DETAILED ACTION
Notice of 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: Claim 15 recites in line 11 “ a viewing port”. There is no mention of “ viewing port” in the specification.
Appropriate correction is required.
35 U.S.C. 112(f) Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a nonstructural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for'' (e.g., "means for'') or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting
sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder ( “configured to”) is not preceded by a structural modifier. Such claim limitation(s) is/are: “ a consensus module configured” in claim 1 and 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being
interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The non-statutory 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 non-statutory 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 non-statutory 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 non-statutory 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 e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of co pending Application No. 19/565354. The claims at issue are identical, they are not patentably distinct from each other because claims 1-20 of the instant application is similar in scope and content of the co pending application claims 1-20, by the same Applicant.
It is clear that all the elements of the application claims 1-20 are to be found in co pending application claims 1-20 (as the application claims 1-20 fully encompasses co pending application claims 1-20). There is no difference between the application claims and the co pending application claims. Since application claims 1-20 are anticipated by claims 1-20 of the co pending application, it is not patentably distinct from co pending application claims.
Application No: 18/927,783
Co pending Application No: 19/565,354
1. A system for dynamic interaction with a synthetic user, comprising:
a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface,
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role;
a model superstructure interconnecting the user interface, the generative AI model, and an independent validation model, wherein the model superstructure is configured to trigger evaluations, by the independent validation model via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
1. A system for dynamic interaction with a synthetic user, comprising:
a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface,
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role;
a model superstructure interconnecting the user interface, the generative AI model, and an independent validation model, wherein the model superstructure is configured to trigger evaluations, by the independent validation model via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
2. The system of claim 1, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response.
2. The system of claim 1, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response.
3. The system of claim 2, wherein the designated virtual character role emulated by the generative AI model corresponds to a Ouija board, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
3. The system of claim 2, wherein the designated virtual character role emulated by the generative AI model corresponds to a Ouija board, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
4. The system of claim 1, wherein if the user input does not satisfy the set of corresponding predetermined model-driven conditions, the user interface displays a rejection animation.
4. The system of claim 1, wherein if the user input does not satisfy the set of corresponding predetermined model-driven conditions, the user interface displays a rejection animation.
5. The system of claim 1, wherein the designated virtual character role is based on a market sector participant, wherein the generative AI model is configured to generate responses tailored to preferences of the market sector participant via the at least one hidden parameter.
5. The system of claim 1, wherein the designated virtual character role is based on a market sector participant, wherein the generative AI model is configured to generate responses tailored to preferences of the market sector participant via the at least one hidden parameter.
6. The system of claim 1, wherein the generative AI model is configured to generate an assessment of the hidden parameter, wherein the user interface displays a graphical animation of the assessment, wherein the assessment includes a generative AI model's confidence level in the user input or hidden parameter.
6. The system of claim 1, wherein the generative AI model is configured to generate an assessment of the hidden parameter, wherein the user interface displays a graphical animation of the assessment, wherein the assessment includes a generative AI model's confidence level in the user input or hidden parameter.
7. The system of claim 1, wherein the set of corresponding predetermined model-driven conditions encompass a set of criteria for at least one emotional expression, wherein the set of criteria enables the response to convey the at least one emotional expression tailored to the designated virtual character role.
7. The system of claim 1, wherein the set of corresponding predetermined model-driven conditions encompass a set of criteria for at least one emotional expression, wherein the set of criteria enables the response to convey the at least one emotional expression tailored to the designated virtual character role.
8. A method for dynamic interaction with a synthetic user, comprising:
providing a user interface that is communicatively connected to a generative AI model, said generative AI model configured to generate a response based on user input to the user interface;
wherein the response is derived from at least one hidden parameter; wherein the at least one hidden parameter pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role;
interconnecting, via a model superstructure, between the user interface, the generative AI model, and a plurality of independent validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of independent validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and employing a consensus module, configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
8. A method for dynamic interaction with a synthetic user, comprising:
providing a user interface that is communicatively connected to a generative AI model, said generative AI model configured to generate a response based on user input to the user interface;
wherein the response is derived from at least one hidden parameter; wherein the at least one hidden parameter pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role;
interconnecting, via a model superstructure, between the user interface, the generative AI model, and a plurality of independent validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of independent validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and employing a consensus module, configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
9. The method of claim 8, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
9. The method of claim 8, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
10. The method of claim 8, wherein the generative AI model is further configured to adapt the designated virtual character role based on at least one historical user interaction.
