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 .
Status of the Claims
The status of the claims as of the response filed 1/2/2026 is as follows: Claims 1-6, 8, and 12-13 remain cancelled. Claims 7 and 9-11 are currently amended and have been considered below.
Response to Amendment
Claim Objection
Claim 7 has been amended to sufficiently clarify the objectionable language such that the corresponding objections are withdrawn.
Rejection Under 35 USC 112(b)
Claim 7 has been amended to sufficiently clarify the indefinite elements and limitations such that the corresponding 35 USC 112(b) rejections are withdrawn.
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 103
The amendments made to the claims introduce limitations that are not fully addressed in the previous office action, and thus the corresponding 35 USC 103 rejections are withdrawn.
Response to Arguments
Rejection Under 35 USC 101
On page 7 of the response filed 1/2/2026 Applicant argues that the amended claims “are not (and cannot be) directed to ‘organizing human activity’ because the features recited therein are not directed to ‘fundamental economic principles,’ commercial or legal interactives [sic],’ or ‘managing personal behavior’” (emphasis original). Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that underlying functions of the claims recite a certain method of organizing human activity in the form of managing personal behavior, interactions, or relationships between people; namely outputting a work schedule generated based on predicted outpatient-related numerical values, obtaining data on a target to be predicted, obtaining past data, calculating pattern data by performing simple correlation and multiplication calculations within the past data, predicting a number of future outpatients of various types based on the pattern data using a model, temporally classifying the predicted patient numbers, and determine a required workforce by converting the predicted values into staffing requirements so that medical staff resource allocation is controlled/assigned based on predicted patient volumes. Such operations fall within the purview of a hospital administrator or other human actor analyzing past data for trends to make workplace demand and staffing determinations for the future, which falls squarely within the category of “certain methods of organizing human activity” such as managing personal behavior, relationships, or interactions between people. These steps also clearly describe resource supply and demand calculations necessary for running an outpatient clinic offering various medical services, such that Examiner respectfully submits that they also fit into the other “certain methods of organizing human activity” groupings of “fundamental economic practices or principles” (e.g. calculating supply and demand for a resource) as well as “commercial or legal interactions” (e.g. making business calculations about patient demand and corresponding staffing requirements so that a commercial medical practice may optimize scheduling operations). Accordingly, Examiner maintains that the claims recite an abstract idea.
On page 7 of the response Applicant argues that the claims cannot fall into the certain methods of organizing human activity category of abstract idea because they include hardware elements that amount to “a particular apparatus that comprises specific structure to carry out particular, detailed features.” Applicant’s arguments are fully considered, but are not persuasive. A claim may still be directed to an abstract idea even if it includes additional elements (such as computer hardware) beyond the abstract idea itself; such additional elements are evaluated in Step 2A – Prong 2 and Step 2B of the eligibility analysis. Examiner maintains that the underlying functions of the instant claims fall into the abstract idea category of certain methods of organizing human activity, as explained in the preceding paragraph.
On page 8 of the response, Applicant argues that the claims are directed to a practical application because “they provide improvement(s) to the medical technological environment” by “generat[ing] specific, structured pattern data and achiev[ing] technical improvements to the hospital operating system, such as automatically generating operating schedules for examination rooms, blood collection rooms, and CT/MRI equipment.” Applicant submits that the performance of “‘complex computations (derived variable generation, dynamic model selection) that are not humanly feasible’ during data processing… provides an improvement to computer functionality” and that the invention “physically improves the performance of a machine learning model by generating new ‘derived features’ through multiplication operations between input variables.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that generating pattern data by multiplying numerical data to derive new input features and calculate correlations between inputs does appear to be humanly feasible; human beings are capable of performing simple mathematical operations like multiplication of numbers to derive other numbers and determine statistical correlations between data types to identify trends or patterns in the data and use such identified trends/patterns as a basis for future predictions. Thus the introduction of an input derived via multiplication as a predictive model input does not appear to improve the physical performance of a machine learning model itself, and is instead considered part of the abstract idea itself. Human beings are also capable of generating operating schedules for examination rooms, blood collection rooms, and CT/MRI equipment based on projected patient demands on such resources. Accordingly, the alleged improvements to technology that Applicant highlights are instead part of the abstract idea itself. Because these functions are part of the abstract idea itself, they cannot provide a practical application or “significantly more” than the abstract idea and thus do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”).
