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 .
Claims 1-20 are pending in this application.
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,282,927. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of US Pat. No. 12,282,927 anticipate or render obvious claims 1-20 of the instant application as set forth in the table below.
Instant Application
US Pat No. 12,282,927
1. A method comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
1. A method comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
accessing, by the employer database system, information associated with an employee;
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to produce translated employee information; and
provisioning, by the employer database system, one or more user accounts for the employee by providing a portion of the translated employee information to one or more third-party system APIs.
2. The method of claim 1, wherein a user account comprises a cloud services account operated by a third-party system.
2. The method of claim 1, wherein a user account comprises a cloud services account operated by a third-party system.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the machine-learned model accesses the identified additional information from a source external to the central database system
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the central database system is configured to request the identified additional information from the employer.
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
9. The method of claim 1, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
10. An employer database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
10. An employer database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
accessing, by the employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
accessing, by the employer database system, information associated with an employee;
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to produce translated employee information; and
provisioning, by the employer database system, one or more user accounts for the employee by providing a portion of the translated employee information to one or more third-party system APIs.
11. The system of claim 10, wherein a user account comprises a cloud services account operated by a third-party system.
11. The system of claim 10, wherein a user account comprises a cloud services account operated by a third-party system.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and wherein the central database system is configured to request the identified additional information from the employer.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and wherein the central database system is configured to request the identified additional information from the employer.
18. The system of claim 10, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
18. The system of claim 10, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
accessing, by the employer database system, information associated with an employee;
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to produce translated employee information; and
provisioning, by the employer database system, one or more user accounts for the employee by providing a portion of the translated employee information to one or more third-party system APIs.
20. The non-transitory computer-readable storage medium of claim 19, wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
20. The non-transitory computer-readable storage medium of claim 19, wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,026,726. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of US Pat. No. 12,026,726 anticipate or render obvious the features of the claims of the instant application as set forth below.
Instant Application
US Pat. No. 12,026,726
1. A method comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
(Training is interpreted as requiring accessing the model and employee information in the database.)
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information (Translating employee information requires accessing it); and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
2. The method of claim 1, wherein a user account comprises a cloud services account operated by a third-party system.
2. The method of claim 1, wherein the user account comprises a cloud services account operated by the third-party system.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
herein the machine-learned model accesses the identified additional information from a source external to the central database system.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the central database system is configured to request the identified additional information from the employer.
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information; and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
10. An employer database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
10. A central database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training, by the central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
(Training is interpreted as requiring accessing the model and employee information in the database.)
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information (Translating employee information requires accessing it); and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
11. The system of claim 10, wherein a user account comprises a cloud services account operated by a third-party system.
11. The system of claim 10, wherein the user account comprises a cloud services account operated by the third-party system.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and wherein the central database system is configured to request the identified additional information from the employer.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
18. The system of claim 10, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
10. A central database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training, by the central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information; and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes a central database system to perform steps comprising:
training, by the central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
(Training is interpreted as requiring accessing the model and employee information in the database.)
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information (Translating employee information requires accessing it); and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
20. The non-transitory computer-readable storage medium of claim 19, wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
20. The non-transitory computer-readable storage medium of claim 19, wherein the machine-learned model is configured to translate the information before receiving a request from the employee to provision the user account.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,694,214. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of US Pat. No. 11,694,214 render anticipate or obvious the features of the claims of the instant application as set forth below.
Instant Application
US Pat. No. 11,694,214
1. A method comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems;
(Training is interpreted as requiring accessing the model and employee information in the database.)
accessing, by the central database system, a third-party system API for use in provisioning a user account;
translating, by the central database system, information associated with an employee using the machine learned model configured to convert the information based on information requirements associated with the accessed API (Translating employee information requires accessing it);
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
2. The method of claim 1, wherein a user account comprises a cloud services account operated by a third-party system.
2. The method of claim 1, wherein the user account comprises a cloud services account operated by the third-party system.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
3. The method of claim 1, wherein the machine learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
5. The method of claim 1, wherein the machine learned model is trained to identify one or more information translation operations performed during the historical provisioning.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
6. The method of claim 1, wherein the machine learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
herein the machine-learned model accesses the identified additional information from a source external to the central database system.
7. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the central database system is configured to request the identified additional information from the employer.
8. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and
wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
1. A method comprising:
…
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
10. An employer database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs.
10. A system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems (Training is interpreted as requiring accessing the model and employee information in the database.);
accessing, by the central database system, a third-party system API for use in provisioning a user account;
translating, by the central database system, information associated with an employee using the machine learned model configured to convert the information based on information requirements associated with the accessed API (Translating employee information requires accessing it);
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
11. The system of claim 10, wherein a user account comprises a cloud services account operated by a third-party system.
11. The system of claim 10, wherein the user account comprises a cloud services account operated by the third-party system.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
12. The system of claim 10, wherein the machine learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
14. The system of claim 10, wherein the machine learned model is trained to identify one or more information translation operations performed during the historical provisioning.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
15. The system of claim 10, wherein the machine learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
16. The system of claim 10, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine learned model accesses the identified additional information from a source external to the central database system.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and wherein the central database system is configured to request the identified additional information from the employer.
17. The system of claim 10, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
18. The system of claim 10, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
10. A system comprising:
…
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes the hardware processor to perform steps comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems (Training is interpreted as requiring accessing the model and employee information in the database.);
accessing, by the central database system, a third-party system API for use in provisioning a user account;
translating, by the central database system, information associated with an employee using the machine learned model configured to convert the information based on information requirements associated with the accessed API (Translating employee information requires accessing it);
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
20. The non-transitory computer-readable storage medium of claim 19, wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
18. The system of claim 10, wherein the machine learned model is configured to translate the information before receiving a request from the employee to provision the user account.
Because the system of claim 10 includes a medium like that claimed in claim 19, it would have been obvious to a person having ordinary skill in the art to recognize that the system of claim 18 includes a medium with instructions, and to thus modify the medium of claim 19 to also include the instructions to implement the same functionality as is done with the medium utilized in the system of claim 18.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over 1-20 of U.S. Patent No. 11,363,323. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of US Pat. No. 11,363,323 anticipate or render obvious the features of the claims of the instant application as set forth below.
Instant Application
US Pat. No. 11,363,323
1. A method comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of historical provisioning of user accounts with one or more third-party systems (Training is interpreted as requiring accessing the model and user account information in the database.);
receiving, by the central database system (the central database being analogous to an employer database), information associated with an employee from an employer;
accessing, by the central database system, a third-party system API for use in provisioning a user account;
translating, by the central database system, the received information using the machine learned model, the machine-learned model trained to identify information requirements associated with the accessed API and to convert the received information for entry into one or more fields of the API based on the identified information requirements (Translating employee information requires accessing it);
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated received information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
2. The method of claim 1, wherein a user account comprises a cloud services account operated by a third-party system.
2. The method of claim 1, wherein the user account comprises a cloud services account operated by the third-party system.
3. The method of claim 1, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
3. The method of claim 1, wherein the machine learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
5. The method of claim 1, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
5. The method of claim 1, wherein the machine learned model is trained to identify one or more information translation operations performed during the historical provisioning.
6. The method of claim 1, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
6. The method of claim 1, wherein the machine learned model is configured to identify a correlation between a portion of the received information and each one of the one or more fields of the API.
7. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
herein the machine-learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and
wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and
wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of historical provisioning of user accounts with one or more third-party systems;
receiving, by the central database system, information associated with an employee from an employer;
accessing, by the central database system, a third-party system API for use in provisioning a user account;
translating, by the central database system, the received information using the machine learned model, the machine-learned model trained to identify information requirements associated with the accessed API and to convert the received information for entry into one or more fields of the API based on the identified information requirements;
requesting, by the central database system via the accessed API, the third-party system provision a user account corresponding to the employee using the translated received information;
receiving, by the central database system, credentials of the provisioned user account from the third-party system; and
providing, by the central database system, the received credentials to the employer.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
10. An employer database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
11. A system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training a machine-learned model using information representative of historical provisioning of user accounts with one or more third-party systems (Training is interpreted as requiring accessing the model and user account information in the database.);
receiving information associated with an employee from an employer;
accessing a third-party system API for use in provisioning a user account;
translating the received information using the machine learned model, the machine-learned model trained to identify information requirements associated with the accessed API and to convert the received information for entry into one or more fields of the API based on the identified information requirements (Translating employee information requires accessing it);
requesting, via the accessed API, the third-party system provision a user account corresponding to the employee using the translated received information;
receiving credentials of the provisioned user account from the third-party system; and
providing the received credentials to the employer.