10. The method of claim 8, wherein the generative AI model is further configured to adapt the designated virtual character role based on at least one historical user interaction.
11. The method of claim 8, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
11. The method of claim 8, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
12. The method of claim 8, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response.
12. The method of claim 8, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response.
13. The method of claim 8, wherein the plurality of independent validation models include a sentiment analysis component to assess user emotions expressed in the user input.
13. The method of claim 8, wherein the plurality of independent validation models include a sentiment analysis component to assess user emotions expressed in the user input.
14. The method of claim 8, wherein the at least one hidden parameter includes at least one temporal element, including a time factor.
14. The method of claim 8, wherein the at least one hidden parameter includes at least one temporal element, including a time factor.
15. A system for dynamic interaction with a synthetic user for Ouija board gameplay, comprising:
a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface,
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role of a Ouija board,
and wherein the user interface comprises a plurality of response indicators and a viewing port, wherein the viewing port is positioned to indicate a particular response indicator on the user interface corresponding to the response;
a model superstructure interconnecting the user interface, the generative AI model, and a plurality of validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
15. A system for dynamic interaction with a synthetic user for Ouija board gameplay, comprising:
a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface,
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role of a Ouija board,
and wherein the user interface comprises a plurality of response indicators and a viewing port, wherein the viewing port is positioned to indicate a particular response indicator on the user interface corresponding to the response;
a model superstructure interconnecting the user interface, the generative AI model, and a plurality of validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions;
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
16. The system of claim 15, wherein the plurality of validation models includes a temporal relevance check, wherein the temporal relevance check assesses an indication of suitability of the response in relation to whether a user selected deceased person is actually deceased.
16. The system of claim 15, wherein the plurality of validation models includes a temporal relevance check, wherein the temporal relevance check assesses an indication of suitability of the response in relation to whether a user selected deceased person is actually deceased.
17. The system of claim 15, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
17. The system of claim 15, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers.
18. The system of claim 15, wherein the at least one hidden parameter includes a set of historical knowledge constraints, restricting the generative AI model to respond within a user selected deceased person's known historical context.
18. The system of claim 15, wherein the at least one hidden parameter includes a set of historical knowledge constraints, restricting the generative AI model to respond within a user selected deceased person's known historical context.
19. The system of claim 15, wherein the at least one hidden parameter includes a set of communication styles of a user selected deceased person, enabling the generative AI model to produce responses consistent with a set of linguistic patterns of the user selected deceased person.
19. The system of claim 15, wherein the at least one hidden parameter includes a set of communication styles of a user selected deceased person, enabling the generative AI model to produce responses consistent with a set of linguistic patterns of the user selected deceased person.
20. The system of claim 15, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
20. The system of claim 15, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
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 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 of this title, 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, 8, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla.
Regarding Claim 1, Henrickson teaches a system for dynamic interaction with a synthetic user, comprising: a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface ( Henrickson: Page 950, para.[3], page 953, para.[2],[3], page 954, para.[2], “Project December”, which allows users to train their own chatbots, using OpenAI’s GPT-3 (Generative Pre-trained Transformers), connected with textual user interface and generates dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), which was generated based on his deceased fiancé),
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), is based on the old texts and Facebook messages of the deceased fiancé);
Henrickson while teaching the system of claim 1, fails to explicitly teach the claimed, a model superstructure interconnecting the user interface, the generative AI model, and an independent validation model, wherein the model superstructure is configured to trigger evaluations, by the independent validation model via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions; and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
However, Khosla does teach the claimed, a model superstructure interconnecting the user interface, the generative AI model, and an independent validation model, wherein the model superstructure is configured to trigger evaluations, by the independent validation model via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions ( Khosla: Para.[0049], Fig. 2B, The verifier component 108 is configured to analyze whether answers provided by the LLM component 106 is valid or not, by utilizing a set of corresponding module and conditions. Para.[0059]-[0068], Fig.3 illustrates different components of the natural language question answering service 102 based on analyzing a natural language question, consists of user interface (UI) of the customer computing device 122, LLM component 106 which sends the generated answer and retrieved passages to the verifier component 108);
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module ( Khosla: Para.[0068], Fig. 3, the watermarking component 110 ( consensus module) sends the watermarked answer and retrieved passages to the customer computing devices 122 such that a user of the customer computing devices 122 may view the answer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Khosla’s teaching of systems and methods for processing natural language queries with verification, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson, because, processing natural language queries with verification, by utilizing generative artificial intelligence (AI) models, such as, large language models (LLM), can provide accurate answers to customer (Khosla, Para.[0009],[0010]).
