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 .
Applicant’s filing dated 07/16/2025 has been received and made of record.
Application 19/270,735 is a continuation of Application 18/980,630 (now US Patent 12,388,774 B2), which claims priority to Provisional Application 63/610,226, filed 12/14/2023.
Claims 1-20 are currently pending in Application 19/270,735.
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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,388,774 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims anticipate the pending application claims (that is, all of the limitations of the pending claims are recited by the patent claims, with allowances for drafting choice). See also the table below.
Pending Application Claims
Patent US 12,388,774 B2 Claims
1. A system that uses at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: at least one non-transitory storage medium that stores instructions; and at least one processor that executes the instructions to: select at least one prompt template from a group of stored prompt templates that are associated with different health coaching paths based at least on user data that at least specifies a coaching path of the different health coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different health coaching paths; generate at least one customized prompt from the at least one prompt template; provide the at least one customized prompt to the at least one LLM to generate a prompted LLM; and facilitate user interaction with the prompted LLM to advance a course of the coaching path.
2. The system of claim 1, wherein the at least one processor further executes the instructions to modify the at least one prompt template before providing the at least one customized prompt to the at least one LLM.
3. The system of claim 2, wherein the at least one processor further executes the instructions to modify the at least one prompt template based at least on the user data.
4. The system of claim 1, wherein the at least one prompt template includes at least one variable.
5. The system of claim 4, wherein generation of the at least one customized prompt from the at least one prompt template is performed at least by setting a value for the at least one variable using at least the user data or current user input.
6. The system of claim 1, wherein the different health coaching paths comprise chronic condition and/or disease management coaching paths.
7. The system of claim 1, wherein generation of the at least one customized prompt from the at least one prompt template is performed using at least the user data or current user input.
8. The system of claim 1, wherein the user interaction is received from a mobile computing device.
9. The system of claim 1, wherein the at least one processor further executes the instructions to facilitate the user interaction with the prompted LLM to advance the course of the coaching path by exchanging at least one message between the prompted LLM and a user interface.
10. The system of claim 1, wherein the at least one processor further executes the instructions to facilitate the user interaction with the prompted LLM to advance the course of the coaching path by configuring communication between the prompted LLM and a user interface.
11. The system of claim 1, wherein the at least one prompt template specifies a role of the LLM.
12. The system of claim 1, wherein the at least one prompt template specifies at least one boundary for the LLM.
13. The system of claim 1, wherein the at least one processor further executes the instructions to use the LLM to render a specific, targeted chronic condition and/or disease management coaching.
14. The system of claim 1, wherein the at least one processor further executes the instructions to use the LLM to implement a food chatbot.
15. A method for using at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: selecting at least one prompt template from a group of stored prompt templates that are associated with different health coaching paths based at least on user data that at least specifies a coaching path of the different health coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different health coaching paths; generating at least one customized prompt from the at least one prompt template using at least the user data or current user input; providing the at least one customized prompt to the at least one LLM to generate a prompted LLM; and facilitating user interaction with the prompted LLM to advance a course of the coaching path.
16. The method of claim 15, further comprising modifying at least one of the group of stored prompt templates or the LLM based at least on evaluation of output of the prompted LLM.
17. The method of claim 16, wherein the modifying is performed while the prompted LLM operates.
18. A computer program product stored in at least one non-transitory storage medium that includes instructions executable by at least one processor to perform a method for using at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: selecting at least one prompt template from a group of stored prompt templates that are associated with different health coaching paths based at least on user data that at least specifies a coaching path of the different health coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different health coaching paths; generating at least one customized prompt from the at least one prompt template using at least the user data or current user input; providing the at least one customized prompt to the at least one LLM to generate a prompted LLM; and causing the prompted LLM to: request user input regarding food; provide one or more assumptions regarding the user input; confirm the one or more assumptions; provide information regarding the food; and upon receiving a request for at least one suggestion to improve the food, provide the at least one suggestion to improve the food.
19. The computer program product of claim 18, wherein the at least one suggestion is constrained at least by the user data or by a program indicated in the user data.
20. The computer program product of claim 18, wherein the method further includes using the prompted LLM or another model to evaluate interaction with the prompted LLM.