On pages 8-9 of the response Applicant asserts that “the claimed features are beyond those recognized in the art as being well-understood, routine, or conventional.” Applicant’s arguments are fully considered, but are not persuasive. Applicant has not provided any specific evidence that the combination of additional elements is unconventional, and Examiner respectfully disagrees with Applicant’s assertion. The various computer-executable units executed by a processor device and a specifically machine-learned model to implement the functions of the invention merely serve as tools with which to digitize and/or automate the otherwise-abstract functions such that they amount to instructions to “apply” the exception with generic computing components and thus do not confer eligibility (see MPEP 2106.05(f)). Further, the use of an input/output unit to display the work schedule merely digitizes the output/sharing of data that could be otherwise be determined and disseminated to others by a human actor, thereby also amounting to instructions to “apply” the abstract idea at a high level. Despite Applicant’s assertions, there is no indication that any particular technology is improved, nor that any of the additional elements are unconventional either alone or in combination because the only additional elements of the claim are recited at a high level of generality (see specifically paras. [0087]-[0089] of Applicant’s specification, noting “A device and an element describe in embodiments may be implemented using one or more general-purpose or special purpose computers, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions”; as well as [0058], noting “the trained model may be generated by using various methodologies of machine learning”; and [0049], noting “the output device may include a device such as a display for displaying a communication session of an application…. the input/output unit 140 may be a means for interfacing with a device in which input and output functions are integrated such as a touchscreen”) such that one of ordinary skill in the art would understand that any processor-based device executing computer code (such as a variety of known machine learning techniques) and including a known type of output unit could implement the invention. Instead, Applicant appears to be offering alleged improvements to business practices such as making workplace demand predictions and corresponding staffing/scheduling determinations; Examiner notes that improvements to existing business practices and abstract ideas themselves do not amount to improvements in computer technology or other technological fields and thus do not confer eligibility. Further, per MPEP 2106.05(f)(2), “‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept.” Applicant has not provided any evidence that the implementation of the abstract idea with computing components provides any technical improvements beyond the improved speed or efficiency inherent with applying clinical workplace demand prediction and scheduling operations in a computing environment.
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 7 and 9-11.
Rejection Under 35 USC 103
Applicant’s arguments on pages 10-11 of the response with respect to alleged deficiencies of Ma and Coulter have been considered and are found persuasive. Examiner agrees that Ma and Coulter fail to teach or suggest, either alone or in combination, the claims as presently amended. The 35 USC 103 rejections for claims 7 and 9-11 are withdrawn.
Claim Objections
Claims 7 and 9-11 are objected to because of the following informalities: claim 7 recites “(ii) generating a composite input variable by multiplying the number of outpatient clinic units by a number of outpatients per outpatient clinic unit.” The “number of outpatient clinic units” has only been previously recited as the “number of outpatient clinic units in past” in prior limitations, and Applicant is advised that all nomenclature should be standardized, e.g. by amending limitation (ii) to recite “generating a composite input variable by multiplying the number of outpatient clinic units in past by a number of outpatients per outpatient clinic unit” for clarity. Appropriate correction is required. Claims 9-11 are also objected to on this basis because they inherit the objectionable language due to their dependence on claim 7.
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 7 and 9-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 7 and 9-11 are directed to an apparatus (i.e. a machine) such that each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claim 7 recites steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 7 recites:
An outpatient prediction apparatus comprising:
a memory that stores computer executable instructions;
a processor that executes the computer executable instructions stored in the memory to operate a prediction engine and a work schedule generator implemented on the outpatient prediction apparatus; and
an input/output unit configured to output, on a service screen, a work schedule automatically generated based on predicted outpatient-related numerical values,
wherein the computer executable instructions include:
user input obtaining instructions configured to obtain data on a target to be predicted,
past data obtaining instructions configured to obtain past data comprising at least one of a number of outpatient clinic units in past, a number of outpatients in past, a number of blood-collecting patients in past, a number of computed tomography (CT)/magnetic resonance imaging (MRI) tests in past, a number of outpatients per outpatient clinic unit in past, a number of blood-collecting patients per outpatient number in past, and a number of CT/MRI tests per outpatient number in past,
pattern data generating instructions configured to calculate pattern data by: (i) calculating a correlation coefficient between the number of outpatients in past and the number of outpatient clinic units in past, and (ii) generating a composite input variable by multiplying the number of outpatient clinic units by a number of outpatients per outpatient clinic unit,
future data predicting instructions configured to predict a number of outpatients in future based on the pattern data, and a number of blood-collecting patients in future, and a number of CT/MRI tests in future based on number of outpatients in future by using a machine-learned model, the future data predicting instructions being configured to execute the machine-learned model stored in the memory and to generate resource-specific prediction values by applying the pattern data to the machine-learned model,
the future data predicting instructions are further configured to predict the number of outpatients in future by using pattern data comprising at least one of a number of reserved patients in future, a number of outpatient clinic units in past/future, a number of outpatients per outpatient clinic unit in past, a value obtained by multiplying a number of outpatient clinic units in past/future by a number of outpatients per outpatient clinic unit in past, a moving average of a number of outpatients in past, and a moving average of a number of reserved patients in past,
the future data predicting instructions are further configured to calculate the number of blood-collecting patients in future by using the number of outpatients in future and the number of blood-collecting patients per outpatient number, and calculating the number of CT/MRI tests in future by using the number of outpatients in future and the number of CT/MRI tests per outpatient number, and
the future data predicting instructions are further configured to automatically classify the number of future outpatients, the number of future blood collection patients, and the number of future CT/MRI tests by at least one of daily, weekly, and monthly, determine a future required workforce based on the classified number of future outpatients, the number of future blood collection patients, and the number of future CT/MRI tests, and generate a work schedule for the target data using the required workforce by converting predicted values into staffing requirements,
whereby the outpatient prediction apparatus performs automated control of medical resource allocation based on predicted patient volumes.