Regarding Claim 8, Henrickson teaches a method for dynamic interaction with a synthetic user, comprising: providing a user interface that is communicatively connected to a generative AI model, said generative AI model configured to generate a response based on user input to the user interface ( Henrickson: Page 950, para.[3], page 953, para.[2],[3], page 954, para.[2], “Project December”, which allows users to train their own chatbots, using OpenAI’s GPT-3 (Generative Pre-trained Transformers), connected with textual user interface and generates dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), which was generated based on his deceased fiancé);
wherein the response is derived from at least one hidden parameter; wherein the at least one hidden parameter pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), is based on the old texts and Facebook messages of the deceased fiancé);
Henrickson while teaching the method of claim 8, fails to explicitly teach the claimed, interconnecting, via a model superstructure, between the user interface, the generative AI model, and a plurality of independent validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of independent validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions; and employing a consensus module, configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
However, Khosla does teach the claimed, interconnecting, via a model superstructure, between the user interface, the generative AI model, and a plurality of independent validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of independent validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions ( Khosla: Para.[0049], [0056], Fig. 2B, The verifier component 108 is configured to analyze whether answers provided by the LLM component 106 is valid or not, by utilizing a set of corresponding modules (textual overlap module 234, NLI module 236, relational NLI module 238, membership inference attack module 240) and conditions. Para.[0059]-[0068], Fig.3 illustrates different components of the natural language question answering service 102 based on analyzing a natural language question, consists of user interface (UI) of the customer computing device 122, LLM component 106 which sends the generated answer and retrieved passages to the verifier component 108);
and employing a consensus module, configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module ( Khosla: Para.[0068], Fig. 3, the watermarking component 110 ( consensus module) sends the watermarked answer and retrieved passages to the customer computing devices 122 such that a user of the customer computing devices 122 may view the answer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Khosla’s teaching of systems and methods for processing natural language queries with verification, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson, because, processing natural language queries with verification, by utilizing generative artificial intelligence (AI) models, such as, large language models (LLM), can provide accurate answers to customer (Khosla, Para.[0009],[0010]).
Regarding Claim 11, Henrickson in view of Khosla teach the method of claim 8. Henrickson further teaches, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging shows the response of the virtual character is limited to letters or one or two words, such as “yeah”, “I know”);
Claims 2, 3, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Everest et al. ( US 12,242,945 B1), hereinafter referenced as Everest.
Regarding Claim 2, Henrickson in view of Khosla teach the system of claim 1. Henrickson in view of Khosla fail to explicitly teach the claimed, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response.
However, Everest does teach the claimed, wherein the user interface is configured to display a graphical animation representing the synthetic user and the response, wherein the graphical animation is based on the response ( Everest: Column 47, lines 44-52, Fig. 1, the virtual avatar model 184 may be configured to receive an educational response and display an animation of the plurality of animations as a function of the educational response. Column 50, lines 64-67, Fig. 2, illustrates a graphical user interface 200 which is configured to receive the user interface structure and visually present any data)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Everest’s teaching of apparatus and methods for personalization of machine learning models, into the system and method, taught by Henrickson in view of Khosla, because, processing natural language queries with verification, by generating user specific output by using personalized machine learning models, customer interaction can be improved. (Everest, Column 2, 3).
