Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In the amendment filed 12/16/2025, the following has occurred: claims 1 10, 16, and 20 have been amended and claim 14 has been canceled. Now, claims 1-3, 5-11, 13, and 15-23 are pending.
The previous rejections under 35 U.S.C. 112(b) are withdrawn based on the amendments to the claims.
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 1-3, 5-11, 13, and 15-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claim 1 recites extracting clinical features for respective patients of a queue of patients scheduled for examination, wherein the clinical features comprise at least a diagnosis or historical medical treatment for the respective patients; extracting operational features for the medical facility; estimating patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on both the extracted clinical features of the respective patients of the queue and extracted operational features for the medical facility; estimating a wait time for a query patient of the patients of the queue based on the estimated examination time durations of patients ahead of the query patient in the queue; transmitting the estimated wait time for presentation; and iteratively repeating the extracting, the estimating of patient-specific examination time durations, and the estimating of the wait time for the query patient to iteratively update the estimated wait time in real-time for the query patient; and receiving a check-in for the query patient.
Claim 16 recites extract patient features for the respective patients of the queue of patients from the database, wherein the patient features comprise at least a diagnosis or historical medical treatment for the respective patients; extracting operational features for a medical facility in which the queue of patients are being examined; estimate patient-specific examination time durations for medical examinations of the respective patients of the queue based on both the extracted patient features of the respective patients of the queue and the extracted operational features for the medical facility; estimate a wait time for a query patient of the patients of the queue based on the estimated examination time durations of patients ahead of the query patient in the queue; transmit the estimated wait time for presentation; and receive a check-in for the query patient.
Claim 20 recites extracting clinical features for respective patients of a queue of patients scheduled for examination, wherein the clinical features comprise at least a diagnosis or historical medical treatment for the respective patients; extracting operational features of the medical facility; estimating patient-specific examination time durations for medical examinations of the respective patients of the queue based on both the extracted clinical features of the respective patients of the queue and extracted operational features for the medical facility; applying a workflow model for the medical examinations of the respective patients of the queue; estimating a wait time for a query patient of the patients of the queue based on the estimated examination time durations of patients ahead of the query patient in the queue and the workflow model; transmitting the estimated wait time for presentation; and receiving a check-in for the query patient.
These limitations, as drafted, given the broadest reasonable interpretation, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, the claims encompass manually extracting data from a queue of scheduled patients, manually extracting operational features for a medical facility, estimating time for patient exams of the patients in the queue using the extracted information, estimating wait time for one of the patients and providing the wait time information to a user, and receiving a check-in for the patient. These could be manual steps carried out between patients and staff in a hospital. Claim 20 includes the additional step of applying a workflow model in the estimation processes. This could similarly be carried out by staff in a hospital in estimating the wait time. Additionally, claim 1 includes the steps of iteratively repeating the above identified steps to update the wait time in real-time. The only discussion in the specification of real-time updates is paragraph 0011, which simply states that an advantage is “providing real-time updates to a patient regarding a wait time for a medical procedure.” Therefore, the broadest reasonable interpretation of this step encompasses manually providing the patient with a wait time as it becomes available, such as hospital staff notifying the patient.
Claims 2-3, 5-11, 13, 15, 17-19, and 21-23 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claims 2-3, 6, and 17 include the workflow model identified above as part of the abstract idea. Claims 5, 7, 10-11, 13, 15, and 19 further expand on the estimated wait time and time duration prediction. Claims 8-9 and 18 further expand on the extracted patient features. Claims 21-23 further define the operational features of the medical facility, which is also part of the abstract idea identified above.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with adding elements similar to adding the words “apply it” to the abstract idea, and generally linking the abstract idea to a particular technological environment, along with insignificant, extra-solution data gathering activity.
Claims 1-3, 5-11, 13, 15, and 21, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “A non-transitory computer readable medium (26) storing instructions executable by at least one electronic processor,” “a display.”
Claims 16-19, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “a database,” “a cellphone upon which a cellphone application program with a user interface is implemented; and at least one electronic processor.” The broadest reasonable interpretation of the recited cellphone with a program and display is a generic computer component such as a smartphone.
