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 .
Status of Claims
This action is in reply to the amendment filed on 1/29/2026.
Claims 1, 5, 7-8, 12, 15, and 18-20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Response to Applicant’s Arguments
Objections
The present amendments to the claims obviate all previous objections thereto with the exception of the objection to Claim 7 regarding “a subset the plurality of content.” As such, this objection is maintained and all others are withdrawn.
Claim Rejections – 35 USC § 112
The present amendments to the claims obviate the previous 112(b) rejections; therefore, these rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant begins the present 101 arguments with several almost entirely conclusory assertions regarding the application of Step 2A, Prong One standards to the claims as presently amended. These arguments misapprehend the standards of Step 2A, Prong One in multiple ways.
Firstly, these arguments reference some discrete features of the claims, e.g., use of generative AI techniques and storage/retrieval of content items in/from a vector database, as support of the overall conclusion that the claims do not recite any abstract ideas. This is not in keeping with the performance of Step 2A, Prong One, wherein claims are analyzed for recitation of judicial exceptions (including abstract ideas) on a limitation-by-limitation basis. See, e.g., the numerous examples of individual limitations which recite various abstract ideas found in MPEP 2106.04(a)(2) and the subsections thereof, as well as the analyses set forth regarding the Examples of the most recent PEG Update. Even if the argued features prevented recitation of abstract ideas for those features themselves (which, to be clear, they do not – see further analysis below), this would not prevent other claim elements (including elements previously identified as reciting judicial exceptions for which no present arguments are presented) from reciting judicial exceptions.
Secondly, Applicant presents various arguments that limitations claimed as being effectuated by computer elements (both in the form of more high-level disclosure of steps being performed by structure such as processors and memory, as well as narrower software-based performance of steps using generative AI techniques) cannot recite abstract ideas (under multiple subcategories thereof). These arguments are untrue regardless of the particular category of abstract idea (ie: certain methods of organizing human activity, mental processes, and mathematical concepts). The aforementioned examples of 2106.04(a)(2) and the subsections thereof as well as the Examples of the most recent PEG Update contain numerous examples of similarly computer-implemented steps reciting abstract ideas. Indeed, MPEP 2106.04(a)(2)(III)(C) (entitled “A Claim That Requires a Computer May Still Recite a Mental Process”) is entirely devoted to refuting this erroneous notion. Similarly, MPEP 2106.04(a)(2)(II) makes clear that activity performed by or in relation to computers may still recite abstract ideas under the category of certain methods of organizing human activity (e.g., “(such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”).
Further, this assertion flies in the face of the holding and content of the seminal Alice, to say nothing of the wealth of subsequent caselaw which as applied the Alice-Mayo subject matter eligibility test to find any number of computer-performed functions which nonetheless recite abstract ideas. See, e.g., FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016), Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017), SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 127 U.S.P.Q.2d 1597 (Fed. Cir. 2018), Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363 (Fed. Cir. 2019), Beteiro, LLC v. DraftKings, Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024), etc. Particularly regarding AI-based applications, Examples 47-49 each contain claims which illustrate that steps explicitly utilizing AI may still recite abstract ideas, depending on the actual content of the steps themselves.
Regarding the presently amended content of the claims, Applicant makes unexplained assertions that the recording, retrieving, and consideration of variables (as claimed, “content items”) specifically in vector form cannot recite abstract ideas, e.g., such steps are purportedly “computationally intensive” and “[u]nlike simple observations, evaluations, judgments, or opinions, the human mind is not equipped to store and retrieve content items in a vector database or generate content using generative artificial intelligence techniques based on the retrieved items.” Examiner disagrees. Examiner sees nothing inherently technological or especially complex about such recording, retrieving, and consideration of variables in vector format which would prevent the claimed limitations from reciting abstract ideas. Indeed, the ability of the human mind to record, comprehend, and perform calculations/observations in relation to data in a vector format should be abundantly clear to anyone who has taken a high school-level algebra course. Further, the evaluation of such user data in vector format to generate content with respect to a specific subject, particularly as presently claimed at an extremely high level, reads as reciting little more than mentally performable analysis, pattern recognition, and extrapolation, said mentally performable steps claimed at a high level as using generative AI techniques. To be clear, while these generative AI techniques themselves are non-abstract additional elements, and were properly considered as such both previously and presently, the function performed using these techniques continues to recite abstract ideas under multiple categorizations.
