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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, i.e. Application Nos. 17/912,887, PCT/CA2021/051033, and 63/055620, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Specifically, these prior-filed applications fail to provide adequate support for at least the following limitations:
“implementing a scheduling model, said scheduling model being a trained AI model configured to receive said at least one LOS probability, said waitlist probability, and current occupancy data of said healthcare facility as inputs, and said scheduling model being configured to output said at least one schedule element…wherein said components of said schedule element comprise: a type of a medical procedure; a date for said medical procedure; a time for said medical procedure; a location for said medical procedure; a list of resources required for said medical procedure; and a list of invitees for said medical procedure” as recited in claim 1;
“wherein the scheduling model predicts a cumulative distribution of occupancy at said healthcare facility over a predetermined time period” as recited in claim 2;
“wherein said scheduling model minimizes said cumulative distribution when determining said values of said components” as recited in claim 3;
“wherein, when said conflict is identified, said server feeds back said at least one LOS probability and said waitlist probability of said specific patient to said scheduling model, along with input data for said specific entry, to thereby produce at least one of a revised schedule element and a revised specific entry” as recited in claim 6;
“wherein said scheduling model is trained on population-wide data and on historical data from said healthcare facility” as recited in claim 10;
“wherein at least one of said LOS model and said waitlist model is a trained AI model” as recited in claim 12;
“wherein both of said LOS model and said waitlist model are trained AI models” as recited in claim 13;
transmitting said schedule element to at least one person on said list of invitees “before entering said schedule element in said calendaring system” as recited in claim 16;
“wherein said cumulative distribution of occupancy at said healthcare facility is a predicted distribution of occupancy of a specific subset of said healthcare facility” as recited in claim 17;
“wherein said specific subunit of said healthcare facility is an Intensive Care Unit (ICU)” as recited in claim 18;
“wherein said cumulative distribution of occupancy at said healthcare facility comprises a predicted distribution of occupancy in an Intensive Care Unit (ICU) and a predicted distribution of occupancy in said healthcare facility” as recited in claim 19;
training said scheduling model to prioritize medical procedures such that high-risk patients are scheduled for near-term medical procedures as recited in claim 20.
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. Claims 1-20 are drawn to a system, which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of:
receiving patient data, said patient data relating to said specific patient;
implementing a length of stay (LOS) model, said LOS model producing at least one LOS probability related to said specific patient's projected LOS at said healthcare facility, wherein said at least one LOS probability is based on said patient data;
implementing a waitlist model for calculating a waitlist probability based on said patient data, said waitlist probability being a probability of an emergent event of said specific patient while said specific patient remains on a waiting list for said at least one medical procedure;
implementing a scheduling model configured to receive said at least one LOS probability, said waitlist probability, and current occupancy data of said healthcare facility as inputs, and said scheduling model being configured to output said at least one schedule element;
receive said at least one schedule element as output from said scheduling model; and
automatically enter said schedule element in said calendaring system, wherein said at least one schedule element is configured for entry in said calendaring system,
wherein said at least one schedule element has a specific set of components,
wherein said components of said schedule element comprise: a type of a medical procedure; a date for said medical procedure; a time for said medical procedure; a location for said medical procedure; a list of resources required for said medical procedure; and a list of invitees for said medical procedure, and
wherein values of said components are automatically determined by said scheduling model.
These elements amount to a form of managing personal behavior or relationships or interactions between people, and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. The elements reciting calculation of probabilities also constitute a form of mathematical calculations, and fall within the scope of the abstract idea as mathematical concepts. Fundamentally the process is that of using data about a patient to estimate their length of stay, probability of experiencing an emergent event while on a waitlist, and the facility’s occupancy to schedule a medical procedure for the patient along with the required resources and personnel. The steps listed above could be performed by a person managing such scheduling operations of a medical facility based on information about current patients and the state of the facility and its resources.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 1 recites the additional elements of a) a server “configured to” perform the subsequent data analysis and calculations, b) said scheduling model being a trained AI model recited as “configured to” receive the LOS probability, said waitlist probability, and current occupancy data of said healthcare facility as inputs and provide the schedule element as an output, and c) the calendaring system being an electronic calendaring system.
Paragraphs 31 and 54 describe the system as comprising a server implemented as either a single unit or distributed system. Paragraph 106 further specifies that “[e]mbodiments of the invention may be executed by a computer processor or similar device programmed in the manner of method steps, or may be executed by an electronic system which is provided with means for executing these steps,” and that “embodiments may take the form of computer software that is stored and/or executable and/or hosted from an online repository or from an online server.” The server is construed accordingly as encompassing generic computing devices.