The written description discloses that the recited computer components encompass generic components including “cellular telephone 52, or a tablet computer, personal data assistant or PDA, and/or so forth)” (see paragraph 0036). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-20, directly or indirectly, recite the following additional elements at a high level of generality, amounting to no more than insignificant, extra-solution activity: “transmitting…to a user interface,” “transmit…to the user interface,” and various receiving and transmitting, including via “email or SMS message,” via the user interface and to generic computer devices such as a cellphone. These recitations amount to no more than insignificant, extra-solution data gathering and transmitting activity. As set forth in MPEP 2106.05(g) insignificant, extra-solution activity, such as insignificant acquisition and data transmission, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-20, directly or indirectly, recite the following additional elements similar to adding the words “apply it” to the abstract idea and generally linking the abstract idea to a particular technological environment: carrying out the estimate using “a deep learning model.” Each of these limitations are recited at a high degree of generality, implemented in a fashion similar to adding the words “apply it” to the abstract idea and generally linking the abstract idea to a particular technological environment. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Insignificant, extra solution, data gathering and transmitting activity (e.g. transmitting and receiving data via a user interface and/or cellphone) has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
Generally linking the abstract idea to a particular technological environment (e.g. using a deep learning model) does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-3, 5, 8-10, and 15-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Weber, US Patent Application Publication No. 2015/0379215 in view of Sanderferd, US Patent Application Publication No. 2017/0124526 and further in view of Saffari, US Patent Application Publication No. 2020/0226551.
As per claim 1, Weber teaches a non-transitory computer readable medium storing instructions executable by at least one electronic processor to perform a patient wait time tracking method for a medical facility, the method comprising: extracting clinical features for respective patients of a queue of patients scheduled for examination, wherein the clinical features comprise at least a diagnosis or historical medical treatment for the respective patients (see paragraphs 0034 and 0036; clinical information for patients in a queue (with scheduled appointments), including information about the medical history and current medical conditions of the patients, is extracted from EHR system by Queue estimator module); estimating patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on the extracted clinical and operational features of the respective patients of the queue (see paragraph 0036 a typical duration of appointment for particular symptoms, condition or service is estimated for the patients in the queue); extracting operational features for the medical facility (see paragraphs 0036-0037; the extracted operational features of the medical facility encompassing typical duration of appointments for individual doctor and information regarding the doctor running behind schedule); determining examination time durations for medical examinations or procedures of the respective patients of the queue (see paragraph 0036; the typical duration of appointments for the individual patient); estimating a wait time for a query patient of the patients of the queue based on the examination time durations of patients ahead of the query patient in the queue (see paragraph 0036 queue estimator calculates the waiting time based on the acquired information, including duration estimates of patients in the queue, for each patient); transmitting the estimated wait time to a user interface for presentation, the user interface comprising a cellphone of the query patient via an email, automatic phone call, or SMS message (see paragraph 0041; current waiting time and option to reschedule is transmitted to patient interface via push notification; paragraph 0064; system may be implemented on a mobile phone) and iteratively repeating the extracting, the determining of examination time durations, and the estimating of the wait time for the query patient to iteratively update the estimated wait time in real-time for the query patient (see paragraph 0036; continuously, or at intervals, recomputes estimated waiting time).
Weber utilizes examination time durations for estimating a wait time for a query patient. However, Weber does not explicitly teach estimating, with a deep learning model, patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on both the extracted clinical features of the respective patients of the queue and extracted operational features for the medical facility. Sanderferd teaches estimating, with a deep learning model, patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on both clinical features of the respective patients of the queue and operational features for the medical facility (see paragraph 0272; prediction of each appointment duration can be based on clinical factors, such as age of the patient and a reason for visit and facility features, such as the office or facility of the appointment; paragraph 0283 describes using artificial intelligence or machine learning for the appointment duration estimation). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize a deep learning model to estimate the examination time used by Weber to estimate wait times with the motivation of improving accuracy of appointment timing and allow for better tracking of trends of availability to match with patient preferences (see paragraph 0069 of Sanderferd).
Weber and Sanderferd does not explicitly teach receiving a check-in for the query patient via the user interface. Saffari teaches receiving a check-in for the query patient via the user interface (see paragraph 0065; describes patient check-in on their mobile device). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to implement the smartphone-based interaction of Saffari to provide and receive information in the system of Weber and Sanderferd with the motivation of further improving the check-in and waiting of patients at clinics (see paragraph 0007 of Saffari).