Applicant next presents arguments in relation to Step 2A, Prong Two, particularly arguing that the claims embody an improvement to a technology, ie: “the claimed approach imparts the technological improvement of adapting generative AI to mimic users, including users who are unavailable such as experts who have departed a company.” Applicant further asserts that this purported technological improvement is effectuated by way of the limitations of the independent claims as presently amended. Examiner disagrees.
Regarding the standards of improvements to a technology under the Prong Two analysis, MPEP 2106.05(a) states in part that “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Examiner finds that the analysis of data associated with an individual (e.g., a subject matter expert, including one who has perhaps departed a company as argued, though not as claimed) to extrapolate the knowledge and/or expertise of that individual, is generally an abstract concept and manually performable endeavor. It is well-settled that mere automation of a manual process is insufficient to show such an improvement (see Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) and LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016)). As presently drafted, the claims at best relate to an improvement to this abstract endeavor rather than an improvement to a technology.
Regarding the technological elements of the claims as presently drafted, e.g., the claimed generative AI techniques used to in part perform this extrapolation, the claims similarly fail to meet the standards of embodying an improvement to the technology of artificial intelligence itself (e.g., in contrast to the improvement to machine learning achieved in Applicant’s referenced Ex Parte Desjardins). Indeed, the claims are entirely silent on any technological details as to how these claimed generative AI techniques function such that an improvement to AI might be achieved. Instead, the claims recite the ”use [of] generative artificial intelligence techniques to” vaguely generate a result based on analysis of subject-specific content items in an entirely results-based manner. In this way, the claims are “drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results. Claims of this nature are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings, Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024).
In other words, as stated in MPEP 2106.05(f), rather than actually embodying an improvement to a technology, “the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.” Indeed, as recited, the broadest reasonable interpretation, the scope of “use generative artificial intelligence techniques to generate, based at least in part on the plurality of content items associated specifically with the user that are retrieved from the vector database, a generated content reflecting information derived from the plurality of content items with respect to a specific subject” would include use of pre-existing generative artificial intelligence techniques. In such a case, Applicant has not demonstrated an improvement to the technology of artificial intelligence, but rather merely applies already developed and invented AI techniques to a particular field of use. As such, there is no way one of ordinary skill in the art at the time of filing could reasonably conclude that the claims as presently drafted embody an improvement to a technology. Rather, “the claim language here provides only a result-oriented solution, insufficient detail for how [it is accomplished]. Our law demands more.” Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017). This is in stark contrast to the inventions of the Finjan, McRO, Visual Memory, and DDR Holdings cases to which Applicant makes entirely unsupported analogies. While each of these cases demonstrate a software-based improvement to a technology, Applicant cannot say that the claims as presently drafted do the same for the reasons explained above.
Examiner further notes that the Weisner v. Google holding cited by Applicant is not applicable here, as what Applicant cites to is not a final decision on the merits (much less a holding that the inventions thereof are subject matter eligible), but rather merely find that Google failed to carry their burden of showing that there was no genuine dispute of material fact in a motion to dismiss, the analysis of said facts requiring a conclusion that the patents in question were subject matter ineligible as a matter of law. As this is not a final holding on the merits, and is made using significantly different standards than that of examination before the USPTO (ie: preponderance of the evidence), Applicant’s analogy here is at best premature even ignoring the conclusory nature thereof.
