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 01/29/2026, the following has occurred: claims 1, 15, 25, and 31 have been amended. Now, claims 1-35 remain pending.
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.
Claims 15-24 are 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 recites “obtaining data comprising: a set of external data related to the attendees in the set of past appointment data structures.” The claim subsequently recites “an attendee” and “a set of past appointment data structures.” However, there is no previous recitation of “attendees” or “a set of past appointment data structures.” Therefore, the antecedent basis of “the attendees in the set of past appointment data structures” is unclear and indefinite.
Claims 16-24 are rejected based on their dependencies on claim 15.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-26 of U.S. Patent No. 12,002,575 in view of Vegas Santiago, US Patent Application Publication No. 2022/0335339.
Each of the limitations recited in claims 1-35 of the instant application are included in claims 1-26 of the ‘575 patent with the exception that the claims of the instant application include a probability and status of completion of the appointment instead of the probability and status of attendance of the appointment. However, this difference is only in how the appointment is labeled. For example, claim 20 of the instant application defines completion status of appointments as including “attending, no-show, late, rescheduled, converted to virtual visit, or canceled.” Similarly, claim 18 of the ‘575 patent defines attendance status as “attending, no-show, late, rescheduled, converted to virtual visit, or canceled.” Therefore, this difference does not patentably distinguish claims 1-35 of the instant application from 1-26 of the ‘575 patent.
Additionally, the claims differ in the recitation of after the appointment, obtaining appointment outcome data and updating the machine learning algorithm based on the appointment outcome data to improve further predictive performance of the machine learning algorithm. However, this recitation is taught by Vegas Santiago (see paragraph 0032; appointment-specific predictors (appointment outcome data) are used to update the AI model). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to add this recitation to claims 1-26 of the ‘575 patent with the motivation of providing AI model development with high accuracy (see paragraph 0032 of Vegas Santiago).
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 1-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-14 Step 2A Prong One
Claim 1 recites obtaining an appointment data structure, comprising an attendee and a corresponding appointment for the attendee; obtaining data comprising a set of external data and at least one of a set of population data related to the attendee, a set of appointment data related to the appointment, or environmental data related to the region around a location of the appointment or the attendee of a designated time; wherein the set of external data comprises at least two different formats; standardizing the at least two different formats of the set of external data; inferring, using the standardized data, a probability that the attendee will complete the appointment; when the probability of completion is below a threshold, performing a mitigation step to increase the probability that the attendee will complete the appointment; and after the appointment, obtaining appointment outcome data.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, the claims encompass manually obtaining data related to an appointment for an attendee, manually inferring the probability that the attendee will complete the appointment based on the data, and taking a manual mitigation step to increase the probability of completing the appointment if the probability is below a threshold. This could be carried out manually between individuals such as a patient with an upcoming appointment with a provider. Standardizing different formats of external data could encompass a manual process such as converting dates in different formats to a common format, such as MM/DD/YYYY. But for the recitation of generic computer components, such manual steps encompass Certain Methods of Organizing Human Activity.
Claims 2-14 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 further specify a time related to obtained environmental data. Claims 4-7 further define the environmental and external data. Claims 8-9 further include inferring communication preferences for the mitigation. Claims 10-11 further define the mitigation step. Claims 12-14 further defines the appointment data. But for the recitation of generic computer components, these limitations are directed to Certain Methods of Organizing Human Activity as explained above.
Claims 1-14 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 generally linking the abstract idea to a particular technological environment.
Claims 1-14, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “at least one processor; and at least one non-transitory memory including computer program code for one or more programs, the at least one non-transitory memory and the computer program code.” The written description discloses that the recited computer components encompass generic components including “those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like” (see paragraph 0032). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] 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.
Additionally, the claims recite performing the inferring “in a machine learning algorithm by the at least one processor and the at least one non-transitory memory” and “updating the machine learning algorithm based on the appointment outcome data to improve future predictive performance of the machine learning algorithm.” These broad recitations merely links the abstract idea to a particular technological environment and also do not integrate the abstract idea into a practical application. Although the recitations including “to improve future predictive performance of the machine learning algorithm,” this is merely an intended use of the recited step, which does not integrate the abstract idea into a practical application.
Furthermore, claims 9-11 include some additional elements, recited in the alternative, along with simply further expanding on the abstract ideas. Therefore, these claims also do not integrate the abstract idea into a practical application because they encompass an abstract idea.