Paragraph 31 states that “[t]he scheduling model 60 preferably comprises a trained AI model that outputs one or more schedule elements 70.” Paragraph 44 further states that “[t]he scheduling model 60 is preferably provided with as much training data as possible,” and lists various types of data including “historical data from the specific healthcare facility,” “data from other similar healthcare facilities and population-wide data,” “existing and historical schedules, outcomes (including mortalities), length-of-stay data, information on waitlist progress and outcomes (including emergent events experienced by waitlisted patients), demographic information for the patients, information on comorbidities of the patients, and/or other patient information,” and “personnel data and other local data for the healthcare facility.” However, no disclosure of the actual model is provided beyond the language of the claim and the example types of training data. The “trained AI model” is therefore construed as encompassing generic forms of machine learning models.
Paragraph 39 further describes the electronic calendaring system as including any of “Microsoft Outlook™, Google Calendar™, Apple Calendar™, eM Client™, HCL Domino™, Mozilla Thunderbird™, and other similar systems.” The electronic calendaring system is therefore construed as broadly encompassing implementation of a calendar using software.
The above elements each amount to mere instructions to implement the abstract idea using computing elements as tools. For example, the server is recited at a high level of generality as configured to perform corresponding computing tasks and disclosed broadly in terms of its functionality. The “trained AI model” is similarly only recited at a high level of generality as receiving the LOS probability, waitlist probability, and current occupancy data of said healthcare facility as inputs and outputting the schedule element, and is also only disclosed broadly as being a trained model. These elements are therefore not sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claim 1 only recites the server, trained AI model, and “electronic” calendaring system as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 2 recites wherein said scheduling model predicts a cumulative distribution of occupancy at said healthcare facility over a predetermined time period. These limitations fall within the scope of the abstract idea as set out above.
Claim 3 recites wherein said scheduling model minimizes said cumulative distribution when determining said values of said components. These limitations fall within the scope of the abstract idea as set out above.
Claim 4 recites wherein entering said schedule element in said calendaring system causes at least one further change in said calendaring system, and wherein specifics of said further change are also determined by said scheduling model. These limitations fall within the scope of the abstract idea as set out above.
Claim 5 recites inspecting said schedule element before entering said schedule element in said calendaring system, wherein inspection of said schedule element comprises comparing said values of said components of said schedule element to values of components of other calendar entries already in said calendaring system, such that, when values of a subset of said components of said schedule element match values of corresponding components of a specific entry of said other entries, said server identifies a conflict between said schedule element and said specific entry. These limitations fall within the scope of the abstract idea as set out above.
Claim 5 further recites the additional element of the server as configured to inspect the schedule element.
Paragraphs 31 and 54 describe the system as comprising a server implemented as either a single unit or distributed system. Paragraph 106 further specifies that “[e]mbodiments of the invention may be executed by a computer processor or similar device programmed in the manner of method steps, or may be executed by an electronic system which is provided with means for executing these steps,” and that “embodiments may take the form of computer software that is stored and/or executable and/or hosted from an online repository or from an online server.” The server is construed accordingly as encompassing generic computing devices.
The recited server only amounts to mere instructions to implement the abstract idea using computing elements as tools. The server is recited at a high level of generality as configured to inspect the schedule element, and disclosed broadly in terms of its functionality. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 6 recites feeding back, when said conflict is identified, said at least one LOS probability and said waitlist probability of said specific patient to said scheduling model, along with input data for said specific entry, to thereby produce at least one of a revised schedule element and a revised specific entry, said revised schedule element having at least one component value that differs from a component value of said schedule element and said revised specific entry having at least one component value that differs from a component value of said specific entry. These limitations fall within the scope of the abstract idea as set out above.
Claim 6 further recites the additional element of the server as configured to perform the function of “feeding back” the at least one LOS probability and said waitlist probability of said specific patient.
As set out above, paragraphs 31 and 54 describe the system as comprising a server implemented as either a single unit or distributed system. Paragraph 106 further specifies that “[e]mbodiments of the invention may be executed by a computer processor or similar device programmed in the manner of method steps, or may be executed by an electronic system which is provided with means for executing these steps,” and that “embodiments may take the form of computer software that is stored and/or executable and/or hosted from an online repository or from an online server.” The server is construed accordingly as encompassing generic computing devices.