This failure is not cured by any of Applicant’s supporting arguments. Summarily, that particular cited passages of the present specification might assert a technological improvement or discuss a purported improvement in technological terms, that does not make it so. Similarly, that the specification “makes clear that a technical problem that existed in the prior art prior to the development of the claimed approach was that systems did not exist for responding to questions for experts, especially when the experts themselves were unavailable” (or, more accurately, the specification asserts this rather than makes it clear that this is true) does not make it so. Indeed, while subject matter eligibility under 101 is a separate standard from those of 102 and 103, several references cited in the previous and present 103 rejections attempt to address this very goal. As the claims do not embody an improvement to technology as asserted by Applicant, the claims are not patent eligible as per the analyses set forth in the updated 101 rejections below.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Applicant’s arguments, all based on the newly recited vector-based claim elements, are moot in view of the updated 103 rejections below. Despite Applicant’s assertions of “careful review[s]” of the previously cited references regarding such vector functionalities, the primary Kelkar reference itself does indeed disclose such features (see updated 103 rejections below for more information). Even were this not the case, various previously recited secondary references also contain vector-related disclosure which could be combined with Kelkar to make obvious the claims as presently amended.
Claim Objections
Claim 7 is objected to because of the following informality: “a subset the plurality of content” should read “a subset of the plurality of content.” Appropriate correction is required.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claims 1, 19, and 20, the limitations of store, in a vector database, data comprising a plurality of content items associated specifically with a user; retrieve the plurality of content items associated specifically with the user from the vector database; and generate, based at least in part on the plurality of content items associated specifically with the user that are retrieved from the vector database, a generated content reflecting information derived from the plurality of content items with respect to a specific subject, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of store, in a vector database, data comprising a plurality of content items associated specifically with a user; retrieve the plurality of content items associated specifically with the user from the vector database; and generate, based at least in part on the plurality of content items associated specifically with the user that are retrieved from the vector database, a generated content reflecting information derived from the plurality of content items with respect to a specific subject, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions; a memory; a processor coupled to the memory and configured to execute instructions; and generative artificial intelligence techniques. In the context of the claims as a whole, these amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.
Claims 2-18, describing various additional limitations to the system of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses wherein the plurality of content items includes a first set of items associated with a first platform, channel, or service and a second set of items associated with a second platform, channel, or service (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 3 discloses wherein the plurality of content items includes a plurality of communications sent to the user (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 4 discloses wherein the plurality of content items includes a plurality of files, documents, or other stored objects associated with the user (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 5 discloses wherein the processor is further configured to store the plurality of content items in the vector database (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 6 discloses wherein the processor is further configured to store the plurality of content items in a manner that associates each item with the user (an abstract idea in the form of a certain method of organizing human activity and a mental process); and which associates with the subject a subset of items that relate to the subject (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim 7 discloses wherein the processor is configured to use the generative artificial intelligence techniques to generate the generated content at least in part by using at least a subset the plurality of content items associated specifically with the user to perform retrieval augmented generation with respect to a query in response to which the generated content was generated (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 8 discloses wherein the processor is configured to use the generative artificial intelligence techniques to generate the generated content at least in part by using at least a subset of the plurality of content items associated specifically with the user to fine tune a large language model (LLM) (mere instructions to apply a judicial exception) used to generate the generated content (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 9 discloses wherein the generated content comprises a summary of at least a subset of the plurality of content items (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 10 discloses wherein the summary is displayed to the user in the form of a dashboard or other summary display (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 11 discloses wherein the generated content is generated in response to a query from a requesting party other than the user (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 12 discloses wherein the generated content is displayed to the user prior to being sent to the requesting party, via an interactive user interface (mere instructions to apply a judicial exception) that enables the user to modify the generated content prior to its being sent to the requesting party (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 13 discloses wherein the generated content is generated in response to receipt of an indication of a need to update an enterprise knowledge base with respect to the specific subject (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 14 discloses wherein the processor is further configured to apply a policy to the generated content (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 15 discloses wherein the processor is configured to generate the generated content based at least in part on an indication that the user is not available to provide the generated content (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 16 discloses wherein the unavailability of the user may be due to one or more of time of day, vacation or other absence, the user no longer being employed by an enterprise with which the system is associated, and the user being deceased (further defines the abstract idea already set forth in Claim 15), which does not integrate the claim into a practical application.