Claims 1-14: 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.”). Additionally, simply using a “machine learning algorithm” merely links the abstract idea to a particular technological environment.
Generally linking the abstract idea to a particular technological environment 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.
Claims 15-24: Step 2A Prong One
Claim 15 recites obtaining data comprising: a set of external data related to the attendees in the set of past appointment data structures; and at least one of: a pending appointment data structure comprising an attendee and a corresponding appointment for the attendee; a set of appointment data related to the appointment; a set of past appointment data structures, each comprising an attendee identity, a past appointment, and a completion status of the past appointment; a set of regional environmental data related to a region around a location of the appointment or the attendees in the set of past appointment data structures from a time of each past appointment; or environmental data related to the region around the location of the appointment or the attendee in the pending appointment data structure at a designated time; wherein the set of external data comprises at least two different formats; standardizing the at least two different formats of the set of external data; inferring a probability that the attendee will complete the appointment; when the probability of completion is below a threshold, performing a mitigation step to increase the probability that each attendee will complete each corresponding appointment; and after the appointment, obtaining appointment outcome data.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, the claims encompass manually obtaining data related to an appointment for an attendee, manually inferring the probability that the attendee will complete the appointment based on the data, and taking a manual mitigation step to increase the probability of completing the appointment if the probability is below a threshold. This could be carried out manually between individuals such as a patient with an upcoming appointment with a provider. Standardizing different formats could encompass a manual process such as converting dates in different formats to a common format, such as MM/DD/YYYY. But for the recitation of generic computer components, such manual steps encompass Certain Methods of Organizing Human Activity.
Claims 16-24 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, claim 16 further encompasses manually recording post appointment attendance status. Claims 17-19 further encompass defining the obtained environmental data, the threshold, the mitigation, and the attendance data. Claim 21 and 23-24 further defines the appointment completion. But for the recitation of generic computer components, these limitations are directed to Certain Methods of Organizing Human Activity as explained above.
Claims 15-24: 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 generally linking the abstract idea to a particular technological environment.
Claims 15-24, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “at least one processor; and at least one non-transitory memory including computer program code for one or more programs, the at least one non-transitory memory and the computer program code.” The written description discloses that the recited computer components encompass generic components including “those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like” (see paragraph 0032). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] 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.
Additionally, the claims recite “training a machine learning model” and performing the inferring “in a machine learning algorithm by the at least one processor and the at least one non-transitory memory” and “updating the machine learning algorithm based on the appointment outcome data to improve future predictive performance of the machine learning algorithm” along with defining the machine learning algorithm as “a random forest, a decision tree, a gradient boosting machine, a support vector machine, a neural network or an ensemble method.” These broad recitations merely links the abstract idea to a particular technological environment and also do not integrate the abstract idea into a practical application. Although the recitations including “to improve future predictive performance of the machine learning algorithm,” this is merely an intended use of the recited step, which does not integrate the abstract idea into a practical application.
Furthermore, claim 19 include some additional elements, recited in the alternative, along with simply further expanding on the abstract ideas. Therefore, this claim also does not integrate the abstract idea into a practical application because it encompasses an abstract idea.
Claims 15-24: 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.”). Additionally, simply “training” and using a “machine learning algorithm” merely links the abstract idea to a particular technological environment.
Generally linking the abstract idea to a particular technological environment 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.
Claims 25-35: Step 2A Prong One
Claims 25 and 35 recites obtain data comprising: a set of external data; and at least one of: an appointment data structure, comprising an attendee and a corresponding appointment for the attendee; a set of EMR data related to the attendee; a set of population data related to the attendee; a set of appointment data related to the appointment; or environmental data related to the region around a location of the appointment or the attendee at a designated time; wherein the set of external data comprises at least two different formats; standardizing the at least two different formats of the set of external data; infer, using the standardized data, a probability that the attendee will complete the appointment; when the probability of completion is below a threshold, recommending a mitigation step to increase the probability that the attendee will complete the appointment; and after the appointment, obtaining appointment outcome data.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, the claims encompass manually obtaining data related to an appointment for an attendee, manually inferring the probability that the attendee will attend the appointment based on the data, and taking a manual mitigation step to increase the probability of attending the appointment if the probability is below a threshold. This could be carried out manually between individuals such as a patient with an upcoming appointment with a provider. Standardizing different formats could encompass a manual process such as converting dates in different formats to a common format, such as MM/DD/YYYY. But for the recitation of generic computer components, such manual steps encompass Certain Methods of Organizing Human Activity.
