DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
This communication is in response to application filed 9/11/2024. It is noted that application is a continuation of 18/641,464 filed 04/22/2024 and claims priority to provisional application 63/537,554 filed 09/11/2023. Claims 1-20 are pending.
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 1-12 are directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 1-12 are drawn to software per se. Software per se (i.e., telephony layer and middleware layer) intrinsically require no tangible physical structure, thus do not constitute tangible physical articles or other forms of matter. Therefore, software per se is not considered to be statutory subject matter. To be statutory, a computer program must be coupled with or combined with some structural recitations. (MPEP 2106.03(I)).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-12 and 19-20 are drawn to an AI system for scheduling appointments, which is within the four statutory categories (i.e. machine). Claims 13-18 are drawn to an computer program product for scheduling appointments, which is within the four statutory categories (i.e. article of manufacture).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. An AI system for scheduling appointments, the AI system comprising:
a telephony layer that is configured to:
receive a voice input from a caller; and
compute an intent of the caller based on the received voice input; and
a middleware layer that is configured to formulate a set of available slots for the caller based on the intent.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to certain methods of organizing human activity – managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
The limitations of receiving a voice input from a caller; computing a first intent from the voice input and formulate a set of available slots based on the intent as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is a method directed to scheduling appointments based on the intent of a caller. This is a method of managing interactions between a receptionist/scheduler and a caller (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions -- see MPEP § 2106.04(a)(2), subsection II). That is other than reciting “artificial intelligence” and “computer processor” language, nothing in the claim element precludes the steps from practically being performed between people or by a person for scheduling appointments. If a claim limitation, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
1. An AI system for scheduling appointments, the AI system comprising:
a telephony layer that is configured to:
receive a voice input from a caller; and
compute an intent of the caller based on the received voice input; and
a middleware layer that is configured to formulate a set of available slots for the caller based on the intent.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitation of an artificial intelligence system recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer.
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– for example, the recitation of telephony and middleware layer merely limits the abstract idea the environment of software on a computer.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.”
Therefore, the additional elements do not add significantly more to the at least one abstract idea.
Independent claims 13 and 19 are directed to certain methods of organizing human activity for similar reasons as claim 1. Furthermore, for similar reasons as representative independent claim 1, analogous independent claims 13 and 19 do not recite additional elements that integrate the judicial exception into a practical application nor add significantly more.
The following dependent claims further the define the abstract idea or are also directed to an abstract idea itself:
Dependent claims 3, 5, 11, and 15 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract).
In relation to claims 7, 8, 9, 10 and 14 these claims specify selecting the set of available slots and filtering the available slots which are certain methods of organizing human activity, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 6, 12, 16, 17 and 20: These claims specify a natural language statement; a web portal with a user interface; dynamically determining; and an administrative portal which thus does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(e)).
Claims 2, 4 and 18: These claims recite a scheduling algorithm and a machine learning algorithm which thus amount to mere instructions to apply an exception by invoking the computer as a tool OR reciting the idea of a solution (i.e. claim fails to recite details of how a solution to a problem is accomplished) or outcome (see MPEP § 2106.05(f)).
The dependent claims further do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava (9,742,912) in view of Rana (8,364,501).
AS per claim 1, Srivastava teaches an AI system for scheduling appointments, the AI system comprising:
a telephony layer that is configured to:
receive a voice input from a caller (Srivastava; Col. 2, lines 56-63)); and
compute an intent of the caller based on the received voice input (Srivastava; Col. 2, lines 61-67; Col. 3, lines 38-42).
Srivastava does not expressly teach a middleware layer that is configured to formulate a set of available slots for the caller based on the intent. However, this is old and well-known in the art as evidenced by Rana. In particular, Rana teaches appointment slot formulation (Rana; Col. 2, lines 14-15 presents schedule options at the interface terminal based on the identified medical services and resources). It would have been obvious to one of ordinary skill in the art to add to the electronic appointment scheduling system of Rana the Interactive Voice Response technology system of Srivastava with the motivation of automatically presenting appointment slots and improving patient scheduling efficiency.
AS per claim 2, Srivastava in view of Rana teaches the AI system of claim 1, wherein the middleware layer is configured to utilize a scheduling algorithm to formulate the set of available slots (Rana; 5, lines 52-53 A listing of combinations may be generated and stored in tables in a database 62 associated with EMR network 19).