11. The system of claim 10, wherein a user account comprises a cloud services account operated by a third-party system.
2. The method of claim 1, wherein the user account comprises a cloud services account operated by the third-party system.
12. The system of claim 10, wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
3. The method of claim 1, wherein the machine learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
13. The system of claim 12, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
4. The method of claim 3, further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
14. The system of claim 10, wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
5. The method of claim 1, wherein the machine learned model is trained to identify one or more information translation operations performed during the historical provisioning.
15. The system of claim 10, wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API.
6. The method of claim 1, wherein the machine learned model is configured to identify a correlation between a portion of the received information and each one of the one or more fields of the API.
16. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine learned model accesses the identified additional information from a source external to the central database system.
17. The system of claim 10, wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account, and wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1, wherein the machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
18. The system of claim 10, wherein the one or more user accounts are provisioned in response to an employee being onboarded.
11. A system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training a machine-learned model using information representative of historical provisioning of user accounts with one or more third-party systems;
receiving information associated with an employee from an employer;
accessing a third-party system API for use in provisioning a user account;
translating the received information using the machine learned model, the machine-learned model trained to identify information requirements associated with the accessed API and to convert the received information for entry into one or more fields of the API based on the identified information requirements;
requesting, via the accessed API, the third-party system provision a user account corresponding to the employee using the translated received information;
receiving credentials of the provisioned user account from the third-party system; and
providing the received credentials to the employer.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to recognize that requesting credentials to provision an account by an employer and receiving them to do so would be commonly done when a new employee needs to be added to the system, such as is the common case when they’re being onboarded. Thus, said artisan would have been motivated to modify claim 1 to request the credentials and provision in response to onboarding the employee so as to perform the necessary step of creating accounts for a new employee after they’re hired as is commonly done in the art.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising:
accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems;
accessing, by the employer database system, information associated with an employee; and
applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee
to provision one or more user accounts for the employee using one or more third-party system APIs.
11. A system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training a machine-learned model using information representative of historical provisioning of user accounts with one or more third-party systems (Training is interpreted as requiring accessing the model and user account information in the database.);
receiving information associated with an employee from an employer;
accessing a third-party system API for use in provisioning a user account;
translating the received information using the machine learned model, the machine-learned model trained to identify information requirements associated with the accessed API and to convert the received information for entry into one or more fields of the API based on the identified information requirements (Translating employee information requires accessing it);
requesting, via the accessed API, the third-party system provision a user account corresponding to the employee using the translated received information;
receiving credentials of the provisioned user account from the third-party system; and
providing the received credentials to the employer.
20. The non-transitory computer-readable storage medium of claim 19, wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
10. The method of claim 1, wherein the machine learned model is configured to translate the received information before receiving a request from the employee to provision the user account.
Claim Rejections - 35 USC § 112
Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 20, it is unclear whether “a machine-learned model” recited therein is the same “a machine-learned model” already recited in claim 19, or intended to be a different, second, machine-learned model. Accordingly, the scope of the claim cannot be properly ascertained, rendering it indefinite.
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 1-6, 8-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sallaka et al. (US 2011/0197254 A1), hereinafter Sallaka, in view of Choudhri (US 2008/0168367 A1), hereinafter Choudhri.