As per claim 2, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches estimating a wait time for the query patient includes: applying a workflow model for the medical examinations of the respective patients of the queue (see paragraph 0036; e.g. known statistical analysis).
As per claim 3, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 2. Weber further teaches the workflow model represents one or more of: a number of concurrent examinations, and/or a number of available medical resources (see paragraph 0036; takes into account information about the duration of appointments for the individual patient or individual doctor, representing concurrent examinations and available medical resources).
As per claim 5, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. As explained above, Weber does not explicitly teach estimating patient-specific examination time durations. Sanderferd further teaches estimating patient-specific examination time durations includes estimating the patient-specific examination wait times based on one or more of: mobility and/or cognitive impairment of a patient ahead in the queue, patient allergies, a patient ahead in a queue having ancillary equipment that may slow examination, a type of examination to be performed, a frequency of an examination to be performed, a likelihood of repeating an imaging session, patient preparedness for examination, issues that may result in image artefacts, a wellness of the patient at check- in, availability of additional help, physical monitoring of the patient, a predicted patient arrival time, and a nature, length or instructions for a procedure or follow-up appointment (see paragraph 0272; predicted appointment duration based on multiple factors including at least a reason for visit (type of exam)). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize a deep learning model to estimate the examination time used by Weber to estimate wait times with the motivation of improving accuracy of appointment timing and allow for better tracking of trends of availability to match with patient preferences (see paragraph 0069 of Sanderferd).
As per claim 8, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches the extracted clinical and operational features include one or more of age, gender, body mass index, smoking history, and chronic illness data from a database (see paragraph 0004; extracted data as identified above is from EMR/HER systems, which includes demographic data such as age and medical history).
As per claim 9, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches the extracted clinical and operational features include patient-specific examination features for the medical examinations of the respective patients of the queue (see paragraph 0036; extracted features includes conditions to be treated and procedures to be performed).
As per claim 10, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches the transmitting further comprises: transmitting the estimated wait time to a web browser running a webpage configured to display the estimated wait time (see paragraphs 0029 and 0041; weight time notifications can be sent via PHR client which includes a web browser interface).
As per claim 15, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches the extracting includes: receiving one or more clinical and operational features for the query patient via the user interface, (see paragraph 0029; PHR client provides web-based portal for a patient to, among other functions, transmit their health information). Weber does not explicitly teach the patient-specific examination time duration for the medical examination of the query patient is estimated based at least in part on the one or more patient features for the query patient received via the user interface. Sanderferd further teaches wherein the patient-specific examination time duration for the medical examination of the query patient is estimated based at least in part on the one or more patient features for the query patient received via the user interface (see paragraph 0272; prediction of each appointment duration can be based on clinical factors, such as age of the patient and a reason for visit and facility features, such as the office or facility of the appointment). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize a deep learning model to estimate the examination time used by Weber to estimate wait times with the motivation of improving accuracy of appointment timing and allow for better tracking of trends of availability to match with patient preferences (see paragraph 0069 of Sanderferd).
As per claim 16, Weber teaches an apparatus, comprising: a database storing patient data related to patients of a queue of patients scheduled for examination (see paragraphs 0034 and 0036; clinical and operational information for patients in a queue (with scheduled appointments)); a cellphone upon which a cellphone application program with a user interface is implemented (see paragraph 0064; system may be implemented on a mobile phone); at least one electronic processor programmed to: extract patient features for respective patients of a queue of patients from the database (see paragraphs 0034 and 0036; clinical and operational information for patients in a queue (with scheduled appointments) is extracted from HER system by Queue estimator module); determining examination time durations for medical examinations or procedures of the respective patients of the queue (see paragraph 0036; the typical duration of appointments for the individual patient); estimate a wait time for a query patient of the patients of the queue based on the examination time durations of patients ahead of the query patient in the queue (see paragraph 0036 queue estimator calculates the waiting time based on the acquired information, including duration estimates of patients in the queue, for each patient); transmit the estimated wait time to the cellphone for presentation by the cellphone application program running on the cellphone (see paragraph 0041; current waiting time and option to reschedule is transmitted to patient interface via push notification; paragraph 0064; system may be implemented on a mobile phone).
