming manner, historical data into feature vectors comprising data for different characteristics related to a caregiver; None of the prior art taught streaming inputs or the processing into feature vectors for training purposes.
training, at a first point in time when computational resources are available based on the feature vectors in a streaming manner, one or more first machine learning (ML)
a first model and a second model, Kobayashi teaches both first and second machine learning algorithms, wherein the computing system comprises the one or
more first ML models and the second ML model, wherein the one or more first ML models are different from the second ML model;
predicting, using the second ML model, Yan does predict actions based on employee related data an impact of one or more caregiver characteristics on continued employment of the caregiver with a care provider, a likelihood that the employee is to resign from the organization comprising:
determining, using the one or more first ML models, a plurality of intermediate prediction scores relating to the characteristics for the caregiver using the one or more first ML models trained to determine different intermediate prediction scores that each relate to the different characteristics for the caregiver;
determining, using the second ML model, a retention prediction for the caregiver using the plurality of intermediate prediction scores, comprising: None of the prior art specified the roles of two distinct machine learning models beyond being trained with different data sets.
predicting a most impactful intermediate prediction score, among the plurality of intermediate prediction scores, to continued employment of the caregiver, and
generating, at a second point in time that is different from the first point in time, Prior art does not teach the combined use of first and second models for retention prediction using intermediate prediction scores, using the second ML model, the retention prediction by providing the plurality of intermediate prediction scores to the second ML model trained to determine the retention prediction based on intermediate prediction scores; and
based on the generated retention prediction and the predicted most impactful intermediate prediction score, implementing one or more actions via an electronic system or a healthcare facility relating to the caregiver to improve treatment for a patient of the caregiver by at least one of: (i) adjusting employment hours of the caregiver, (ii) scheduling, by the electronic system, tasks for the patient to a different caregiver, (iii) providing additional incentives for completing tasks for the patient, or (iv) identifying a replacement for the caregiver, wherein identifying the replacement for the caregiver is responsive to the electronic system placing a hiring announcement.
These individually or in combination did not teach the complete scope of the claim. The dependent claims 2, 4-8, 10, 12-15, 18-22 are not rejected because of their inherent dependency on claims 1, 11 and 17.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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