As to claim 1, Sallaka discloses a method comprising:
accessing, by an employer database system, a provisioning system for use in provisioning a user account (Fig. 1, #14, 26; Fig. 3, #82, 88; [0035]; [0055], Third-party APIs such as #82 and 88 in third-party applications 78 and 80 respectively are accessed via messages such as callback messages to facilitate provisioning of one or more user accounts in response to a provisioning request for a new employee.);
accessing, by the employer database system, information associated with an employee (Figs. 1-3; [0034]; [0056], The received, accessed, information for the new employee is translated into requests formulated for one or more third-party applications, e.g. email or database, to provision resources from those applications via their respective API interfaces. Interacting through those respective interfaces requires converting the received information to at least one field understandable by the application to enable requested provisioning.); and
applying, by the employer database system, the accessed provisioning system to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs (Fig. 3; [0074]; [0075], Processed account information are received from the third-party application(s), e.g. #82 and 88, and provided to the requestor, i.e. the employer using the HCM in the example of employee onboarding.).
Sallaka does not specifically disclose accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems; and applying the accessed machine learned model to perform the provisioning.
However, it is first noted that the fact the claimed machine learned model was trained using information representative of a historical provisioning of user accounts with one or more third-party systems does not carry patentable weight. This describes steps performed prior, and outside, the scope of the method being claimed. The machine learning model is only something generically accessed and generically used for provision one or more user accounts without any specifics to how the model is used in doing so. So long as a machine learned model can perform the actual claimed steps, i.e. be accessed and can by applied to provision a user account as claimed, then it meets the requirements of the claim. How the model was trained does not affect any claimed structure or steps being performed since an equivalent functioning model can be trained or configured by any other manner and still perform the same claimed functionality. As such, these features do not carry patentable weight and need not be disclosed by the prior art in rejecting the claim. See MPEP §2111.04.
However, Choudhri discloses accessing a machine learned model configured to identify information requirements associated with the accessed API ([0097]; [0098]) and configuring a source to work with the capabilities of a new device based on the learned API of the new device by converting the information of the source to work with the API ([0098], the source is configured to match the input capability of the API).
Before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Sallaka with the teachings of Choudhri by modifying Sallaka such that when a new application is added having its own API (Sallaka, Figs. 1, 3; [0044]), that the central database system (central provisioning module) includes a machine learned model configured to be accessed to identify information requirements associated with the accessed API like Choudhri and to then use the machine learning model to convert the received provisioning requests, and corresponding data therein associated with an employee, into provisioning requests commensurate with the API of the newly added application (Sallaka, [0044]; [0056]). Thus, as combined, rendering obvious “accessing, by an employer database system, a machine-learned model” and “applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs” as claimed. Said artisan would have been motivated to do so in order to enable the system of Sallaka to learn API input capabilities so as to enable self-configuration therewith for the user accounts of Sallaka and reduce the need for manual actions (Choudhri, [0096]-[0098]).
As to claim 10, Sallaka discloses an employer database system comprising:
a hardware processor ([0089]; [0090]); and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising ([0089]; [0090]):
accessing, by an employer database system, a provisioning system for use in provisioning a user account (Fig. 1, #14, 26; Fig. 3, #82, 88; [0035]; [0055], Third-party APIs such as #82 and 88 in third-party applications 78 and 80 respectively are accessed via messages such as callback messages to facilitate provisioning of one or more user accounts in response to a provisioning request for a new employee.);
accessing, by the employer database system, information associated with an employee (Figs. 1-3; [0034]; [0056], The received, accessed, information for the new employee is translated into requests formulated for one or more third-party applications, e.g. email or database, to provision resources from those applications via their respective API interfaces. Interacting through those respective interfaces requires converting the received information to at least one field understandable by the application to enable requested provisioning.); and
applying, by the employer database system, the accessed provisioning system to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs (Fig. 3; [0074]; [0075], Processed account information are received from the third-party application(s), e.g. #82 and 88, and provided to the requestor, i.e. the employer using the HCM in the example of employee onboarding.).
Sallaka does not specifically disclose accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems; and applying the accessed machine learned model to perform the provisioning.
However, it is first noted that the fact the claimed machine learned model was trained using information representative of a historical provisioning of user accounts with one or more third-party systems does not carry patentable weight. This describes steps performed prior, and outside, the scope of the method being claimed. The machine learning model is only something generically accessed and generically used for provision one or more user accounts without any specifics to how the model is used in doing so. So long as a machine learned model can perform the actual claimed steps, i.e. be accessed and can be applied to provision a user account as claimed, then it meets the requirements of the claim. How the model was trained does not affect any claimed structure or steps being performed since an equivalent functioning model can be trained or configured by any other manner and still perform the same claimed functionality. As such, these features do not carry patentable weight and need not be disclosed by the prior art in rejecting the claim. See MPEP §2111.04.