Claim 17 discloses wherein the processor is further configured to identify the user as an expert with respect to the specific subject (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 18 discloses wherein the processor is configured to identify the user as an expert with respect to the specific subject at least in part by sending a query to each of a plurality of digital twins (mere instructions to apply a judicial exception), each configured to use generative artificial intelligence and the plurality of content items to generate a response on behalf of a respective user (an abstract idea in the form of a certain method of organizing human activity and a mental process); receive from each digital twin a corresponding response (an abstract idea in the form of a certain method of organizing human activity and a mental process); and use a large language model (mere instructions to apply a judicial exception) to determine based at least in part on the responses that the user is an expert with respect to the specific subject (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 11, 13-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kelkar et al (PGPub 20230274095) (hereafter, “Kelkar”) in view of Ritchey et al (PGPub 20230308605) (hereafter, “Ritchey”).
Regarding Claims 1, 19, and 20, Kelkar discloses a system configured to store, in a vector database, data comprising a plurality of content items (Abstract; ¶ 0003, 0009, 0014, 0024, 0036-0037, 0071-0072, 0079-0082, 0109; Figs. 1-3, 5; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; the present invention allows an entity to create an autonomous Conversational AI system (ACAI) by letting a software system inspect past conversations available in email exchanges or chatbots or voice call logs; this ACAI system is capable of interacting with humans by mimicking the conversations observed in the conversation logs; Fig. 5 shows a sample PII-removed conversation between a User and a Human Agent found in the conversation logs; backend systems include but are not limited to databases, software, etc. and the system can read from or write to backend systems; to maximize coverage of topics and subtopics the ACAI system will handle, while using a small number of initial conversations, a Conversation Ranker module identifies the top k most frequent auto-topic and auto-subtopic pairs, and selects the n most representative conversations for each auto-topic and auto-subtopic pair; it does so by calculating a normalized mean conversation embedding of all the conversations in each auto-topic and auto-subtopic pair, and choosing the n conversations closest to the normalized mean conversation embedding; a conversation embedding is a vector representation of the conversation in a multi-dimensional space with the property that similar conversations will have conversation embeddings that are closer to each other in the multi-dimensional space and conversely, dissimilar conversations will have conversation embeddings that are farther apart in the multi-dimensional space). Kelkar does not explicitly disclose but Ritchey does disclose wherein the content items are associated specifically with a user; the system comprising a memory configured to store data (Abstract; ¶ 0002, 0005, 0046, 0085; Figs. 4, 11; an enterprise system and method for maintaining and transitioning humans to a supplementary adaptable sentient self-reliant entity is presented, said system including at least one entity with an artificial neural network to at least one transform and maintain; in one objective embodiment the recipient biological system can operate as a personal assistant; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; the enterprise system to produce a family of compatible recurrent capable biological, bio-mechatronic, and mechatronic systems that emulate at least one specific person or derivation of a person; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like).
Kelkar does not explicitly disclose but Ritchey does disclose:
a processor coupled to the memory (¶ 0085; Fig. 11; Claim 1; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like; said neural activity within the biomechatronic or mechatronic system emulating the cognition of a biological being consisting of at least one artificial neural network with back propagation by incorporating generative artificial intelligence); and
a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions (¶ 0085, 0097; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like; the host computer includes either software (written programs or procedures or rules and associated documentation pertaining to the operation of a computer system and that are stored in read/write memory) and/or firmware ( coded instructions that are stored permanently in read-only memory)).