Weber utilizes examination time durations for estimating a wait time for a query patient. However, Weber does not explicitly teach estimating, with a deep learning model, patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on both the extracted clinical features of the respective patients of the queue and extracted operational features for the medical facility. Sanderferd teaches estimating, with a deep learning model, patient-specific examination time durations for medical examinations or procedures of the respective patients of the queue based on both clinical features of the respective patients of the queue and operational features for the medical facility (see paragraph 0272; prediction of each appointment duration can be based on clinical factors, such as age of the patient and a reason for visit and facility features, such as the office or facility of the appointment; paragraph 0283 describes using artificial intelligence or machine learning for the appointment duration estimation). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize a deep learning model to estimate the examination time used by Weber to estimate wait times with the motivation of improving accuracy of appointment timing and allow for better tracking of trends of availability to match with patient preferences (see paragraph 0069 of Sanderferd).
Weber and Sanderferd does not explicitly teach receiving a check-in for the query patient via the user interface. Saffari teaches receiving a check-in for the query patient via the user interface (see paragraph 0065; describes patient check-in on their mobile device). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to implement the smartphone-based interaction of Saffari to provide and receive information in the system of Weber and Sanderferd with the motivation of further improving the check-in and waiting of patients at clinics (see paragraph 0007 of Saffari).
As per claim 21, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber does not explicitly teach the extracted operational features for the medical facility comprise one or more of staff experience, equipment known or predicted to be used, use of a contrast agent, a number of procedures known or predicted to be performed, a location of the procedure, a type of procedure known or predicted to be performed, and a protocol known or predicted to be used for a medical examination. Sanderferd further teaches extracted operational features for the medical facility comprise one or more of staff experience, equipment known or predicted to be used, use of a contrast agent, a number of procedures known or predicted to be performed, a location of the procedure, a type of procedure known or predicted to be performed, and a protocol known or predicted to be used for a medical examination (see paragraph 0274; operational features can be anticipated change in staff or the procedure resources expected). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to incorporate additional operational features for use by Weber to estimate wait times with the motivation of improving accuracy of appointment timing and allow for better tracking of trends of availability to match with patient preferences (see paragraph 0069 of Sanderferd).
Claims 17-20 and 22-23 recites substantially similar apparatus and method claims to computer medium claims 1-2, 9-10, and 21 and, as such, are rejected for similar reasons as given above.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Weber, US Patent Application Publication No. 2015/0379215 in view of Sanderferd, US Patent Application Publication No. 2017/0124526 and Saffari, US Patent Application Publication No. 2020/0226551 and further in view of Day, US Patent Application Publication No. 2016/0371441.
As per claim 6, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber does not explicitly teach the workflow model is a Discrete Event Simulation model. Day teaches a model for predicting patient wait times (see paragraph 0063; hospital operations prediction system identifies the states of patient flow, including patients waiting for admission, therapy, etc.), wherein states may be determined from a Discrete Event Simulation model (see paragraph 0086; simulation method, which is a discrete event method, updates state information). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize a Discrete Event Simulation model in the analysis of Weber and Sanderferd with the motivation of making forecasts more accurate where states are updated dynamically as is the case in Weber (see paragraph 0100 of Day).
As per claim 7, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber does not explicitly teach estimating the wait time further includes: calculating a confidence interval for the estimated wait time. Day teaches a model for predicting patient wait times (see paragraph 0063; hospital operations prediction system identifies the states of patient flow, including patients waiting for admission, therapy, etc.), wherein the probability of a patient being in a status includes calculating a confidence interval for the prediction (see paragraph 0092; forecasted probabilities derived from confidence intervals). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize confidence intervals in the analysis of Weber with the motivation of making forecasts more accurate where states are updated dynamically as is the case in Weber and Hanson(see paragraph 0100 of Day).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Weber, US Patent Application Publication No. 2015/0379215 in view of Sanderferd, US Patent Application Publication No. 2017/0124526 and Saffari, US Patent Application Publication No. 2020/0226551 and further in view of Larsen, US Patent Application Publication No. 2016/0027138.