However, Choudhri discloses accessing a machine learned model configured to identify information requirements associated with the accessed API ([0097]; [0098]) and configuring a source to work with the capabilities of a new device based on the learned API of the new device by converting the information of the source to work with the API ([0098], the source is configured to match the input capability of the API).
Before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Sallaka with the teachings of Choudhri by modifying Sallaka such that when a new application is added having its own API (Sallaka, Figs. 1, 3; [0044]), that the central database system (central provisioning module) includes a machine learned model configured to be accessed to identify information requirements associated with the accessed API like Choudhri and to then use the machine learning model to convert the received provisioning requests, and corresponding data therein associated with an employee, into provisioning requests commensurate with the API of the newly added application (Sallaka, [0044]; [0056]). Thus, as combined, rendering obvious “accessing, by an employer database system, a machine-learned model” and “applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs” as claimed. Said artisan would have been motivated to do so in order to enable the system of Sallaka to learn API input capabilities so as to enable self-configuration therewith for the user accounts of Sallaka and reduce the need for manual actions (Choudhri, [0096]-[0098]).
As to claim 19, Sallaka discloses a non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes an employer database system to perform steps comprising ([0089]; [0090]):
accessing, by an employer database system, a provisioning system for use in provisioning a user account (Fig. 1, #14, 26; Fig. 3, #82, 88; [0035]; [0055], Third-party APIs such as #82 and 88 in third-party applications 78 and 80 respectively are accessed via messages such as callback messages to facilitate provisioning of one or more user accounts in response to a provisioning request for a new employee.);
accessing, by the employer database system, information associated with an employee (Figs. 1-3; [0034]; [0056], The received, accessed, information for the new employee is translated into requests formulated for one or more third-party applications, e.g. email or database, to provision resources from those applications via their respective API interfaces. Interacting through those respective interfaces requires converting the received information to at least one field understandable by the application to enable requested provisioning.); and
applying, by the employer database system, the accessed provisioning system to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs (Fig. 3; [0074]; [0075], Processed account information are received from the third-party application(s), e.g. #82 and 88, and provided to the requestor, i.e. the employer using the HCM in the example of employee onboarding.).
Sallaka does not specifically disclose accessing, by an employer database system, a machine-learned model trained using information representative of a historical provisioning of user accounts with one or more third-party systems; and applying the accessed machine learned model to perform the provisioning.
However, it is first noted that the fact the claimed machine learned model was trained using information representative of a historical provisioning of user accounts with one or more third-party systems does not carry patentable weight. This describes steps performed prior, and outside, the scope of the method being claimed. The machine learning model is only something generically accessed and generically used for provision one or more user accounts without any specifics to how the model is used in doing so. So long as a machine learned model can perform the actual claimed steps, i.e. be accessed and can be applied to provision a user account as claimed, then it meets the requirements of the claim. How the model was trained does not affect any claimed structure or steps being performed since an equivalent functioning model can be trained or configured by any other manner and still perform the same claimed functionality. As such, these features do not carry patentable weight and need not be disclosed by the prior art in rejecting the claim. See MPEP §2111.04.
However, Choudhri discloses accessing a machine learned model configured to identify information requirements associated with the accessed API ([0097]; [0098]) and configuring a source to work with the capabilities of a new device based on the learned API of the new device by converting the information of the source to work with the API ([0098], the source is configured to match the input capability of the API).