The recited server only amounts to mere instructions to implement the abstract idea using computing elements as tools. The server is recited at a high level of generality as configured to “feed back” the LOS probability and waitlist probability to the scheduling model, and is disclosed broadly in terms of its functionality. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 7 recites wherein said scheduling model produces both a revised schedule element and a revised specific entry. These limitations fall within the scope of the abstract idea as set out above.
Claim 8 recites wherein said scheduling model also takes existing entries in said calendaring system as inputs, and wherein said values of components of said schedule element do not conflict with components of said existing entries. These limitations fall within the scope of the abstract idea as set out above.
Claim 9 recites wherein said scheduling model generates a schedule element for all patients at said healthcare facility at a specific time, thereby generating a full schedule for medical procedures at said healthcare facility. These limitations fall within the scope of the abstract idea as set out above.
Claim 10 recites wherein said scheduling model is generated using population-wide data and on historical data from said healthcare facility. These limitations fall within the scope of the abstract idea as set out above.
Claim 10 further recites the additional element of training the scheduling model using the population-wide data and on historical data from said healthcare facility.
Paragraph 31 states that “[t]he scheduling model 60 preferably comprises a trained AI model that outputs one or more schedule elements 70.” Paragraph 44 further states that “[t]he scheduling model 60 is preferably provided with as much training data as possible,” and lists various types of data including “historical data from the specific healthcare facility,” “data from other similar healthcare facilities and population-wide data,” “existing and historical schedules, outcomes (including mortalities), length-of-stay data, information on waitlist progress and outcomes (including emergent events experienced by waitlisted patients), demographic information for the patients, information on comorbidities of the patients, and/or other patient information,” and “personnel data and other local data for the healthcare facility.” However, no further disclosure of the actual model or the training process is provided beyond the listed example types of data. Training the scheduling model is therefore construed as encompassing generic forms of machine learning techniques.
The recited training of the scheduling model only amounts to mere instructions to implement the abstract idea using computing elements as tools. The training is only recited at a high level of generality as the model being “trained on” the population-wide data and on historical data from said healthcare facility, and no disclosure is provided of the training itself. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 11 recites wherein said emergent event is at least one of an unplanned hospitalization of said at least one patient and a mortality of said at least one patient. These limitations fall within the scope of the abstract idea as set out above.
Claim 12 recites the additional element of wherein at least one of said LOS model and said waitlist model is a trained AI model.
Paragraph 56 states that “[i]n some embodiments, the LOS model 40 and/or the waitlist model 50 comprise trained AI models that are trained using data on relevant populations.” No further disclosure is provided of particular “AI” models or processes by which such models are trained. The trained AI model is therefore construed as encompassing any generic form of artificial intelligence algorithm.
The recited trained AI model only amounts to mere instructions to implement the abstract idea using computing elements as tools. The model itself is only recited at a high level of generality as a “trained AI model,” and no disclosure is provided of actual models or training. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 13 recites wherein both of said LOS model and said waitlist model are trained AI models.
Paragraph 56 states that “[i]n some embodiments, the LOS model 40 and/or the waitlist model 50 comprise trained AI models that are trained using data on relevant populations.” No further disclosure is provided of particular “AI” models or processes by which such models are trained. The trained AI models are therefore construed as encompassing any generic form of artificial intelligence algorithm.
The recited trained AI models only amount to mere instructions to implement the abstract idea using computing elements as tools. The models itself are only recited at a high level of generality as “trained AI models,” and no disclosure is provided of actual models or training. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 14 recites wherein said list of invitees comprises at least one medical professional. These limitations fall within the scope of the abstract idea as set out above.
Claim 15 recites wherein said at least one medical professional is identified by said scheduling model by assessment of said type of said medical procedure and of personnel records of said healthcare facility, such that said at least one medical professional has a suitable skill level for said medical procedure. These limitations fall within the scope of the abstract idea as set out above.
Claim 16 recites wherein said server is further configured to transmit said schedule element to at least one person on said list of invitees before entering said schedule element in said calendaring system. These limitations fall within the scope of the abstract idea as set out above.
Claim 17 recites wherein said cumulative distribution of occupancy at said healthcare facility is a predicted distribution of occupancy of a specific subset of said healthcare facility. These limitations fall within the scope of the abstract idea as set out above.
Claim 18 recites wherein said specific subunit of said healthcare facility is an Intensive Care Unit (ICU). These limitations fall within the scope of the abstract idea as set out above.
Claim 19 recites wherein said cumulative distribution of occupancy at said healthcare facility comprises a predicted distribution of occupancy in an Intensive Care Unit (ICU) and a predicted distribution of occupancy in said healthcare facility. These limitations fall within the scope of the abstract idea as set out above.