Kelkar additionally discloses retrieve the plurality of content items associated specifically with the user from the vector database (¶ 0009, 0014, 0036-0037, 0071-0072, 0079-0082; to maximize coverage of topics and subtopics the ACAI system will handle, while using a small number of initial conversations, a Conversation Ranker module identifies the top k most frequent auto-topic and auto-subtopic pairs, and selects the n most representative conversations for each auto-topic and auto-subtopic pair; it does so by calculating a normalized mean conversation embedding of all the conversations in each auto-topic and auto-subtopic pair, and choosing the n conversations closest to the normalized mean conversation embedding; a conversation embedding is a vector representation of the conversation in a multi-dimensional space with the property that similar conversations will have conversation embeddings that are closer to each other in the multi-dimensional space and conversely, dissimilar conversations will have conversation embeddings that are farther apart in the multi-dimensional space).
Kelkar additionally discloses use generative artificial intelligence techniques to generate, based at least in part on the plurality of content items that are retrieved from the vector database, a generated content reflecting information derived from the plurality of content items with respect to a specific subject (Abstract; ¶ 0009, 0014-0015, 0024, 0036-0042, 0057, 0071-0073, 0075, 0079-0082, 0085; Figs. 1, 4; Claims 1, 8; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; this ACAI system is capable of interacting with humans by mimicking the conversations observed in the conversation logs; Sentence-level Auto-Intent annotation enables the ACAI System to respond to longer messages, typically found in emails; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model; to maximize coverage of topics and subtopics the ACAI system will handle, while using a small number of initial conversations, a Conversation Ranker module identifies the top k most frequent auto-topic and auto-subtopic pairs, and selects the n most representative conversations for each auto-topic and auto-subtopic pair; it does so by calculating a normalized mean conversation embedding of all the conversations in each auto-topic and auto-subtopic pair, and choosing the n conversations closest to the normalized mean conversation embedding; a conversation embedding is a vector representation of the conversation in a multi-dimensional space with the property that similar conversations will have conversation embeddings that are closer to each other in the multi-dimensional space and conversely, dissimilar conversations will have conversation embeddings that are farther apart in the multi-dimensional space). Kelkar does not explicitly disclose but Ritchey does disclose wherein the content items are associated specifically with the user (Abstract; ¶ 0002, 0005, 0046, 0085; Figs. 4, 11; Claim 1; an enterprise system and method for maintaining and transitioning humans to a supplementary adaptable sentient self-reliant entity is presented, said system including at least one entity with an artificial neural network to at least one transform and maintain; in one objective embodiment the recipient biological system can operate as a personal assistant; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; the enterprise system to produce a family of compatible recurrent capable biological, bio-mechatronic, and mechatronic systems that emulate at least one specific person or derivation of a person; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like; said neural activity within the biomechatronic or mechatronic system emulating the cognition of a biological being consisting of at least one artificial neural network with back propagation by incorporating generative artificial intelligence).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the AI creation and use techniques of Ritchey with the AI response system of Kelkar because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Ritchey are applicable to the base device (Kelkar), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 2, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the plurality of content items includes a first set of items associated with a first platform, channel, or service and a second set of items associated with a second platform, channel, or service (¶ 0025, 0061; conversation logs include, but are not limited to, the transcript of the conversation, voice recordings, and meta data pertaining to the conversation; this data includes but is not limited to multimodal conversational logs like chat logs, emails, voice recordings of conversations across multiple languages and omni-channel streams; the system handles different modes of conversational logs including but not limited to chat logs, email logs, and voice differently).