Claims 26-30 and 32-35 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 26-30 and 32-35 further define the obtained data and mitigation. But for the recitation of generic computer components, these limitations are directed to Certain Methods of Organizing Human Activity as explained above.
Claims 25-35: 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 generally linking the abstract idea to a particular technological environment.
Claims 21-28, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “a non-transitory computer-readable storage medium with instructions stored thereon, which when executed by a processor “ and “non-transitory computer-readable storage medium having stored thereon one or more program instructions which, when executed by one or more processors, cause an apparatus to at least perform the following operations.” The written description discloses that the recited computer components encompass generic components including “those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like” (see paragraph 0032). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] 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.
Additionally, the claims recite performing the inferring “using a machine learning algorithm,” “using a trained machine learning algorithm executed on the processor,” and “updating the machine learning algorithm based on the appointment outcome data to improve future predictive performance of the machine learning algorithm.” These broad recitations merely links the abstract idea to a particular technological environment and also do not integrate the abstract idea into a practical application. Although the recitations including “to improve future predictive performance of the machine learning algorithm,” this is merely an intended use of the recited step, which does not integrate the abstract idea into a practical application.
Claims 25-35: 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.”). Additionally, simply using a “machine learning algorithm” merely links the abstract idea to a particular technological environment.
Generally linking the abstract idea to a particular technological environment 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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 12-17, and 20-35 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Vegas Santiago, US Patent Application Publication No. 2022/0335339.
As per claim 1, Vegas Santiago teaches a computer-implemented method of increasing likelihood of appointment completion, comprising: providing at least one processor (see paragraph 0026; module, unit or system may include a computer processor); and at least one non-transitory memory including computer program code for one or more programs, the at least one non-transitory memory and the computer program code configured to, with the at least one processor (see paragraph 0026; computer memory with instructions), perform steps comprising: obtaining an appointment data structure, comprising an attendee and a corresponding appointment for the attendee (see paragraph 0031; describes appointment data and patient data for an appointment); obtaining data comprising a set of external data (see paragraphs 0031-0032; external data can encompass any other data set such as historical pattern data) and at least one of a set of population data related to the attendee (see paragraph 0048; models may be employed for patient population; paragraph 0038 also looks for same or similar patients); a set of appointment data related to the appointment (see paragraph 0032; examination parameters and appointment-specific predictors); environmental data related to the region around a location of the appointment or the attendee at a designated time (see paragraph 0034; collects weather forecast data that looks at time window around the time of the appointment); wherein the set of external data comprises at least two different formats (see paragraph 0039; describes obtaining EMR data in different formats such as RIS data); standardizing the at least two different formats of the set of external data (see paragraph 0039; the data can be cleaned, standardized, normalized, etc. and used to train models and generate predictions); inferring, using the standardized data, in a machine learning algorithm by the at least one processor and the at least one non-transitory memory, a probability that the attendee will complete the appointment (see paragraph 0035; machine learning algorithms use collected data to predict the likelihood of a patient no-show; a completion comprises a plurality of possible statuses including a no-show (see e.g. claim 20)); when the probability of completion is below a threshold, performing a mitigation step to increase the probability that the attendee will complete the appointment (see paragraph 0036; patient below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up); and after the appointment, obtaining appointment outcome data and updating the machine learning algorithm based on the appointment outcome data to improve further predictive performance of the machine learning algorithm (see paragraph 0032; appointment-specific predictors (appointment outcome data) are used to update the AI model).
As per claim 2, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further teaches the designated time is at least one day prior to a hour of the appointment (see paragraph 0034; looks at a 5 day window, which is encompassed by at least one hour prior to the appointment).
As per claim 3, Vegas Santiago teaches the method of claim 2 as described above. Vegas Santiago further teaches the designated time is selected from at least one month prior to the date of the appointment, at least one week prior to the date of the appointment, at least three days prior to the day of the appointment, or at least one day prior to the date of the appointment (see paragraph 0034; looks at a 5 day window, which is encompassed by at least three days and at least one day prior to the appointment).