Before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Sallaka with the teachings of Choudhri by modifying Sallaka such that when a new application is added having its own API (Sallaka, Figs. 1, 3; [0044]), that the central database system (central provisioning module) includes a machine learned model configured to be accessed to identify information requirements associated with the accessed API like Choudhri and to then use the machine learning model to convert the received provisioning requests, and corresponding data therein associated with an employee, into provisioning requests commensurate with the API of the newly added application (Sallaka, [0044]; [0056]). Thus, as combined, rendering obvious “accessing, by an employer database system, a machine-learned model” and “applying, by the employer database system, the accessed machine-learned model to the accessed information associated with the employee to provision one or more user accounts for the employee using one or more third-party system APIs” as claimed. Said artisan would have been motivated to do so in order to enable the system of Sallaka to learn API input capabilities so as to enable self-configuration therewith for the user accounts of Sallaka and reduce the need for manual actions (Choudhri, [0096]-[0098]).
As to claims 2 and 11, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, discloses wherein a user account comprises a cloud services account operated by a third-party system (Sallaka, Fig. 1; [0022]).
Additionally, while the prior art discloses wherein the user account comprises a cloud services account operated by the third-party system, the fact that the account comprises a cloud services account is non-functional descriptive material and does not carry patentable weight. The claims do not utilize the fact that it is a cloud services account to perform any functionality specific to that type of data which would not occur to any other type of account data. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claim. See MPEP §2111.05.
As to claims 3 and 12, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, does not disclose wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
However, as similarly set forth in claims 1 and 10 above, the claims do not perform the steps of training. I.e. the machine is merely already trained, e.g. in the past by any entity. The machine learning model is also not part of the claimed system, but merely used with the system, and is only something accessed and generically used for provision one or more user accounts without any specifics to how the model is used in doing so. As such, the specifics of “wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields” do not limit structure being claimed, nor require any steps to be performed and therefore do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 1 and 10 above.
As to claims 4 and 13¸ the claims are rejected for the same reasons as claims 3 and 12 above. In addition, Sallaka, as previously modified with Choudhri, does not disclose further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
However, the claimed limitations are interpreted as further defining the training having been performed outside the scope of the claims as described by parent claims 3 and 12. As discussed in claims 3 and 12, the training is not recited as necessarily being performed as part of the claims and does not limit the scope of the claims. For the same rationale, the features of claims 4 and 13 do not carry patentable weight, and are rejected for the same reasons as claims 3 and 12 above.
As to claims 5 and 14, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, does not disclose wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
However, the claims do not necessarily use the feature of the machine learning model trained to “identify one or more information translation operations performed during the historical provisioning” to perform any claimed function. The claims do not recite performing the training as part of the steps performed (i.e. the machine is merely trained, e.g. in the past by any entity), nor is the machine learning model part of the claimed system, but merely used with the system (and used only to perform the function of converting as recited in claims 1 and 10). As such, the features of claims 5 and 14 do not limit the claimed steps being performed, nor do they limit the structure of a system being claimed. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 3 and 13 above.
As to claims 6 and 15, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, discloses wherein the machine-learned model is configured to identify a correlation between a portion of the accessed information and one or more fields of a third-party system API (Choudhri, [0096]-[0098], E.g. learning input capabilities of a device through the inputs available of the API.).
The reasons and motivations for combining the teachings of Sallaka and Choudhry are the same as previously set forth with respect to claims 1 and 11 above.
Additionally, the claims do not necessarily use the feature of the machine learning model trained “to identify a correlation between a portion of the information and one or more fields of the API” to perform any claimed function. The claims do not recite performing the training as part of the steps performed (i.e. the machine is merely trained, e.g. in the past by any entity), nor is the machine learning model part of the claimed system, but merely used with the system (and used only to perform the function of converting as recited in claims 1 and 10). As such, the features of claims 7 and 17 do not limit the claimed steps being performed, nor do they limit the structure of a system being claimed. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 1 and 10 above.
As to claims 8 and 17, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, discloses wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account (Sallaka, [0042]; [0072]; [0073], Validation can identify incorrect or missing information that is then additional information needed. Furthermore, an approver can identify and correct necessary data. As previously combined with Choudhri, includes the machine learning model for the tasks.), and
wherein the central database system is configured to request the identified additional information from the employer (Sallaka, [0073], A manager, i.e. part of the employer, can modify or correct a subset of the requested data. Thus the system can request and correct missing data from the employer.).