Claim 12 is a method claim performing the steps in system claim 2 above and as such, claim 12 is similar in scope and content to claim 2 and therefore, claim 12 is rejected under similar rationale as presented against claim 2 above.
Regarding Claim 3, Henrickson in view of Khosla, further in view of Everest teach the system of claim 2. Henrickson further teaches, wherein the designated virtual character role emulated by the generative AI model corresponds to a Ouija board, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers ( Henrickson: Page 952, Para.[1], thanbot’s ( an AI-powered chatbot trained on the digital communications, memories, and personality data of a deceased person) conversational style contributes to the perception of deceased, similar to Ouija board. Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging shows the response of the virtual character is limited to letters or one or two words, such as “yeah”, “I know”);
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Brackett et al. ( US 20120040319 A1), hereinafter referenced as Brackett.
Regarding Claim 4, Henrickson in view of Khosla teach the system of claim 1. Henrickson in view of Khosla fail to explicitly teach the claimed, wherein if the user input does not satisfy the set of corresponding predetermined model-driven conditions, the user interface displays a rejection animation.
However, Brackett does teach the claimed, wherein if the user input does not satisfy the set of corresponding predetermined model-driven conditions, the user interface displays a rejection animation ( Brackett: Para.[0049], [0049], Fig. 10, if the medicine bottle is not the correct bottle in accordance with previous instructions provided to the user, then the viewing screen of the interactive window can display a rejection symbol).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brackett’s teaching of system and method for compliance with rules, into the system and method, taught by Henrickson in view of Khosla, because by complying with the rules, efficient system can be achieved. (Brackett, Para.[0002]-[0008]).
Claims 5, 10, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Abramson et al. (US 20180293483 A1), hereinafter referenced as Abramson.
Regarding Claim 5, Henrickson in view of Khosla teach the system of claim 1. Henrickson in view of Khosla fail to explicitly teach the claimed, wherein the designated virtual character role is based on a market sector participant, wherein the generative AI model is configured to generate responses tailored to preferences of the market sector participant via the at least one hidden parameter.
However, Abramson does teach the claimed, wherein the designated virtual character role is based on a market sector participant, wherein the generative AI model is configured to generate responses tailored to preferences of the market sector participant via the at least one hidden parameter ( Abramson: Para.[0029], Fig. 2, Chatbot engine 208 establish a set of interaction rules for a chatbot, which may provide or determining when (and in what order) to utilize the data and various data sources available to index engine 206. As an example, chat bot engine 208 may establish a rule set dictating that, in response to receiving dialogue input, a specific chat bot may attempt to provide a response using data from the following data sets (in order): 1) social data from a specific person/ entity, 2) social data from users similar to the specific person/entity, 3) social data from a global user base (such as the internet at large) that may or may not be similar to the specific person/entity, and 4) generic, catch all phrases/ questions that are not specific to the specific person/entity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Abramson’s teaching of systems and methods of creating a conversational chat bot of a specific person using social data, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by creating a conversational chat bot of a specific person or specific entity, user’s experience can be enhanced (Abramson, Para.[0001]-[0004]).
Regarding Claim 10, Henrickson in view of Khosla teach the method of claim 8. Henrickson in view of Khosla fail to explicitly teach the claimed, wherein the generative AI model is further configured to adapt the designated virtual character role based on at least one historical user interaction.
However, Abramson does teach the claimed, wherein the generative AI model is further configured to adapt the designated virtual character role based on at least one historical user interaction ( Abramson: Para.[0031], the social data for a historical figure (such as Abraham Lincoln) may include handwritten letters and similar correspondences authored by the historical figure, books authored or about the historical figure, information related to the relevant time period associated with the historical figure, physical media comprising audio data and/or video data, photos, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Abramson’s teaching of systems and methods of creating a conversational chat bot of a specific person using social data, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by creating a conversational chat bot of a specific person or specific entity, user’s experience can be enhanced (Abramson, Para.[0001]-[0004]).