As per claim 4, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further teaches the environmental data comprises weather data (see paragraph 0034; weather forecast data).
As per claim 12, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further teaches the appointment data comprises data selected from a provider of the appointment, a nature of the appointment, one or more conditions the attendee holds, one or more diagnoses the attendee is currently being treated for, whether the appointment is telemedicine or in-person, or whether the appointment is an initial consultation or a second opinion (see paragraph 0046; patient condition among other data).
As per claim 13, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific time (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 14, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further the appointment is a commitment at a specific place (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 15, Vegas Santiago teaches a computer-implemented method of increasing likelihood of appointment completion, comprising: providing at least one processor (see paragraph 0026; module, unit or system may include a computer processor); and at least one non-transitory memory including computer program code for one or more programs, the at least one non-transitory memory and the computer program code configured to, with the at least one processor (see paragraph 0026; computer memory with instructions), perform steps comprising: obtaining data comprising: a set of external data related to the attendees in the set of past appointment data structures (see paragraphs 0031-0032; age, gender, etc. for historical appointment now-shows); and at least one of: a pending appointment data structure comprising an attendee and a corresponding appointment for the attendee (see paragraph 0031; describes appointment data and patient data for an appointment); a set of appointment data related to the appointment (see paragraph 0032; examination parameters and appointment-specific predictors); a set of past appointment data structures, each comprising an attendee identity, a past appointment, and a completion status of the past appointment (see paragraph 0032; describes historic patient no-shows for past appointments; a completion comprises a plurality of possible statuses including a no-show (see e.g. claim 20)); a set of regional environmental data related to a region around a location of the appointment or the attendees in the set of past appointment data structures from a time of each past appointment (see paragraph 0031; leverages historical weather data); or environmental data related to the region around the location of the appointment or the attendee in the pending appointment data structure at a designated time (see paragraph 0034; collects weather forecast data that looks at time window around the time of the appointment); wherein the set of external data comprises at least two different formats (see paragraph 0039; describes obtaining EMR data in different formats such as RIS data); standardizing the at least two different formats of the set of external data (see paragraph 0039; the data can be cleaned, standardized, normalized, etc. and used to train models and generate predictions); training a machine learning model with the standardized data (see paragraph 0032; historical data used to train AI model); inferring, using the machine learning algorithm, a probability that the attendee will complete the appointment (see paragraph 0035; machine learning algorithms use collected data to predict the likelihood of a patient no-show); when the probability of completion is below a threshold, performing a mitigation step to increase the probability that each attendee will complete each corresponding appointment (see paragraph 0036; patient below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up); and after the appointment, obtaining appointment outcome data and updating the machine learning algorithm based on the appointment outcome data to improve further predictive performance of the machine learning algorithm (see paragraph 0032; appointment-specific predictors (appointment outcome data) are used to update the AI model).
As per claim 16, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches after the appointment in the pending appointment data structure has passed, recording a completion status of the attendee at the appointment (see paragraph 0080; monitors patient shows and no-shows); and further training the machine learning model with the pending appointment data structure and the completion status (see paragraph 0080; monitored shows/no-shows used in training the machine learning model).
As per claim 17, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the environmental data and the regional environmental data comprise weather data (see paragraph 0034; weather forecast data).
As per claim 20, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the completion status of the past appointment in the appointment data structures is selected from attending, no-show, late, rescheduled, converted to virtual visit, or cancelled visit (see paragraph 0080; monitors patient shows and no-shows).
As per claim 21, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the completion status of the past appointment in the appointment data structures is selected from completed or incomplete (see paragraph 0032; describes historic patient no-shows for past appointments; a completion comprises a plurality of possible statuses including a no-show (see e.g. claim 20) being an incomplete appointment).
As per claim 22, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the machine learning algorithm is a random forest (see paragraph 0031; describes random forest).