Additionally, the claims do not necessarily use the feature of the machine learning model “to identify additional information associated with the employee needed to provision the user account” to perform any claimed function. The claims do not recite performing the training as part of the steps performed (i.e. the machine is merely trained, e.g. in the past by any entity), nor is the machine learning model part of the claimed system, but merely used with the system (and used only to perform the function of converting as recited in claims 1 and 10). As such, the features of claims 9 and 19 do not limit the claimed steps being performed, nor do they limit the structure of a system being claimed. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 1 and 10 above.
As to claims 9 and 18, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, discloses wherein the one or more user accounts are provisioned in response to an employee being onboarded (Sallaka, ([0074]; [0075], Processed account information, i.e. credentials of the provisioned account, are received from the third-party application(s) and provided to the requestor, i.e. the employer using the HCM in the example of employee onboarding.).
As to claim 20, the claim is rejected for the same reasons as claim 19 above. In addition, Sallaka, as previously modified with Choudhri, does not disclose wherein a machine-learned model is configured to translate the information before receiving a request from the employee to provision a user account.
However, the claims do not necessarily use the feature of the machine learning model “to translate the information before receiving a request from the employee to provision the user account” to perform any claimed function. The claims do not recite performing the training as part of the steps performed (i.e. the machine is merely trained, e.g. in the past by any entity), nor is the machine learning model part of the claimed system, but merely used with the system (and used only to perform the function of converting as recited in claims 1 and 11). As such, the features of claims 10 and 20 do not limit the claimed steps being performed, nor do they limit the structure of a system being claimed. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 1 and 10 above.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sallaka and Choudhri as applied above, and further in view of Bonforte (US 2011/0029620 A1).
As to claims 7 and 16, the claims are rejected for the same reasons as claims 1 and 10 above. In addition, Sallaka, as previously modified with Choudhri, discloses wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision a user account (Sallaka, [0042]; [0072]; [0073], Validation can identify incorrect or missing information that is then additional information needed. Furthermore, an approver can identify and correct necessary data. As previously combined with Choudhri, includes the machine learning model for the tasks.),
Sallaka, as previously modified with Choudhri, does not disclose , and wherein the machine learned model accesses the identified additional information from a source external to the central database system.
However, Bonforte discloses a machine learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine learned model accesses the identified additional information from a source external to the central database system (Figs. 2, 7; [0036]; [0087]; [0093], Missing information for a user profile being provisioned, i.e. a user account, is identified. External resources, such as those associated with step 706, are searched for the missing information. Also, as the profile includes employer information and company role, it is analogous to an employee’s user account.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Sallaka, as previously modified with Choudhry, with the teachings of Bonforte by further modifying Sallaka such that the validation operations of Sallaka which include detecting missing information of an employee, further include detecting missing information and searching and obtaining said missing information from other sources like is done by Bonforte. Said artisan would have been motivated to do so in order to better fully automatically provision user accounts in Sallaka by enabling Sallaka to automatically search for and obtain missing information needed (Bonforte, [0080]).
Additionally, the claims do not necessarily use the feature of the machine learning model “to identify additional information associated with the employee needed to provision the user account” to perform any claimed function. The claims do not recite performing the training as part of the steps performed (i.e. the machine is merely trained, e.g. in the past by any entity), nor is the machine learning model part of the claimed system, but merely used with the system (and used only to perform the function of converting as recited in claims 1 and 10). As such, the features of claims 9 and 19 do not limit the claimed steps being performed, nor do they limit the structure of a system being claimed. As such, these features do not carry patentable weight and need not be taught by the prior art to reject the claims. See MPEP §2111.04. Accordingly, the claims are fully rejected for the same reasons as claims 1 and 10 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kaznady (US 2017/0213280 A1) discloses training a machine learning model using historical information of applicants for a loan including employment information ([0051], [0056]).
Barron-Kraus et al. (US 2020/0019637 A1) discloses mapping requests to API fields and translating data in between. Additionally, when requesting data, additional data can be determined to be needed and searched for and retrieved from additional sources.
Bahrami et al. (US 2018/0165135 A1) discloses learning APIs based on their API documentations via a learning API that understands functionalities, parameters, operations, accessibility and outputs of APIs.
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/James E Richardson/ Primary Examiner, Art Unit 2167
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