1. A system that uses at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: a memory allocation configured to store at least one executable asset; and a processor allocation configured to access the memory allocation and execute the at least one executable asset to instantiate: an LLM interaction service that: selects at least one prompt template that includes at least one variable from a group of stored prompt templates that are associated with different chronic condition and/or disease management coaching paths based at least on user data that at least specifies a coaching path of the different chronic condition and/or disease management coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different chronic condition and/or disease management coaching paths; generates at least one customized prompt at least by setting a value for the at least one variable using at least the user data or current user input; provides the at least one customized prompt to the at least one LLM to generate a prompted LLM; and facilitates user interaction with the prompted LLM to advance a course of the chronic condition and/or disease management coaching paths.
2. The system of claim 1, wherein the LLM interaction service is operable to modify the at least one prompt template that includes the at least one variable before providing the at least one customized prompt to the at least one LLM.
3. The system of claim 2, wherein the LLM interaction service is operable to modify the at least one prompt template that includes the at least one variable based at least on the user data.
4. The system of claim 1, wherein the LLM interaction service or at least one other service is operable to generate a model to evaluate interaction with the at least one LLM.
5. The system of claim 4, wherein the model is an LLM.
6. The system of claim 4, wherein the model is the at least one LLM.
7. The system of claim 4, wherein the LLM interaction service or the at least one other service uses labeled data to adapt and fine-tune the model.
8. The system of claim 7, wherein the LLM interaction service or the at least one other service is operable to label data to generate the labeled data.
9. The system of claim 1, wherein the LLM interaction service facilitates the user interaction with the prompted LLM to advance the course of the chronic conditions and/or disease management coaching by exchanging at least one message between the prompted LLM and a user interface.
10. The system of claim 1, wherein the LLM interaction service facilitates the user interaction with the prompted LLM to advance the course of the chronic condition and/or disease management coaching path by configuring communication between the prompted LLM and a user interface.
11. The system of claim 1, wherein the at least one prompt template that includes the at least one variable specifies a role of the LLM.
12. The system of claim 1, wherein the at least one prompt template that includes the at least one variable specifies at least one boundary for the LLM.
13. The system of claim 1, wherein the LLM interaction service uses the LLM to render a specific, targeted chronic conditions and/or disease management coaching.
14. The system of claim 1, wherein the LLM interaction service uses the LLM to implement a food chatbot.
15. A method for using at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: selecting at least one prompt template that includes at least one variable from a group of stored prompt templates that are associated with different chronic condition and/or disease management coaching paths based at least on user data that at least specifies a health coaching path of the different chronic condition and/or disease management coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different chronic condition and/or disease management coaching paths; generating at least one customized prompt at least by setting a value for the at least one variable using at least the user data or current user input; providing the at least one customized prompt to the at least one LLM to generate a prompted LLM; and facilitating user interaction with the prompted LLM to advance a course of the chronic condition and/or disease management coaching paths.
16. The method of claim 15, further comprising modifying at least one of the group of stored prompt templates or the LLM based at least on evaluation of output of the prompted LLM.
17. The method of claim 16, wherein the modifying is performed while the prompted LLM operates.
18. A computer program product stored in at least one non-transitory storage medium that includes instructions executable by at least one processor to perform a method for using at least one large language model (LLM) to implement a controlled artificial intelligence chat environment, comprising: selecting at least one prompt template that includes at least one variable from a group of stored prompt templates that are associated with different chronic condition and/or disease management coaching paths based at least on user data that at least specifies a coaching path of the different chronic condition and/or disease management coaching paths, the stored prompt templates including tone and content restrictions associated with a respective one of the different chronic condition and/or disease management coaching paths; generates at least one customized prompt at least by setting a value for the at least one variable using at least the user data or current user input; providing the at least one customized prompt to the at least one LLM to generate a prompted LLM; and causing the prompted LLM to: request user input regarding at least one meal; provide one or more assumptions regarding the user input; confirm the one or more assumptions; pre-enhance at least one response; provide information regarding the at least one meal; and upon receiving a request for at least one suggestion to improve the at least one meal, provide the at least one suggestion to improve the at least one meal.
19. The computer program product of claim 18, wherein the at least one suggestion is constrained at least by the user data or by a program indicated in the user data.
20. The computer program product of claim 18, wherein the method further includes using the prompted LLM or another model to evaluate interaction with the prompted LLM.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ahmed (US 2025/0005269 A1) describes an LLM system that provides recipe guidance suitable for a user’s health conditions.
Krans (US 2018/0375807 A1) describes a virtual assistant enhancement system that includes determining a coaching style and providing health motivation.
Sejpal (US 2025/0028768 A1) describes a machine-learning model that customizes meals based on individual user data.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal Divecha can be reached at (571) 272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IMAD HUSSAIN/Primary Examiner, Art Unit 2453
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