AS per claim 3, Srivastava in view of Rana teaches the system of claim 2, wherein the scheduling algorithm is customizable for a target medical office (Rana; Col. 5, lines 65-67 Additionally, the combinations stored in database 62 may be configured to be modifiable by healthcare provider institutions, individual practitioners, etc).
AS per claim 4, Srivastava teaches the AI system of claim 1 wherein the set of available slots is formulated by a machine learning algorithm (Srivastava; Col. 3, lines 33-44).
AS per claim 5, Srivastava teaches the AI system of claim 4, wherein the machine learning algorithm is trained using historical scheduling data associated with the target medical office (Srivastava; Col. 3, lines 33-44).
AS per claim 6, Srivastava teaches the AI system of claim 1 wherein the telephony layer is configured to receive the voice input from the caller as a natural language statement (Srivastava; Col. 2, line 58).
AS per claim 7, Srivastava in view of Rana teaches the AI system of claim 1 wherein:
the intent of the caller includes at least one appointment selection parameter (Rana; Col. 7, lines 62-66 the user is given the option of selecting either a location for the procedure or a medical provider, and in process block 90 resources are filtered by the scheduler 70 based on whether the patient would prefer a specific practitioner or a specific location.); and
the middleware layer is configured to select the set of available slots such that each member of the set matches at least one appointment selection parameter (Rana; Col. 8, lines 4-10 In process block 92, the scheduler system 70 also requests a proposed time frame for scheduling the procedure from the patient 30, and then accesses calendars or schedules in resource database 76 for the required resources for each step in the process. In process block 94, the scheduler 70 identifies common schedule openings between the resources required within the time frame specified by the patient).
AS per claim 8, Srivastava in view of Rana teaches the AI system of claim 1 wherein:
the intent of the caller includes at least one appointment selection parameter (Rana; Col. 7, lines 62-66 the user is given the option of selecting either a location for the procedure or a medical provider, and in process block 90 resources are filtered by the scheduler 70 based on whether the patient would prefer a specific practitioner or a specific location); and
the middleware layer is configured to select the set of available slots such that each member of the set is linked to at least one appointment selection parameter (Rana; Col. 8, lines 4-10 In process block 92, the scheduler system 70 also requests a proposed time frame for scheduling the procedure from the patient 30, and then accesses calendars or schedules in resource database 76 for the required resources for each step in the process. In process block 94, the scheduler 70 identifies common schedule openings between the resources required within the time frame specified by the patient).
AS per claim 9, Srivastava in view of Rana teaches the AI system of claim 1 wherein: the intent of the caller includes an appointment selection parameter (Rana; Col. 7, lines 62-66 the user is given the option of selecting either a location for the procedure or a medical provider, and in process block 90 resources are filtered by the scheduler 70 based on whether the patient would prefer a specific practitioner or a specific location); and
the middleware layer is configured to select the set of available slots such that each member of the set is associated with the appointment selection parameter (Rana; Col. 8, lines 4-10 In process block 92, the scheduler system 70 also requests a proposed time frame for scheduling the procedure from the patient 30, and then accesses calendars or schedules in resource database 76 for the required resources for each step in the process. In process block 94, the scheduler 70 identifies common schedule openings between the resources required within the time frame specified by the patient).
AS per claim 10, Srivastava in view of Rana teaches the AI system of claim 1 wherein:
the intent of the caller includes a plurality of appointment selection parameters (Rana; Col. 7, lines 62-66 the user is given the option of selecting either a location for the procedure or a medical provider, and in process block 90 resources are filtered by the scheduler 70 based on whether the patient would prefer a specific practitioner or a specific location); and
the middleware layer is configured to select the set of available slots such that each member of the set is associated with at least one of the plurality of appointment selection parameters (Rana; Col. 8, lines 4-10 In process block 92, the scheduler system 70 also requests a proposed time frame for scheduling the procedure from the patient 30, and then accesses calendars or schedules in resource database 76 for the required resources for each step in the process. In process block 94, the scheduler 70 identifies common schedule openings between the resources required within the time frame specified by the patient)..