As per claim 23, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific time (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 24, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific place (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 25, Vegas Santiago teaches a system for increasing likelihood of appointment completion, comprising: a non-transitory computer-readable storage medium with instructions stored thereon, which when executed by a processor (see paragraph 0026; computer memory with instructions), perform steps comprising: obtaining data comprising: a set of external data (see paragraphs 0031-0032; external data can encompass any other data set such as historical pattern data); and at least one of: an appointment data structure, comprising an attendee and a corresponding appointment for the attendee (see paragraph 0031; describes appointment data and patient data for an appointment); a set of electronic medical record (EMR) data related to the attendee (see paragraph 0031; describes data collected from an EMR); a set of data related to the appointment (see paragraph 0032; examination parameters and appointment-specific predictors); or environmental data related to the region around a location of the appointment or the attendee at a designated time (see paragraph 0034; collects weather forecast data that looks at time window around the time of the appointment); wherein the set of external data comprises at least two different formats (see paragraph 0039; describes obtaining EMR data in different formats such as RIS data); standardizing the at least two different formats of the set of external data (see paragraph 0039; the data can be cleaned, standardized, normalized, etc. and used to train models and generate predictions); inferring, using the standardized data, a probability that the attendee will complete the appointment (see paragraph 0035; machine learning algorithms use collected data to predict the likelihood of a patient no-show); when the probability of completion is below a threshold, recommending a mitigation step to increase the probability that the attendee will complete the appointment (see paragraph 0036; patient below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up); and after the appointment, obtaining appointment outcome data and updating the machine learning algorithm based on the appointment outcome data to improve further predictive performance of the machine learning algorithm (see paragraph 0032; appointment-specific predictors (appointment outcome data) are used to update the AI model).
As per claim 26, Vegas Santiago teaches the system of claim 25 as described above. Vegas Santiago further teaches inferring the probability that the attendee will complete the appointment using a trained machine learning algorithm executed on the processor (see paragraph 0035; machine learning algorithms use collected data to predict the likelihood of a patient no-show (the inverse of which is likelihood of the patient showing for the appointment)).
As per claim 27, Vegas Santiago teaches the system of claim 25 as described above. Vegas Santiago further teaches the non-transitory computer-readable medium further comprises at least a portion of the database of an EMR system comprising the EMR data (see paragraph 0060; data sources in architecture include EMR).
As per claim 28, Vegas Santiago teaches the system of claim 25 as described above. Vegas Santiago further teaches performing the mitigation step, the mitigation step comprising sending one computer-generated electronic communication message to the attendee (see paragraph 0036; patient below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up).
As per claim 29, Vegas Santiago teaches the system of claim 25 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific time (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 30, Vegas Santiago teaches the system of claim 25 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific place (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 31, Vegas Santiago teaches a non-transitory computer-readable storage medium having stored thereon one or more program instructions which, when executed by one or more processors (see paragraph 0026; computer memory with instructions), cause an apparatus to at least perform the following operations: obtain data comprising: a set of external data (see paragraphs 0031-0032; external data can encompass any other data set such as historical pattern data); and at least one of: an appointment data structure, comprising an attendee and a corresponding appointment for the attendee (see paragraph 0031; describes appointment data and patient data for an appointment); a set of EMR data related to the attendee (see paragraph 0031; describes data collected from an EMR)); a set of population data related to the attendee (see paragraph 0048; models may be employed for patient population; paragraph 0038 also looks for same or similar patients); a set of appointment data related to the appointment (see paragraph 0032; examination parameters and appointment-specific predictors); or environmental data related to the region around a location of the appointment or the attendee at a designated time (see paragraph 0034; collects weather forecast data that looks at time window around the time of the appointment); wherein the set of external data comprises at least two different formats (see paragraph 0039; describes obtaining EMR data in different formats such as RIS data); standardizing the at least two different formats of the set of external data (see paragraph 0039; the data can be cleaned, standardized, normalized, etc. and used to train models and generate predictions); infer, using the standardized data in a machine learning algorithm, a probability that the attendee will complete the appointment (see paragraph 0035; machine learning algorithms use collected data to predict the likelihood of a patient no-show); when the probability of completion is below a threshold, recommending a mitigation step to increase the probability that the attendee will complete the appointment (see paragraph 0036; patient below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up); and after the appointment, obtaining appointment outcome data and updating the machine learning algorithm based on the appointment outcome data to improve further predictive performance of the machine learning algorithm (see paragraph 0032; appointment-specific predictors (appointment outcome data) are used to update the AI model).
As per claim 32, Vegas Santiago teaches the computer readable storage medium of claim 31 as described above. Vegas Santiago further teaches at least one database including at least a subset of the population data, the appointment data, the external data, and the environmental data, or the EMR data stored thereon (see paragraph 0049; data from which prediction is made comes from database).