As per claim 11, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber further teaches the transmitting includes at least one of displaying the estimated wait time (see paragraph 0041; current waiting time and option to reschedule is transmitted to patient interface via push notification). Weber does not explicitly teach the time is displayed as at least one of a running timer and rounding to a nearest time interval. Larsen teaches monitoring a patient waiting after check-in (see paragraph 0062) and displaying running timer of the patient’s wait time (see paragraph 0067; after the patient enters the exam area, the timer begins to count up, the current elapsed time being continually written to the display). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to provide wait time information to the patient in the form of a running timer with the motivation of improving patient time-based experiences in medical facilities (see paragraph 0007 of Larsen).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Weber, US Patent Application Publication No. 2015/0379215 in view of Sanderferd, US Patent Application Publication No. 2017/0124526 and Saffari, US Patent Application Publication No. 2020/0226551 and further in view of Bollapragada, US Patent Application Publication No. 2011/0125539.
As per claim 13, Weber, Sanderferd, and Saffari teaches the computer readable medium of claim 1. Weber does not explicitly teach updating a display of the estimated wait time on the user interface to include a buffer time period. Bollapragada a display of patient times in scheduled appointment blocks that include a buffer time period (see paragraph 0073; displayed time slots included slack of flexibility to allow for adjustments for emergency cases). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to incorporate the buffer time into the wait time displayed in Weber and Sanderferd with the motivation of allowing for unpredictable events affecting patient wait (see paragraph 0073 of Bollapragada).
Response to Arguments
In the remarks filed 06/23/2025, Applicant argues that (1) the claims do not recite Certain Methods of Organizing Human Activity because they recite interactions between a computer and human, not interactions between humans, including social activities and following rules or instructions; (2) the claims integrate the abstract idea into a practical application of improvements in estimating and providing a wait time for a query patient using a trained machine learning model; (3) the combination of applied prior art does not teach the user interface comprising a cellphone of the query patient via an email, automatic phone call, or SMS message; (4) the check-in disclosed in Larsen is not an a user cellphone.
In response to argument (1), the examiner acknowledges that the claims do not include recitations limiting the scope to human-to-human interaction. However, the question is whether the broadest reasonable interpretation to one of ordinary skill in the art of the recited limitations encompasses Certain Methods of Organizing Human Activity. As previously pointed out, MPEP 2106.04(a)(II)(C) describes certain subgroupings of managing personal behavior. Applicant objects to these examples because the pre-date the 2019 PEG. However, the 2019 PEG does not supersede the contents of the MPEP. The examiner maintains that examples of considering information while inputting data and a mental process that a neurologist should follow when testing a patient are applicable similar subgroupings for managing personal behavior. Further, MPEP 2106.04(a)(2)(II)(B) provides another similar example of using an algorithm for determining the optimal number of visits by a business representative to a client. The claims similarly recite an algorithm for estimating a wait time and providing that information to a business representative (a provider who will see the patient). Taken as a whole, the examiner respectfully maintains that the claim recitations encompass an abstract idea as previously set forth.
In response to argument (2), while providing improved wait time estimation may be a useful improvement for healthcare participants, this is not a technical solution that integrates the abstract idea into a practical application. The discussion of training and applying machine learning algorithms at pages 13-15 of the remarks is unrelated to the limitations recited in the claims. Claims 1, 16, and 20 recite “a deep learning model” being used for estimating patient-specific examination time durations. There is no further limitations on the recited “deep learning model.” There is no recitations directed to any type of training or processing involving the “deep learning model.” Therefore, arguments directed to “machine learning algorithms, particularly when trained on large datasets,” “convolutional neural networks (CNNs) involve millions of parameters and perform billions of calculations,” “the use of powerful GPUs or TPUs, distributed computing resources, and optimized libraries and frameworks,” “training a deep learning model involves iterating over large datasets, adjusting millions of parameters through gradient descents,” etc. are unpersuasive because they have not relationship to any limitations recited in the claims.
In response to argument (3), Weber teaches sending a push notification regarding wait time to a patient mobile phone (see paragraph 0041; current waiting time and option to reschedule is transmitted to patient interface via push notification; paragraph 0064; system may be implemented on a mobile phone). The disclosed patient mobile phone is encompassed by a cellphone of the query patient via a message. Therefore, the examiner respectfully maintains that this limitation encompasses the combination of applied teachings.
Applicant’s argument (4) has been fully considered but is moot in view of the new grounds of rejection set forth above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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, Robert Morgan can be reached on 571-272-6773. 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.
C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
Prosecution Insights