Claim 20 recites wherein said at least one patient is designated a high-risk patient when said waitlist probability of said at least one patient is high and wherein said scheduling model is trained to prioritize medical procedures such that high-risk patients are scheduled for near-term medical procedures. These limitations fall within the scope of the abstract idea as set out above.
Claims 1-20 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In order to satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(I). However, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed, and even original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. See MPEP 2161.01(I) citing in part Ariad, 598 F.3d at 1349 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention's boundaries.").
With regard to claim 1, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a system which “implement[s] a scheduling model, said scheduling model being a trained AI model configured to receive said at least one LOS probability, said waitlist probability, and current occupancy data of said healthcare facility as inputs, and said scheduling model being configured to output said at least one schedule element…wherein said components of said schedule element comprise: a type of a medical procedure; a date for said medical procedure; a time for said medical procedure; a location for said medical procedure; a list of resources required for said medical procedure; and a list of invitees for said medical procedure…”.
Paragraphs 30 and 31 of the specification as originally filed describe the scheduling as being performed by one or more trained AI models, and the scheduling model as comprising a trained AI model that outputs one or more schedule elements.
Paragraph 44 further states that:
“The scheduling model 60 is preferably provided with as much training data as possible. For example, the scheduling model 60 is preferably trained on historical data from the specific healthcare facility, as well as on data from other similar healthcare facilities and population-wide data. The scheduling model 60 preferably receives existing and historical schedules, outcomes (including mortalities), length-of-stay data, information on waitlist progress and outcomes (including emergent events experienced by waitlisted patients), demographic information for the patients, information on comorbidities of the patients, and/or other patient information to use as training data. As well, the scheduling model 60 is preferably trained on personnel data and other local data for the healthcare facility (i.e., data related to the medical professionals, infrastructure, and equipment available to the specific healthcare facility where the system IO is implemented).”
However, no disclosure is provided of any particular models used, or of the scheduling model beyond characterization as a “trained AI model.” Examiner notes that the term “AI model” is only a description of an entire field of mathematical modeling techniques, and encompasses dozens of distinct model types having different requirements, functionalities, and use characteristics. Furthermore, no further disclosure is provided of how any such model is trained beyond listing examples of potential categories of training data and that the model is trained using the historical data.
Examiner notes that the disclosure also fails to provide support for generating a schedule element based on said at least one LOS probability and current occupancy data of said healthcare facility as inputs. While paragraph 51 provides a general example of assigning a surgical slot to a first patient in preference over a second patient based on the first patient having a higher mortality probability than the second patient, no disclosure is provided of how a scheduling element is determined in any fashion using a length of stay probability.
Given that no particular model is disclosed, and no manner of training such a model is provided beyond broad categories of information which could be used as training data, the disclosure fails to provide written description support for the scheduling model and its functions.
Claims 2-20 inherit the deficiencies of claim 1 through dependency and are likewise rejected.
With regard to claim 2, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a system in which the scheduling model predicts a cumulative distribution of occupancy at said healthcare facility over a predetermined time period.
As set out above, the disclosure does not provide written description support for the claimed trained AI scheduling model, and contains no disclosure of any particular models used, or of the scheduling model beyond characterization as a “trained AI model.” Similarly, while the specification states in paragraph 42 that “[t]he occupancy rate may be modeled as a function of various parameters in the healthcare facility,” “the occupancy rate may be predicted and/or determined by the scheduling model 60 based on its input data,” and that “the scheduling model 60 is configured to predict a cumulative distribution of occupancy for the healthcare facility,” no disclosure is provided of how the scheduling model performs these functions or what model is actually being used to predict a cumulative distribution of occupancy at said healthcare facility over a predetermined time period. Likewise, no actual occupancy function for the healthcare facility over a period of time is disclosed for further use in calculation of a corresponding cumulative distribution function.
With regard to claim 3, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a system in which the scheduling model minimizes said cumulative distribution when determining said values of said components.
Examiner references the rejections and rationales set out above with respect to claims 1 and 2 detailing the lack of support for both the scheduling model itself and the prediction of a cumulative distribution function. Paragraph 41 further states that “the scheduling model 60 is configured to minimize an occupancy of said healthcare facility,” while paragraph 42 provides that “the scheduling model 60 is configured to predict a cumulative distribution of occupancy for the healthcare facility and to determine schedule element(s) that satisfy a minimum (or one or more local minima) of the occupancy distribution function.” However, no disclosure is provided of how the scheduling model determines values of scheduling components which minimize the cumulative distribution. This is especially true given the lack of support for how the scheduling model determines the values of scheduling components.