Regarding Claim 3, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the plurality of content items includes a plurality of communications sent to a system (¶ 0025, 0061; conversation logs include, but are not limited to, the transcript of the conversation, voice recordings, and meta data pertaining to the conversation; this data includes but is not limited to multimodal conversational logs like chat logs, emails, voice recordings of conversations across multiple languages and omni-channel streams; the system handles different modes of conversational logs including but not limited to chat logs, email logs, and voice differently). Kelkar does not explicitly disclose but Ritchey does disclose wherein the plurality of content items relate to the user (¶ 0005; a Neural Correlates of Consciousness (NCC) relational database of information, knowledge, and artifacts that define and enable an entity’s biological, bio-mechatronic, and mechatronic survival and operation in various environments needed to perform various tasks; in the present invention relational database derived from brain activity sensing and processing can be considered an artifact or a collection of artifacts; brain activity sensing system data of a subscriber, video footage of what subscriber is seeing, or processed data that comprises subscriber’s relational database are artifacts).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 4, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the plurality of content items includes a plurality of files, documents, or other stored objects (¶ 0025, 0061; conversation logs include, but are not limited to, the transcript of the conversation, voice recordings, and meta data pertaining to the conversation; this data includes but is not limited to multimodal conversational logs like chat logs, emails, voice recordings of conversations across multiple languages and omni-channel streams; the system handles different modes of conversational logs including but not limited to chat logs, email logs, and voice differently). Kelkar does not explicitly disclose but Ritchey does disclose wherein the plurality of content items relate to the user (¶ 0005; a Neural Correlates of Consciousness (NCC) relational database of information, knowledge, and artifacts that define and enable an entity’s biological, bio-mechatronic, and mechatronic survival and operation in various environments needed to perform various tasks; in the present invention relational database derived from brain activity sensing and processing can be considered an artifact or a collection of artifacts; brain activity sensing system data of a subscriber, video footage of what subscriber is seeing, or processed data that comprises subscriber’s relational database are artifacts).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 5, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar does not explicitly disclose but Ritchey does disclose wherein the processor is further configured to store the plurality of content items in the memory (¶ 0055-0056, 0058, 0062, 0080, 0085; storage of artifacts; collecting, maintaining, processing, and handing critical information consisting of personal information and items in the most secure manner possible by limiting access to a limited number of authorized users, creating secure backups, and storing physical and digital records and artifacts in a secure manner; Enterprise business architectures, including work groups, shown in FIG. 1 operates to manage the artifact collection, storage, processing for design, construction, testing 301, fielding, and maintenance for humanlike artificial intelligent entities according to the present invention; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like). Kelkar additionally discloses wherein the memory comprises the vector database (¶ 0009, 0036-0037, 0071-0072, 0079-0082; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; to maximize coverage of topics and subtopics the ACAI system will handle, while using a small number of initial conversations, a Conversation Ranker module identifies the top k most frequent auto-topic and auto-subtopic pairs, and selects the n most representative conversations for each auto-topic and auto-subtopic pair; it does so by calculating a normalized mean conversation embedding of all the conversations in each auto-topic and auto-subtopic pair, and choosing the n conversations closest to the normalized mean conversation embedding; a conversation embedding is a vector representation of the conversation in a multi-dimensional space with the property that similar conversations will have conversation embeddings that are closer to each other in the multi-dimensional space and conversely, dissimilar conversations will have conversation embeddings that are farther apart in the multi-dimensional space).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 6, Kelkar in view of Ritchey discloses the limitations of Claim 5. Kelkar does not explicitly disclose but Ritchey does disclose wherein the processor is further configured to store the plurality of content items in a manner that associates each item with the user (¶ 0055-0056, 0058, 0062, 0080, 0085; storage of artifacts; collecting, maintaining, processing, and handing critical information consisting of personal information and items in the most secure manner possible by limiting access to a limited number of authorized users, creating secure backups, and storing physical and digital records and artifacts in a secure manner; Enterprise business architectures, including work groups, shown in FIG. 1 operates to manage the artifact collection, storage, processing for design, construction, testing 301, fielding, and maintenance for humanlike artificial intelligent entities according to the present invention; components are connected by a system bus and/or electrical bus and include, but are not limited to, input/output jacks, a portable power system with a battery, interactive input devices, video card, hard drive for storing data, random access memory for storing volatile data, central processing systems, and the like; panoramic imagery and brain activity imagery representing the same subject matter that may be logged by a user/recipient).