But for the recitation of generic computer components like a memory and processor executing various computer executable instructions and use of a machine-learned model, the italicized functions, when considered as a whole, describe a clinical workplace demand prediction and scheduling operation that could be achieved by a human actor such as a clinician or other medical professional managing their personal behavior and/or interactions with others. For example, a hospital administrator could obtain and analyze historical patient volume data with a mathematical model to identify various types of patterns/ trends in the data and make various predictions about a number of future outpatients, blood-collecting patients, and CT/MRI scans expected to occur at a future time based on the past patterns/trends, for example by calculating correlations between historic data types and performing simple multiplication operations on numerical inputs. The administrator could then use the predictions to make daily, weekly, and/or monthly staffing requirements to meet the expected patient/service demands and generate an appropriate work schedule, which they could display on a communal work board or other written form of communication such that medical resource allocation (i.e. staffing needs) is assigned based on predicted patient volumes. Accordingly, claim 7 recites an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 9-11 inherit the limitations that recite an abstract idea from their dependence on claim 7, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 9-11 recite additional limitations that further describe the abstract idea identified in the independent claims. Specifically, claim 9 specifies predicting a number of outpatients in the future by using one of various models utilizing different types of input data and pattern data, which an administrator would be capable of achieving by utilizing models like decision trees, checklists, equations, etc. that utilize one or more of those types of data as inputs along with historical data patterns to make patient demand predictions. Claim 10 specifies making the predictions by selecting and using a model with the lowest mean absolute percentage error from among a variety of models utilizing different types of input data, which an administrator would be capable of achieving by selecting and utilizing a ‘best’ model evaluating one of those data input types according to MAPE metrics. Claim 11 specifies that at least one of the models has been trained through training data having past data as an input and an actual number of outpatients as an output, the training including optimizing parameters by using at least one of a boosting-based regression model, particle swarm optimization, a meta-heuristic algorithm, or Bayesian optimization. Training models with training data via optimizing parameters could be accomplished by an administrator or other human actor generating or fitting models (e.g. decision trees, checklists, equations, etc.) based on analysis of known past data correlated with actual number of outpatients as an output and iteratively optimizing the parameters or coefficients of the models so that they accurately predict the outputs based on the inputs. Specifying that parameters are optimized via at least one of the listed techniques describes the use of mathematical concepts to perform parameter adjustment, which falls into the abstract idea category of “mathematical concepts.”
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claim 7 does not include additional elements that integrate the abstract idea into a practical application. The additional elements of claim 7 include a memory that stores computer executable instructions, a processor that executes the computer executable instructions stored in the memory, an input/output unit used to output a work schedule, the computer executable instructions including user input obtaining instructions, past data obtaining instructions, pattern data generating instructions, and future data predicting instructions that are each configured to perform various steps of the invention, as well as use of a machine-learned model stored in the memory to perform the predicting functions. These additional elements, when considered in the context of the claim as a whole, merely serve to automate operations that could otherwise be achieved by human actors (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components (see MPEP 2106.05(f)). For example, a hospital administrator could obtain and analyze past patient/services demand data from a clinic to identify patterns/trends via mathematical modeling and calculation techniques and make predictions about future patient volume and corresponding staff scheduling determinations. The use of computerized instructions stored in a memory and executed on a processor as well as a machine-learned model stored in the memory to achieve such functions thus merely digitizes and/or automates these otherwise-abstract steps and does not provide integration into a practical application. Further, the use of an input/output unit to output the work schedule merely digitizes the output/sharing of data that could be otherwise be determined and disseminated to others by a human actor, thereby also amounting to instructions to “apply” the abstract idea at a high-level. Accordingly, claim 7 as a whole is directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 9-11 is also not integrated into a practical application under a similar analysis as above. The functions of claims 9-10 are performed with the same additional elements introduced in the independent claims, without introducing any new additional elements of their own, and accordingly also amount to mere instructions to apply the abstract idea on these same additional elements. Claim 11 specifies that the at least one model is machine-learned via various mathematical techniques, which again merely invokes high-level computerized machine learning techniques as a tool with which to digitize/automate the otherwise-abstract concept of using a model to make clinical demand predictions such that it also amounts to instructions to “apply” the exception as similarly indicated for claim 7.