Regarding Claim 14, Henrickson in view of Khosla teach the method of claim 8. Henrickson in view of Khosla fail to explicitly teach the claimed, wherein the at least one hidden parameter includes at least one temporal element, including a time factor.
However, Abramson does teach the claimed, wherein the at least one hidden parameter includes at least one temporal element, including a time factor ( Abramson: Para.[0024],[0028], Fig. 2, data store(s) 204 may store social data by a user identification of a specific person associated with the social data, date/time, social data subject/topic, social data type, or the like. Index engine 206 may have access to one or more data sources comprising generic conversation data, which may comprise scripted and/or pre-generated automatic questions/replies, generic conversational and time period-based algorithms/models, and personality-neutral social data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Abramson’s teaching of systems and methods of creating a conversational chat bot of a specific person using social data, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by creating a conversational chat bot of a specific person or specific entity, user’s experience can be enhanced (Abramson, Para.[0001]-[0004]).
Claims 7, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Gelfenbeyn et al. (US 20230351120 A1), hereinafter referenced as Gelfenbeyn.
Regarding Claim 7, Henrickson in view of Khosla teach the system of claim 1. Henrickson in view of Khosla fail to explicitly teach, wherein the set of corresponding predetermined model-driven conditions encompass a set of criteria for at least one emotional expression, wherein the set of criteria enables the response to convey the at least one emotional expression tailored to the designated virtual character role.
However, Gelfenbeyn does teach the claimed, wherein the set of corresponding predetermined model-driven conditions encompass a set of criteria for at least one emotional expression, wherein the set of criteria enables the response to convey the at least one emotional expression tailored to the designated virtual character role ( Gelfenbeyn: Para.[0027],[0028], The AI character model utilize a LLM in conversations with the users. In order to obtain more effective and appropriate responses to user questions and messages, the platform may apply various restrictions, classifications, shortcuts, and filters in response to user questions. These targeted requests to the LLMs will result in optimized performance. The AI character model may evolve its characteristics, change emotions, and acquire knowledge based on conversations with the users).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gelfenbeyn’s teaching of systems and methods for observation-based training of an Artificial Intelligence (AI) character model, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by allowing virtual character models to train to change their interactions with users based on observing interactions between users and virtual characters, can improve user’s experience. (Gelfenbeyn, Para.[0003],[0004]).
Regarding Claim 9, Henrickson in view of Khosla teach the method of claim 8. Henrickson in view of Khosla fail to explicitly teach, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
However, Gelfenbeyn does teach the claimed, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback (Gelfenbeyn: Para.[0109], an AI character can be built from the data log of a real human actor. The log data ( hidden parameter) can be used to train the AI character model. The learning of the AI character model may include a feedback loop that receives the results showing whether the AI character model works. Based on the results, the behavior of the AI character generated by the AI character model may be adjusted).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gelfenbeyn’s teaching of systems and methods for observation-based training of an Artificial Intelligence (AI) character model, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by allowing virtual character models to train to change their interactions with users based on observing interactions between users and virtual characters, can improve user’s experience. (Gelfenbeyn, Para.[0003],[0004]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Rodgers et al. (US 12231380 B1), hereinafter referenced as Rodgers.
Regarding Claim 13, Henrickson in view of Khosla teach the method of claim 8. Henrickson in view of Khosla fail to explicitly teach, wherein the plurality of independent validation models include a sentiment analysis component to assess user emotions expressed in the user input .
However, Rodgers does teach the claimed, wherein the plurality of independent validation models include a sentiment analysis component to assess user emotions expressed in the user input (Rodgers: Column 35, lines 37-47, Fig. 1, the sentiment pathways 168 are sequences of steps or rules used by the sentiment analyzer 134 to understand, interpret, and appropriately respond to the sentiment or emotional state expressed by a user during a chat conversation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rodger’s teaching of systems and methods of using generative AI chatbots to gather and enhance information for facilitating resolution of a service request, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by detecting user’s emotion during the conversation, customer service chatbot can efficient handle the request. (Rodgers, Column 2, 3).
Claims 15, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of González-González et al. ( Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal, Sensors (Basel, Switzerland), 23(1), 545, January, 2023), hereinafter referenced as González-González, further in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla.