As per claim 33, Vegas Santiago teaches the computer readable storage medium of claim 31 as described above. Vegas Santiago further teaches the program instructions further configured to cause the apparatus to perform at least part of the mitigation step via an automated process (see paragraph 0067; processes can be automatically scheduled).
As per claim 34, Vegas Santiago teaches the computer readable storage medium of claim 31 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific time (see paragraph 0031; appointment is for a specific date/time, location, etc.).
As per claim 35, Vegas Santiago teaches the computer readable storage medium of claim 31 as described above. Vegas Santiago further teaches the appointment is a commitment at a specific place (see paragraph 0031; appointment is for a specific date/time, location, etc.).
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.
Claim(s) 5-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vegas Santiago, US Patent Application Publication No. 2002/0335339 in view of Batey, US Patent Application Publication No. 2019/0019163.
As per claim 5, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago does not explicitly teach the external data comprises economic data. Batey teaches obtaining external data comprising economic data (see paragraph 0048; location and average patient income level) and predicting patient attendance (see paragraph 0052; predicts characteristics including appointment attendance). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include economic data in the prediction modeling of Vegas Santiago with the motivation of improving clustering of similar patients (see paragraph 0020) as both relate to communicating with patients having appointments.
As per claim 6, Vegas Santiago and Batey teaches the method of claim 5 as described above. As noted above, Vegas Santiago does not explicitly teach the economic data. Batey further teaches the economic data comprises economic data related to the region around the attendee (see paragraph 0048; location and average patient income level). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include economic data in the prediction modeling of Vegas Santiago with the motivation of improving clustering of similar patients (see paragraph 0020) as both relate to communicating with patients having appointments.
As per claim 7, Vegas Santiago and Batey teaches the method of claim 5 as described above. As noted above, Vegas Santiago does not explicitly teach the economic data comprises global or national economic data. Batey further teaches the economic data comprises global or national economic data (see paragraph 0057; clustered patient income data based on geographic location is encompassed by global or national economic data). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include economic data in the prediction modeling of Vegas Santiago with the motivation of improving clustering of similar patients (see paragraph 0020) as both relate to communicating with patients having appointments.
As per claim 8, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago further teaches inferring a communication to the attendee (see paragraph 0036; patient inferred to be below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up). Vegas Santiago does not explicitly teach that the communication is a communication preference of the attendee. Batey teaches using communication preferences for an attendee of an appointment (see paragraph 0022; describes various communication channels and patient preferences of certain channels). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include patient preferences for the mitigating communication of Vegas Santiago with the motivation of ensuring that patients are responsive to the communication (see paragraph 0022 of Batey).
As per claim 9, Vegas Santiago and Batey teaches the method of claim 8 as described above. Vegas Santiago further teaches the mitigation step comprises a communication step (see paragraph 0036; patient inferred to be below a certain percentage level can have a mitigation step performed such as follow-up to confirm showing up). As noted above, Vegas Santiago does not explicitly teach the communication preferences. Batey further teaches a communication step selected from written, telephonic, text message, or electronic mail communication based on communication preferences (see paragraph 0022; describes various communication channels and patient preferences of certain channels). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include patient preferences for the mitigating communication of Vegas Santiago with the motivation of ensuring that patients are responsive to the communication (see paragraph 0022 of Batey).
Claim(s) 10 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vegas Santiago, US Patent Application Publication No. 2002/0335339 in view of Cinnor, US Patent Application Publication No. 2020/0151634.
As per claim 10, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago does not explicitly teach the mitigation step comprises providing transportation assistance to the attendee, the transportation assistance selected from transportation maps, a ride sharing credit, a public transportation credit, an ambulette dispatch, or an ambulance dispatch. Cinnor teaches providing a mitigation to avoid patient no-show to an appointment selected from transportation maps, a ride sharing credit, a public transportation credit, an ambulette dispatch, or an ambulance dispatch (see paragraph 0032; Uber/Lyft are examples of ride sharing services; paragraph 0034 describes avoiding appointment no-show). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include travel assistance as a mitigation for the patient of Vegas Santiago with the motivation of addressing further options for addressing patient no-shows (see paragraph 0032 of Cinnor).