Accordingly, the additional elements of claims 7 and 9-11 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 7 and 9-11 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of various computer executable instructions stored in a memory and executed by a processor as well as a machine-learned model stored in the memory for performing the obtaining, calculating, generating, predicting, classifying, determining, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. Similarly, the use of an input/output unit to output the work schedule on a service screen also merely digitizes the otherwise-abstract sharing of staffing schedules and amounts to mere instructions to apply the abstract idea in a computing environment. As evidence of the generic nature of the above recited additional elements, Examiner notes Figs. 1-2 and paras. [0040]-[0045] & [0087]-[0089] of Applicant’s specification, where the computing elements of the invention are described in a highly generic manner with many known examples such as a smart phone, desktop, or general-purpose computer such that one of ordinary skill in the art would understand that any processor-based computer device with input/output capabilities may be utilized to implement the invention. Further, machine learning methods are described at a high level and with various examples of known machine learning techniques in para. [0058] such that one of ordinary skill in the art would understand that many known types of machine learning could be utilized to implement the at least one prediction model.
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation, input/output unit, and machine learning models in combination is to digitize and/or automate a clinical workplace demand prediction and scheduling operation that could otherwise be achieved as a certain method of organizing human activity. Thus, when considered as a whole and in combination, claims 7 and 9-11 are not patent eligible.
Subject Matter Free from Prior Art
The following is a statement of reasons for the indication of subject matter free from prior art:
The prior art of record fails to expressly teach or suggest, either alone or in combination, each and every feature of the independent claim. In particular, the prior art fails to teach calculating pattern data by (i) calculating a correlation coefficient between the number of outpatients in past and the number of clinic units in past, and (ii) generating a composite input variable by multiplying the number of outpatient clinic units in past by a number of outpatients per outpatient clinic unit for use as input to a machine-learned model for predicting outpatient demands of different types. Upon completion of an updated prior art search, Examiner submits that the closest related art includes:
- Ma et al. (CN 111755106 A), disclosing methods for using predictive models to forecast the number of patients in different departments of a clinical facility, including multiplying a predicted total patient number by the ratio of patients in each department, but failing to explicitly disclose generating a composite input variable by multiplying the number of outpatient clinic units in past by a number of outpatients per outpatient clinic unit, nor the departments being related to blood collection patients and CT/MRI tests or the staffing and scheduling features of the claims;
- Coulter et al. (US 20100305966 A1), disclosing a clinical outpatient prediction system that estimates department-specific patient volume for the specific departments of blood collection/phlebotomy and radiology imaging including MRI and CAT scans, as well as converts predicted patient volume into a future required workforce for the purpose of generating and displaying a work schedule for a given time period using the required workforce, but failing to explicitly disclose the generating pattern data in the two specific manners claimed;
- Boyle et al. (US 20120232926 A1), Wang et al. (US 20210287781 A1), Tolbert (US 20200273562 A1), Levin et al. (US 20140136458 A1), Schuck (US 20170185721 A1), Chen et al. (WO 2022134650 A1), Zhang et al. (Reference U on the PTO-892 mailed 1/31/2025), DeLurgio et al. (Reference V on the PTO-892 mailed 1/31/2025), Klute et al. (Reference W on the PTO-892 mailed 1/31/2025), and Luo et al. (Reference U on the accompanying PTO-892), disclosing various automated systems for predicting patient demand/volume in clinical facilities and/or allocating/scheduling corresponding appropriate clinical staffing resources to meet the predicted demand, but failing to explicitly disclose the generating pattern data in the two specific manners claimed.
Though many aspects of the independent claims are disclosed in the prior art, it would not have been obvious to one of ordinary skill in the art to combine the disparate features into the invention of the instant claims. In particular, it would not have been obvious to calculate pattern data specifically by (i) calculating a correlation coefficient between the number of outpatients in past and the number of clinic units in past and (ii) generating a composite input variable by multiplying the number of outpatient clinic units in past by a number of outpatients per outpatient clinic unit for use as input to a machine-learned model for predicting outpatient demands of different types. Accordingly, the prior art, either alone or in combination, does not disclose or render obvious all the features of independent claim 7 and it is found to recite subject matter free from prior art, as are claims 9-11 depending therefrom.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684