Regarding Claim 15, Henrickson teaches a system for dynamic interaction with a synthetic user for Ouija board gameplay, comprising:
a user interface that is communicatively connected to a generative AI model that is configured to dynamically generate a response based on user input to the user interface ( Henrickson: Page 950, para.[3], page 953, para.[2],[3], page 954, para.[2], “Project December”, which allows users to train their own chatbots, using OpenAI’s GPT-3 (Generative Pre-trained Transformers), connected with textual user interface and generates dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), which was generated based on his deceased fiancé),
wherein the response is derived from at least one hidden parameter, wherein the at least one hidden parameter is pre-loaded into the generative AI model and directs the generative AI model to emulate specific attributes associated with a designated virtual character role of a Ouija board ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging between a man and the chatbot named “Jessbot’’( synthetic user), is based on the old texts and Facebook messages of the deceased fiancé. Page 952, Para.[1], the conversational style contributes to the perception of deceased, similar to Ouija board);
Henrickson while teaching the system of claim 15, fails to explicitly teach the claimed, and wherein the user interface comprises a plurality of response indicators and a viewing port, wherein the viewing port is positioned to indicate a particular response indicator on the user interface corresponding to the response; a model superstructure interconnecting the user interface, the generative AI model, and a plurality of validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions; and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
However, González-González does teach the claimed, and wherein the user interface comprises a plurality of response indicators and a viewing port, wherein the viewing port is positioned to indicate a particular response indicator on the user interface corresponding to the response ( González-González: Section 5.3, 5.4 illustrates building of a educational/learning gaming system by integrating a conversational Chatbot. Several functional modules are used to build the educational chatbots. Natural language understanding subsystems look for intents and entities to understand the meaning of user entries, the dialog manager designs the possible conversation, natural language generation generates the answer, which can be in different formats (video, audio, images, buttons, etc.) or standard text. Although the essential bidirectional communication with a chatbot is through text in the interaction module ( user interface) and can be through selectors ( response indicators). A script and a dialogue control system must be created to allow a chatbot to converse with the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate González-González’s teaching of systems, methods and architecture for integrating conversational bots in learning game, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson, because, by including social, cultural, and individual backgrounds for the personalization and the support of gamified chatbots on self-efficacy and self-regulated skills, which positively impact the students’ autonomy (González-González, Section 7).
Henrickson in view of González-González while teaching the system of claim 15, fails to explicitly teach the claimed, a model superstructure interconnecting the user interface, the generative AI model, and a plurality of validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions; and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module.
However, Khosla does teach the claimed, a model superstructure interconnecting the user interface, the generative AI model, and a plurality of validation models, wherein the model superstructure is configured to trigger evaluations, by the plurality of validation models via the model superstructure, of the response to determine satisfaction of a set of corresponding predetermined model-driven conditions ( Khosla: Para.[0049], [0056], Fig. 2B, The verifier component 108 is configured to analyze whether answers provided by the LLM component 106 is valid or not, by utilizing a set of corresponding modules (textual overlap module 234, NLI module 236, relational NLI module 238, membership inference attack module 240) and conditions. Para.[0059]-[0068], Fig.3 illustrates different components of the natural language question answering service 102 based on analyzing a natural language question, consists of user interface (UI) of the customer computing device 122, LLM component 106 which sends the generated answer and retrieved passages to the verifier component 108);
and a consensus module configured to deliver the response to the user interface, in response to a determination that each of the set of corresponding predetermined model-driven conditions is satisfied by the consensus module ( Khosla: Para.[0068], Fig. 3, the watermarking component 110 ( consensus module) sends the watermarked answer and retrieved passages to the customer computing devices 122 such that a user of the customer computing devices 122 may view the answer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Khosla’s teaching of systems and methods for processing natural language queries with verification, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of González-González, because, processing natural language queries with verification, by utilizing generative artificial intelligence (AI) models, such as, large language models (LLM), can provide accurate answers to customer (Khosla, Para.[0009],[0010]).