AS per claim 11, Srivastava in view of Rana teaches the AI system of claim 10, wherein the middleware layer is configurable such that:
different weights are assignable to each of the plurality of appointment selection parameters; and the set of appointment slots is determined based on filtering available slots using the different weights (Rana; Col. 5, lines 65-67 Additionally, the combinations stored in database 62 may be configured to be modifiable by healthcare provider institutions, individual practitioners, etc – “modifiable” reads on “assigning different weights…”).
AS per claim 12, Srivastava in view of Rana teaches the AI system of claim 10, further comprising a web portal that provides a user interface for assigning the different weights to each of the plurality of appointment selection parameters (Rana; Col. 4, lines 35-36 The patient communication channel 14 may join the interface module 12 to a web server 22).
Claim 13 repeats substantially similar limitations as claims 1, 6 and 7 and the reasons for rejection are incorporated herein. As per the further features, Srivastava in view of Rana teaches in response to detecting that the caller requested an appointment scheduling service, communicates with an electronic health record (“EHR”) system (Rana; Col. 3, lines 14-20 a system for scheduling patient appointments is provided which includes a patient interface terminal and a computer system scheduler communicatively coupled to the patient interface terminal and to a medical record database which includes medical data for specific patients) to provide the requested appointment scheduling service to the caller during the telephone call.
AS per claim 14, Srivastava in view of Rana teaches the computer program of claim 13 further comprising instructions, that when executed by the processor:
filter a set of appointment slots; and generate a subset of the
appointment slots (Rana; Col. 3, lines 20-27 The computer system is adapted to receive data from the patient interface terminal to identify a patient making a request, and to filter medical services available to the patient based on the patient identity. By identifying the patient, it is possible to filter healthcare services, information, and data based on the age, sender, or history of the patient).
AS per claim 15, Srivastava in view of Rana teaches the computer program of claim 14 further comprising instructions, that when executed by the processor filter the set of appointment slots based on a set of configurable appointment selection parameters (Rana; Col. 5, lines 34-38 Interface module 12 may further include a specialized care module 50 configured to compare a reason received from the patient 30 with data from the EMR 18 to determine if the received reason/EMR data combination indicates that the patient 30 needs specialized care and/or scheduling.)
AS per claim 16, Srivastava in view of Rana teaches the computer program of claim 15 further comprising instructions, that when executed by the processor dynamically determine the set of configurable appointment selection parameters (Srivastava; Col. 3, lines 40-45 – dynamic determinations made through machine learning and predictive models).
AS per claim 17, Srivastava in view of Rana teaches the computer program of claim 15 further comprising instructions, that when executed by the processor dynamically determine the set of configurable appointment selection parameters based on the caller’s previously scheduled appointments (Rana; Col. 7, lines 5-6; Col. 6, lines 42-47).
AS per claim 18, Srivastava in view of Rana teaches the computer program of claim 15 further comprising instructions, that when executed by the processor dynamically determine the set of configurable appointment selection parameters using a machine learning algorithm (Srivastava; Col. 3, lines 40-45 – dynamic determinations made through machine learning and predictive models).
Claim 19 teaches substantially similar limitations as claims 1, 13 and 14 and the reasons for rejection are incorporated herein. Srivastavain in view of Rana further teaches the system comprising a middleware layer configured to: schedule a target appointment slot for the caller based on a second voice input received from the caller by the AI2VR system (Srivastava; Col. 3, lines 1-16 teaches identifying a plurality of features from the voice response which reads on a “second input”). Rana teaches appointment slot formulation (Rana; Col. 2, lines 14-15 presents schedule options at the interface terminal based on the identified medical services and resources). It would have been obvious to one of ordinary skill in the art to add to the electronic appointment scheduling system of Rana the Interactive Voice Response technology system of Srivastava with the motivation of automatically presenting appointment slots and improving patient scheduling efficiency.
As per claim 20, Rana teaches the automated system of claim 19 further comprising an administrative portal that provides a user interface for configuring the set of filtering rules (Rana; Col. 4, lines 35-36 The patient communication channel 14 may join the interface module 12 to a web server 22).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vyas et al. (Sonali Vyas & Deepshikha Bhargava. Algorithms and Software for Smart Health. In: Smart Health Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-4201-2_4. Pgs. 27-47. 2021) the closest nonpatent literature of record teaches IVR and using technology in telehealth apps to schedule appointments.
Balwani (WO-2015035309-A1) the closest foreign prior art of record teaches appointment scheduling and filtering by parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 11/1/2025