Kelkar additionally discloses which associates with the subject a subset of items that relate to the subject (¶ 0028, 0033-0039; the normalized conversation logs go through a series of annotation steps to enrich the conversation logs with NLU annotations including but not limited to turn-level auto-intent, turn-level auto-response, auto-topic, auto-subtopic and sentence-level auto-intent).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 7, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the system is configured to use the generative artificial intelligence techniques to generate the generated content at least in part by using at least a subset the plurality of content items to perform retrieval augmented generation with respect to a query in response to which the generated content was generated (Abstract; ¶ 0015, 0024, 0036, 0057, 0073, 0075, 0082-0083, 0085, 0093-0094; Figs. 1, 4; Claims 1, 8; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; this ACAI system is capable of interacting with humans by mimicking the conversations observed in the conversation logs; Sentence-level Auto-Intent annotation enables the ACAI System to respond to longer messages, typically found in emails; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model; the Generative DNN discovers most auto-topics and auto-subtopics from the logs and it is good enough to be deployed; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model). Kelkar does not explicitly disclose but Ritchey does disclose wherein the content items are associated specifically with the user; system functions executed by way of a processor (Abstract; ¶ 0002, 0005, 0046, 0085; Figs. 4, 11; Claim 1; an enterprise system and method for maintaining and transitioning humans to a supplementary adaptable sentient self-reliant entity is presented, said system including at least one entity with an artificial neural network to at least one transform and maintain; in one objective embodiment the recipient biological system can operate as a personal assistant; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; the enterprise system to produce a family of compatible recurrent capable biological, bio-mechatronic, and mechatronic systems that emulate at least one specific person or derivation of a person; said neural activity within the biomechatronic or mechatronic system emulating the cognition of a biological being consisting of at least one artificial neural network with back propagation by incorporating generative artificial intelligence).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 11, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the generated content is generated in response to a query from a requesting party other than the user (¶ 0079, 0083-0085, 0093-0094; Fig. 1; when the ACAI System receives the incoming messages from users, it is able to predict the sentence-level auto-intent for every message; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model).
Regarding Claim 13, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the generated content is generated in response to receipt of an indication of a need to update an enterprise knowledge base with respect to the specific subject (¶ 0051, 0100-0104, 0107; a retraining process is triggered periodically; the new conversation logs from the Conversation AI Inbox are passed through the discovery process, leading to a new Automatic Conversational AI Configuration; this configuration file adds new stories to the existing configuration and then retrains the ACAI System).
Regarding Claim 14, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the processor is further configured to apply a policy to the generated content (¶ 0076; the AutoFlows Generator converts the conversations into a graph of sentence-level auto-intents and turn-level auto-responses; this graph may contain stories and flows; while stories and flows both represent conversations, a story cannot have branching conditions because it is always linear whereas flows can have branching conditions according to business logic or other conditions).
Regarding Claim 15, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar does not explicitly disclose but Ritchey does disclose wherein the processor is configured to generate the generated content based at least in part on an indication that the user is not available to provide the generated content (¶ 0005, 0058, 0125; it is therefore an objective of the present invention to develop a family of related personal assistant methods and systems that contribute to a Neural Correlates of Consciousness (NCC) relational database of information, knowledge, and artifacts that define and enable an entity’s biological, bio-mechatronic, and mechatronic survival and operation in various environments needed to perform various tasks; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; at the subscriber’s request, when a part or the whole of the subscriber’s biological body dies, subscriber could have their stored relational database installed into a human-like robot that mimic’s themselves).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 16, Kelkar in view of Ritchey discloses the limitations of Claim 15. Kelkar does not explicitly disclose but Ritchey does disclose wherein the unavailability of the user may be due to one or more of time of day, vacation or other absence, the user no longer being employed by an enterprise with which the system is associated, and the user being deceased (¶ 0005, 0058, 0125; it is therefore an objective of the present invention to develop a family of related personal assistant methods and systems that contribute to a Neural Correlates of Consciousness (NCC) relational database of information, knowledge, and artifacts that define and enable an entity’s biological, bio-mechatronic, and mechatronic survival and operation in various environments needed to perform various tasks; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; at the subscriber’s request, when a part or the whole of the subscriber’s biological body dies, subscriber could have their stored relational database installed into a human-like robot that mimic’s themselves).