Regarding Claim 17, Henrickson in view of González-González, further in view of Khosla teach the system of claim 1. Henrickson further teaches, wherein the specific attributes associated with the designated virtual character role include limiting the response, via the at least one hidden parameter, to letters or numbers, and providing one- or two-word answers ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction as shown in the text messaging shows the response of the virtual character is limited to letters or one or two words, such as “yeah”, “I know”).
Regarding Claim 19, Henrickson in view of González-González, further in view of Khosla teach the system of claim 15. Henrickson further teaches, wherein the at least one hidden parameter includes a set of communication styles of a user selected deceased person, enabling the generative AI model to produce responses consistent with a set of linguistic patterns of the user selected deceased person ( Henrickson: Page 950, para.[3], page 954, para.[2], the system (OpenAI’s GPT-3) is trained with old SMS and Facebook messages of the deceased fiancé , to emulate the stylistic and semantic patterns ( specific attributes, hidden parameters) of the provided dataset. The response/dynamic interaction generated by GPT-3, as shown in the text messaging shows the response of the deceased fiancé).
Claims 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of González-González et al. ( Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal, Sensors (Basel, Switzerland), 23(1), 545, January, 2023), hereinafter referenced as González-González, further in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Abramson et al. (US 20180293483 A1), hereinafter referenced as Abramson.
Regarding Claim 16, Henrickson in view of González-González, further in view of Khosla teach the system of claim 15. Henrickson in view of González-González, further in view of Khosla fail to explicitly teach the claimed, wherein the plurality of validation models includes a temporal relevance check, wherein the temporal relevance check assesses an indication of suitability of the response in relation to whether a user selected deceased person is actually deceased.
However, Abramson does teach the claimed, wherein the plurality of validation models includes a temporal relevance check, wherein the temporal relevance check assesses an indication of suitability of the response in relation to whether a user selected deceased person is actually deceased ( Abramson: Para.[0017], a chatbot can be trained by using personalized personality index ( created from social data of a specific person) of a specific person. Para.[0033], a personalized personality index may comprise social data relating to a deceased relative of a user. A set of data acquisition rules may be assigned to the personalized personality index to provide instructions for acquiring data related to various time periods of the deceased relative's lifetime (e.g., before, during and/or after the lifetime) ( temporal relevance check). Such instruction may include asking a user questions about a time period, one or more events and/or people, or asking a user where such information may be obtained. Such questions may indicate the specific person represented by the personalized personality index (e.g., the deceased relative) possesses a perceived awareness that he/she is, in fact, deceased).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Abramson’s teaching of systems and methods of creating a conversational chat bot of a specific person using social data, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of González-González, further in view of Khosla, because, by creating a conversational chat bot of a specific person or specific entity, user’s experience can be enhanced (Abramson, Para.[0001]-[0004]).
Regarding Claim 18, Henrickson in view of González-González, further in view of Khosla teach the system of claim 15. Henrickson in view of González-González, further in view of Khosla fail to explicitly teach the claimed, wherein the at least one hidden parameter includes a set of historical knowledge constraints, restricting the generative AI model to respond within a user selected deceased person's known historical context.
However, Abramson does teach the claimed, wherein the at least one hidden parameter includes a set of historical knowledge constraints, restricting the generative AI model to respond within a user selected deceased person's known historical context ( Abramson: Para.[0030]-[0032], Fig. 3 illustrates the creation of a conversational chat bot of a specific person. At 304, social data for the specific person/ entity may be accessed. The social data ( hidden parameter) for a historical figure (such as Abraham Lincoln) may include handwritten letters and similar correspondences authored by the historical figure, books authored or about the historical figure, information related to the relevant time period associated with the historical figure, physical media comprising audio data and/or video data, photos, etc. At 306, personality index is created, which will restrict accessing the data ( data can accessed based on personality index)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Abramson’s teaching of systems and methods of creating a conversational chat bot of a specific person using social data, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of González-González, further in view of Khosla, because, by creating a conversational chat bot of a specific person or specific entity, user’s experience can be enhanced (Abramson, Para.[0001]-[0004]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Henrickson et al. ( Chatting with the dead: The hermeneutics of thanabots, Media, Culture & Society, Vol. 45, issue 5, PP 949-966, July, 2023), hereinafter referenced as Henrickson, in view of González-González et al. ( Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal, Sensors (Basel, Switzerland), 23(1), 545, January, 2023), hereinafter referenced as González-González, further in view of Khosla et al. (US 20250005052 A1), hereinafter referenced as Khosla, further in view of Gelfenbeyn et al. (US 20230351120 A1), hereinafter referenced as Gelfenbeyn.