With regard to claim 10, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a system in which the scheduling model is trained on population-wide data and on historical data from said healthcare facility.
As cited above with respect to claim 1, paragraph 44 states that:
“The scheduling model 60 is preferably provided with as much training data as possible. For example, the scheduling model 60 is preferably trained on historical data from the specific healthcare facility, as well as on data from other similar healthcare facilities and population-wide data. The scheduling model 60 preferably receives existing and historical schedules, outcomes (including mortalities), length-of-stay data, information on waitlist progress and outcomes (including emergent events experienced by waitlisted patients), demographic information for the patients, information on comorbidities of the patients, and/or other patient information to use as training data. As well, the scheduling model 60 is preferably trained on personnel data and other local data for the healthcare facility (i.e., data related to the medical professionals, infrastructure, and equipment available to the specific healthcare facility where the system IO is implemented).”
However, no disclosure is provided of any particular models used, or of the scheduling model beyond characterization as a “trained AI model.” Examiner notes that the term “AI model” is only a description of an entire field of mathematical modeling techniques, and encompasses dozens of distinct model types having different training methods and requirements. No further disclosure is provided of how any such model is trained beyond listing examples of potential categories of training data and that the model is trained using population-wide data or historical data from the healthcare facility.
Given that no particular model is disclosed, and no manner of training such a model is provided beyond broad categories of information which could be used as training data, the disclosure fails to provide written description support for training the scheduling model on population-wide data and on historical data from said healthcare facility.
With regard to claims 12 and 13, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a trained AI LOS model and a trained AI waitlist model.
Paragraphs 20 and 21 reflect the language of the claims themselves in stating that the LOS model and said waitlist model are trained AI models. Paragraph 56 similarly only provides that “[i]n some embodiments, the LOS model 40 and/or the waitlist model 50 comprise trained AI models that are trained using data on relevant populations.” However, no further disclosure is provided of any “trained AI models” corresponding to the LOS model and waitlist model, or how any such models perform the corresponding functions of producing at least one LOS probability related to said specific patient's projected LOS at said healthcare facility and calculating a waitlist probability based on said patient data of an emergent event of said specific patient while said specific patient remains on a waiting list for said at least one medical procedure. Merely describing the model as a “trained AI model” and stating it may be trained using data on relevant populations is not sufficient to provide written description support for a model capable of performing specific functions.
With regard to claims 17, 18, and 19, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a system which predicts a cumulative distribution of occupancy of a specific subset of said healthcare facility, or which does so where the subunit of said healthcare facility is an Intensive Care Unit (ICU).
Examiner references the rejections and rationales set out above with respect to claims 1 and 2 detailing the lack of support for both the scheduling model itself and the prediction of a cumulative distribution function. Paragraphs 25 and 26 reflect the language of the claims themselves with no further disclosure. Paragraph 43 states that the maximum occupancy corresponding to the occupancy function may be the maximum occupancy of a subunit of the healthcare facility, while paragraph 48 describes the scheduling model creating a schedule for an ICU. However, no further disclosure is provided of how the system predicts a cumulative distribution of occupancy of a specific subset of said healthcare facility, or predicts a cumulative distribution of occupancy of an Intensive Care Unit (ICU).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 is indefinite because Examiner is unable to determine the metes and bounds of the claim. Specifically, claim 15 is presently recited as depending from itself. For purposes of the present examination claim 15 has been construed as depending from claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-9, 11, 12, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Day et al (US Patent Application Publication 2021/0193302) in view of McNair (US 10,622,109) and Chattopadhyay et al (US Patent Application Publication 2024/0161911).