As per claim 18, Vegas Santiago teaches the method of claim 15 as described above. Vegas Santiago does not explicitly teach inferring whether a mobility factor selected from age, disability status, or travel time is contributive above a predetermined threshold. Cinnor teaches inferring whether a mobility factor selected from age, disability status, or travel time is contributive above a predetermined threshold (see paragraph 0031; identifies including transportation options and age in contributing to now-show; paragraph 0034 identifies adjustable threshold for no-show). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include travel assistance as a mitigation for the patient of Vegas Santiago with the motivation of addressing further options for addressing patient no-shows (see paragraph 0032 of Cinnor).
As per claim 19, Vegas Santiago and Cinnor teaches the method of claim 18 as described above. Vegas Santiago further teaches the mitigation step is implemented based on a predetermined threshold (see paragraph 0036). Vegas Santiago does not explicitly teach the mitigation step comprises providing transportation assistance to the attendee, the transportation assistance selected from transportation maps, a ride sharing credit, a public transportation credit, an ambulance dispatch, or an ambulette dispatch. Cinnor further teaches the mitigation step comprises providing transportation assistance to the attendee, the transportation assistance selected from transportation maps, a ride sharing credit, a public transportation credit, an ambulance dispatch, or an ambulette dispatch see paragraph 0032; Uber/Lyft are examples of ride sharing services; paragraph 0034 describes avoiding appointment no-show). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include travel assistance as a mitigation for the patient of Vegas Santiago with the motivation of addressing further options for addressing patient no-shows (see paragraph 0032 of Cinnor).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vegas Santiago, US Patent Application Publication No. 2002/0335339 in view of Peltz, US Patent Application Publication No. 2021/0090031.
As per claim 11, Vegas Santiago teaches the method of claim 1 as described above. Vegas Santiago does not explicitly teach the mitigation step comprises converting the appointment to a virtual appointment or changing a location of the appointment to a home or place of business of the attendee. Peltz teaches a mitigation step for a patient appointment comprises converting the appointment to a virtual appointment or changing a location of the appointment to a home or place of business of the attendee (see paragraph 0040; converts patient’s appointment to a telehealth appointment). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to include appointment conversion as a mitigation for the patient of Vegas Santiago with the motivation of providing a means for salvaging a scheduled appointment (see paragraph 0040 of Peltz).
Response to Arguments
In the remarks filed 01/29/2026, Applicant argues (1) the amended claims are patentable over claims 1-26 of US Patent 12,002,575; (2) the claims provide a technical improvement by acquiring data from multiple, heterogenous sources in different machine-readable formats, standardize the different formats into a form suitable for machine-learning; (3) Vegas Santiago does not teach a feedback system that uses appointment outcomes for improving future predictions.
Applicant’s argument (1) has been fully considered but is moot in view of the new grounds of rejection set forth above.
In response to argument (2), representative claim 1 recites obtaining 1) “a set of external data” and 2) “at least one of” population, appointment, or environmental data. Only “the set of external data” includes two different formats, which are then standardized. Finally, the standardized data is used in “a machine learning algorithm” during an “inferring process.” Therefore, different sources of data are not defined in terms of their disparate data formats, machine-readable or otherwise. Furthermore, the claim does not include any detail regarding “using the standardized data in a machine learning algorithm” and how it relates “inferring…a probability that the attendee will complete the appointment.” The examiner respectfully submits that this argument is not commensurate in scope with the claim limitations. As a result, the steps of obtaining “at least two different formats” and “standardizing” the different formats are part of the abstract idea as explained in the above updated rejection.
In response to argument (3), the examiner has cited updated portions of Vegas Santiago related to the amendment limitations. In particular, Vegas Santiago teaches “appointment-specific predictors” (appointment outcomes) are used to “re-train/update” the “AI-model and conduct inferencing with the AI model” (see paragraph 0032) The recitation of using this updating “to improve future predictive performance” is an intended use of the “updating.” However, Vegas Santiago does state that this process “provide AI model development with high accuracy” (see paragraph 0032). Therefore, the examiner respectfully maintains that the teachings of Vegas Santiago are encompassed by the amended claim language.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Diwan, US Patent Application Publication No. 2024/0296939, discloses retraining a model by updating model parameters based on appointment schedule recommendations, and using the updated model for future predictions made by the model.
Valero-Bover et al., Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment, discloses predictive modeling of appointment attendance and continuously retraining the predictive model.
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.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683