Regarding Claim 20, Henrickson in view of González-González, further in view of Khosla teach the system of claim 15. Henrickson in view of González-González, further in view of Khosla fail to explicitly teach, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback.
However, Gelfenbeyn does teach the claimed, further comprising a feedback loop mechanism for iteratively enhancing alignment between the at least one hidden parameter and the designated virtual character role based on user feedback (Gelfenbeyn: Para.[0109], an AI character can be built from the data log of a real human actor. The log data ( hidden parameter) can be used to train the AI character model. The learning of the AI character model may include a feedback loop that receives the results showing whether the AI character model works. Based on the results, the behavior of the AI character generated by the AI character model may be adjusted).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gelfenbeyn’s teaching of systems and methods for observation-based training of an Artificial Intelligence (AI) character model, into the system and method of creation of synthetic user using OpenAI’s GPT, taught by Henrickson in view of Khosla, because, by allowing virtual character models to train to change their interactions with users based on observing interactions between users and virtual characters, can improve user’s experience. (Gelfenbeyn, Para.[0003],[0004]).
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Bean et al. (US 20250114699 A1) teaches a methods and systems for personalizing an avatar for a user for use in a game includes extracting features of the avatar associated with the user. Receiving inputs from the user for customizing at least a feature of the game that is identified as customizable. Constraints of the game are determined and used during updating of a skin of the avatar, wherein the skin of the avatar is updated by adjusting the feature to include the attribute customized using the inputs of the user. The updated skin used to generate an updated avatar that is stored in a user profile of the user and used to represent the user in the game.
Zelenin et al. (US 20170132828 A1) teaches a control system which provides an interface for virtual characters, or avatars, during live avatar-human interactions. A human interactor can select facial expressions, poses, and behaviors of the virtual character using an input device mapped to menus on a display device.
Börütecene et al. (Otherworld: Ouija Board as a Resource for Design, HttF '19: Proceedings of the Halfway to the Future Symposium 2019, Article No.: 4, Pages 1 - 4) teaches the Ouija board to contact spirits from the so-called otherworld. Although it is considered paranormal activity, the way it works rests on ideomotor actions and we argue that the Ouija is a resource for design for the following aspects: It is a 1) collective tangible exploration tool operated by two or more people through a physical pointer that moves, seemingly on its own, around the letters to probe meanings by composing messages. It has been used by artists as a medium offering 2) creative stimulation to generate material and develop ideas for their work. The Ouija also enables people to express nonconscious knowledge, as research suggests, and thus can provide 3) access to tacit dimension. In this paper, we present the Otherworld Framework that describes its principal elements and provide speculations on how to exploit them in design for collaborative, engaging and unconventional group interactions.
Joypriyanka et al. (CheckersMind: Enhancing Cognitive Ability in Dimentia Patients through Checkers Game Therapy with Chatbot, IEEE, Feb, 2023 ) teaches how reinforcement learning could be used to improve the checkers-playing experience for people with dementia as part of a therapeutic gaming environment. In order to train an agent that can aid in both offensive and defensive strategies, the Soft Actor-Critic (SAC) reinforcement learning algorithm is used. The fundamental goal of this research is to find ways to stimulate the brains of people with dementia in order to improve their cognitive abilities, such as memory, problem-solving, and observational skills. The Deep Deterministic Policy Gradients (DDPG) and the Soft Actor-Critic reinforcement learning algorithms are compared and contrasted. The results show that the Soft Actor-Critic algorithm excels in this setting. This study adds to the growing body of evidence supporting the use of game-based therapies in the treatment of dementia, with the aim of improving patients' cognitive abilities and quality of life.
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