With respect to claim 1, Day discloses the claimed system for managing an electronic calendaring system, said system comprising:
a server ([98] describes the system deployed as a server) configured to produce at least one schedule element detailing at least one medical procedure for a specific patient at a healthcare facility, and said server being configured to:
receive patient data, said patient data relating to said specific patient ([43]-[45] and [49] describe receiving patient case data relating to particular patients);
implement a length of stay (LOS) model, said LOS model producing at least one LOS probability related to said specific patient's projected LOS at said healthcare facility, wherein said at least one LOS probability is based on said patient data (Figures 5A and 5B elements 502 and 112, [34], [45], [73]-[75], and [84]-[85] describe trained timeline models used to estimate the amount of time required for events such as time spent on specific units as well as time until discharge, i.e. probabilities related to projected LOS);
implement a scheduling model configured to receive said at least one LOS probability and current occupancy data of said healthcare facility as inputs, and said scheduling model being configured to output said at least one schedule element (Figures 1 and 4 element 118, and Figure 5C, [40], [42], [45], [46], [50], [51], [86], [90], and [100] describe a resource optimization component which schedules patient procedures based on the projected timelines and current state data, including current occupancy);
receive said at least one schedule element as output from said scheduling model (Figure 5C elements 412 and 510, [31], [45], and [86] show and describe the system receiving the scheduling information from the optimization component); and
automatically enter said schedule element in said calendaring system, wherein said at least one schedule element is configured for entry in said electronic calendaring system (Figure 5C elements 412 and 510, [31], [45], [86], and [102] show and describe the system automatically entering the scheduling information to schedule the respective patients and procedures/resources),
wherein said at least one schedule element has a specific set of components (Figure 5C elements 412 and 510, [39], [45], [46], and [87] describe the scheduled events as having corresponding times, places, resources, staff, and other elements),
wherein said components of said schedule element comprise: a type of a medical procedure; a date for said medical procedure; a time for said medical procedure; a location for said medical procedure; a list of resources required for said medical procedure; and a list of invitees for said medical procedure ([45], [46], [51], [52], [54], [57], [87], and [91] describe the system scheduling the respective cases with information on the particular procedure, date/time, room, ward, or other location, resources assigned, and staff assigned; Figure 8 shows an example dashboard), and
wherein values of said components are automatically determined by said scheduling model (Figure 5C element 412, [86], [87], [101], and [102] describe the optimization component performing the task of determining the associated resources and elements for each scheduled procedure);
but does not expressly disclose:
implementing a waitlist model for calculating a waitlist probability based on said patient data, said waitlist probability being a probability of an emergent event of said specific patient while said specific patient remains on a waiting list for said at least one medical procedure;
said scheduling model being a trained AI model configured to receive said waitlist probability.
However, McNair teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to implement a waitlist model for calculating a waitlist probability based on patient data, said waitlist probability being a probability of an emergent event of said specific patient while said specific patient remains on a waiting list for said at least one medical procedure (Figures 2, 3A, and 9A, Column 3 lines 19-42, Column 7 lines 33-49, and Column 9 lines 4-10 describe using patient biomarkers to calculate a probability of mortality while waiting for a surgery), and use a scheduling model to generate a schedule element based on the waitlist probability (Figures 2 and 3A, Column 7 lines 49-51, Column 10 lines 53-56, Column 12 lines 9-14, Column 14 line 65 – Column 15 line 9, and Column 15 line 65 – Column 16 line 3 describe using the probability to schedule a particular patient for surgery).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the system of Day to implement a waitlist model for calculating a waitlist probability based on patient data, said waitlist probability being a probability of an emergent event of said specific patient while said specific patient remains on a waiting list for said at least one medical procedure, and use a scheduling model to generate a schedule element based on the waitlist probability as taught by McNair since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Day already discloses contemplates using patient acuity or priority in aspects of resource allocation (see e.g. [59] and [93]), and generating a scheduling element using a probability of an emergent event of a patient on a waiting list calculated using the patient’s data as taught by McNair would serve that same function in Day, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Chattopadhyay further teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to use a trained AI model to generate patient scheduling elements (Figures 6 and 8, [15], [17], [23], and [26] describe a trained AI model using information on patient procedures, current scheduling data, and current resource availability to generate schedules for future patient procedures).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day and McNair to use a trained AI model to generate patient scheduling elements as taught by Chattopadhyay since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Day and McNair already teach using scheduling models to generate scheduling elements for patient procedures, and generating the scheduling elements using a trained AI model as taught by Chattopadhyay would serve that same function in Day and McNair, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 4, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein entering said schedule element in said calendaring system causes at least one further change in said calendaring system, and wherein specifics of said further change are also determined by said scheduling model ([87], [89], [91], and [94] describe the system adjusting schedules for multiple resources in reaction to a scheduled case or allocation).
With respect to claim 5, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein said server is further configured to inspect said schedule element before entering said schedule element in said calendaring system, wherein inspection of said schedule element comprises comparing said values of said components of said schedule element to values of components of other calendar entries already in said calendaring system, such that, when values of a subset of said components of said schedule element match values of corresponding components of a specific entry of said other entries, said server identifies a conflict between said schedule element and said specific entry ([45], [87], [89], [91], [95], and [102] describe the system reordering and changing the allocations based on the current timeline of cases and a change to be made to the timeline).
With respect to claim 6, Day/McNair/Chattopadhyay teach the system according to claim 5. Day further discloses:
wherein, when said conflict is identified, said server feeds back said at least one LOS probability of said specific patient to said scheduling model, along with input data for said specific entry, to thereby produce at least one of a revised schedule element and a revised specific entry ([45], [87], [89], [91], [95], and [102] describe the system reordering and changing the allocations based on the current timeline of cases and a change to be made to the timeline),
said revised schedule element having at least one component value that differs from a component value of said schedule element and said revised specific entry having at least one component value that differs from a component value of said specific entry ([45], [87], [89], [91], [95], and [102] describe the system reordering and changing the allocations based on the current timeline of cases and a change to be made to the timeline);
but does not expressly
feeding back said waitlist probability of said specific patient to said scheduling model.
However, McNair teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to calculate a waitlist probability and feed the probability back to a scheduling model (Column 5 lines 61-65, Column 14 line 65 – Column 15 line 9, and Column 15 line 65 – Column 16 line 3 describe repeatedly inputting a probability of mortality to a scheduling model in order to schedule a procedure for a patient).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to calculate a waitlist probability and feed the probability back to a scheduling model as taught by McNair since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches feeding patient data back into a scheduling model in order to generate a scheduling element, and including a waitlist probability in that information as taught by McNair would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 7, Day/McNair/Chattopadhyay teach the system according to claim 6. Day further discloses:
wherein said scheduling model produces both a revised schedule element and a revised specific entry ([45], [87], [89], [91], [95], and [102] describe the system reordering and changing the allocations based on the current timeline of cases and a change to be made to the timeline).
With respect to claim 8, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein said scheduling model also takes existing entries in said calendaring system as inputs, and wherein said values of components of said schedule element do not conflict with components of said existing entries (Figure 5C shows existing scheduled cases being fed back into the system as current state data; [27], [43], [45], [46], [47], [87], and [90] describe the system scheduling and allocating new patients based on the current state of the facility).
With respect to claim 9, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein said scheduling model generates a schedule element for all patients at said healthcare facility at a specific time, thereby generating a full schedule for medical procedures at said healthcare facility ([43], [46], [57], and [93] describe the system considering all patient cases at the facility; [40], [45], [46], [86], and [89] describe the optimization component generating schedules).
With respect to claim 11, Day/McNair/Chattopadhyay teach the system according to claim 1. Day does not expressly disclose wherein said emergent event is at least one of an unplanned hospitalization of said at least one patient and a mortality of said at least one patient.
However, McNair teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to calculate a waitlist probability of patient mortality prior to a procedure (Figure 9A, Column 7 lines 44-51, Column 8 lines 11-15, Column 14 lines 1-6, and Column 21 lines 63-67 describe the probability including a probability of mortality of the patient).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to calculate a waitlist probability of patient mortality prior to a procedure as taught by McNair since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches calculating a waitlist probability, and having the probability be a probability of patient mortality as taught by McNair would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 12, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein at least one of said LOS model and said waitlist model is a trained AI model ([78], [80], and [85] describe the timeline models including trained AI models).
With respect to claim 14, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein said list of invitees comprises at least one medical professional ([46], [51], and [57] describe the system assigning clinical staff).
With respect to claim 15, Day/McNair/Chattopadhyay teach the system according to claim 1. Day further discloses:
wherein said at least one medical professional is identified by said scheduling model by assessment of said type of said medical procedure and of personnel records of said healthcare facility, such that said at least one medical professional has a suitable skill level for said medical procedure ([54], [58], and [60] describe the system having information on the skills and qualifications of staff members such as surgeons, and using rules regarding required qualifications).
With respect to claim 20, Day/McNair/Chattopadhyay teach the system according to claim 1. Day does not expressly disclose wherein said at least one patient is designated a high-risk patient when said waitlist probability of said at least one patient is high and wherein said scheduling model is trained to prioritize medical procedures such that high-risk patients are scheduled for near-term medical procedures.
However, McNair teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to designated a patient as a high-risk patient when a waitlist probability of said at least one patient is high and wherein a scheduling model prioritizes medical procedures such that high-risk patients are scheduled for near-term medical procedures (Column 14 line 65 – Column 15 line 6, Column 15 lines 61-65, Column 17 line 61 – Column 18 line 4, and Column 19 lines 18-25 describe designating patients according to risk and prioritizing high risk patients for urgent scheduling).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to designated a patient as a high-risk patient when a waitlist probability of said at least one patient is high and wherein a scheduling model prioritizes medical procedures such that high-risk patients are scheduled for near-term medical procedures as taught by McNair since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches calculating a waitlist probability for patients, and further designating a patient as a high-risk patient when a waitlist probability of said at least one patient is high and scheduling high-risk patients for near-term medical procedures as taught by McNair would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Chattopadhyay further teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to train an AI scheduling model to schedule patient procedures (Figures 6 and 8, [15], [17], [23], and [26] describe a trained AI model using information on patient procedures, current scheduling data, and current resource availability to generate schedules for future patient procedures).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to use a trained AI model to schedule the patient procedures as taught by Chattopadhyay since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Day, McNair, and Chattopadhyay already teach using scheduling models to generate scheduling elements for patient procedures, and generating the scheduling elements using a trained AI model as taught by Chattopadhyay would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Day et al (US Patent Application Publication 2021/0193302) in view of McNair (US 10,622,109) and Chattopadhyay et al (US Patent Application Publication 2024/0161911) as applied to claim 1, and further in view of Bhavani (US Patent Application Publication 2021/0391062).
With respect to claim 10, Day/McNair/Chattopadhyay teach the system according to claim 1. Day does not expressly disclose wherein said scheduling model is trained on population-wide data and on historical data from said healthcare facility.
However, Bhavani teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to train a scheduling model on population-wide data and on historical data from a healthcare facility ([15], [16], and [18]-[22] describe training a patient scheduling machine learning model using historical data for the local community or region as well as for the healthcare facility).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to train a scheduling model on population-wide data and on historical data from a healthcare facility as taught by Bhavani since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches training a scheduling model, and doing so using population-wide data and on historical data from a healthcare facility as taught by Bhavani would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Day et al (US Patent Application Publication 2021/0193302) in view of McNair (US 10,622,109) and Chattopadhyay et al (US Patent Application Publication 2024/0161911) as applied to claim 1, and further in view of Fabian (US Patent Application Publication 2017/0177806).
With respect to claim 16, Day/McNair/Chattopadhyay teach the system according to claim 1. Day does not expressly disclose wherein said server is further configured to transmit said schedule element to at least one person on said list of invitees before entering said schedule element in said calendaring system.
However, Fabian teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to transmit a schedule element to at least one person on a list of invitees for a scheduled medical event before entering said schedule element in a calendaring system ([73], [75], [77], [79], and [80] describe a surgical scheduling system in which staff members being scheduled for procedures are notified prior to finalizing the scheduled procedure).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to transmit a schedule element to at least one person on a list of invitees for a scheduled medical event before entering said schedule element in a calendaring system as taught by Fabian since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches training allowing individuals involved in the process to view and receive information on schedule elements (see Day [97]), and further transmitting a schedule element to at least one person on a list of invitees for a scheduled medical event before entering said schedule element in a calendaring system as taught by Fabian would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Day et al (US Patent Application Publication 2021/0193302) in view of McNair (US 10,622,109) and Chattopadhyay et al (US Patent Application Publication 2024/0161911) as applied to claim 12, and further in view of Alghatani et al, Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation (hereinafter Alghatani).
With respect to claim 13, Day/McNair/Chattopadhyay teach the system according to claim 12. Day does not expressly disclose wherein both of said LOS model and said waitlist model are trained AI models.
However, Alghatari teaches that it was old and well known in the art of medical resource assignment before the effective filing date of the claimed invention to have both of a LOS model and a waitlist model be trained AI models
(Abstract, P.11, and P.14 – P.16 describe training AI models for predicting both patient length of stay and patient mortality).
Therefore it would have been obvious to one of ordinary skill in the art of medical resource assignment before the effective filing date of the claimed invention to modify the combination of Day, McNair, and Chattopadhyay to have both a LOS model and a waitlist model be trained AI models as taught by Alghatari since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of Day, McNair, and Chattopadhyay already teaches a trained AI LOS model, and having both the LOS model and a waitlist model be trained AI models as taught by Alghatari would serve that same function in Day, McNair, and Chattopadhyay, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claims not Presently Rejected under 35 USC 102/103
Claims 2, 3, and 17-19 are not presently rejected under 35 USC 102/103 under the closest prior art of record cited herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Abri (US Patent Application Publication 2009/0125337);
Martindale et al (US Patent Application Publication 2020/0211701);
Aggarwal et al (US Patent Application Publication 2021/0375441);
Padala (US Patent Application Publication 2019/0304596).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Gregory Lultschik/Examiner, Art Unit 3682
Prosecution Insights