The rationale to combine remains the same as for Claim 1.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kelkar in view of Ritchey and Fields et al (PGPub 20240303745) (hereafter, “Fields”).
Regarding Claim 8, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar additionally discloses wherein the processor is configured to use the generative artificial intelligence techniques to generate the generated content at least in part by using at least a subset of the plurality of content items associated specifically with the user to fine tune a language model used to generate the generated content (Abstract; ¶ 0015, 0024, 0036, 0051, 0057, 0073, 0075, 0082, 0085, 0100-0104; Figs. 1, 4; Claims 1, 8-9; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; this ACAI system is capable of interacting with humans by mimicking the conversations observed in the conversation logs; Sentence-level Auto-Intent annotation enables the ACAI System to respond to longer messages, typically found in emails; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model; as new flows are discovered within new historical conversation logs, the Generative DNN discovers more auto-topics and auto-subtopics thereby increasing the coverage with time and eventually leading to convergence; re-training of the ACAI System may occur to improve performance of the DM model and NLG model by discovering new conversation flows within auto-topics and auto-subtopics, and/or to improve the performance of the NLU model by discovering new paraphrases for existing intents and response templates). Kelkar does not explicitly disclose but Fields does disclose wherein the language model is a large language model (LLM) (¶ 0055-0057; the ML chatbot may include and/or derive functionality from a Large Language Model (LLM); the ML chatbot may be trained using large training sets of text which may provide sophisticated capability for natural-language skills).
The rationale to combine Kelkar and Ritchey remains the same as for Claim 1. It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the AI response generation and retraining techniques of Fields with the AI response system of Kelkar and Ritchey because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Fields are applicable to the base device (Kelkar and Ritchey), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claims 9-10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kelkar in view of Ritchey and Luzhnica et al (US 11516158) (hereafter, “Luzhnica”).
Regarding Claim 9, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar does not explicitly disclose but Luzhnica does disclose wherein the generated content comprises a summary of at least a subset of the plurality of content (Column 7, lines 3-35; Column 70, lines 11-32; generating a summary of the CS based on the one or more likely relevant portions of the content; in aspects systems of the invention comprise ≥2 types of neural networks, each type of neural network performing a particular function (e.g., in the case of EPDNN(s) prediction semantic element probability distributions based on prompts and training data inputs and in the case of CANN(s) analyzing the content of system-generated messages to generate summaries thereof, summaries of elements thereof, or collections of semantic elements therefrom, etc.). Kelkar additionally discloses wherein the content is the content items (Abstract; ¶ 0003, 0024, 0079, 0082, 0109; Figs. 1-3, 5; Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs; the present invention allows an entity to create an autonomous Conversational AI system (ACAI) by letting a software system inspect past conversations available in email exchanges or chatbots or voice call logs; this ACAI system is capable of interacting with humans by mimicking the conversations observed in the conversation logs; Fig. 5 shows a sample PII-removed conversation between a User and a Human Agent found in the conversation logs; backend systems include but are not limited to databases, software, etc. and the system can read from or write to backend systems).
The rationale to combine Kelkar and Ritchey remains the same as for Claim 1. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the AI generation techniques of Luzhnica with the AI response system of Kelkar and Ritchey because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Luzhnica are applicable to the base device (Kelkar and Ritchey), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 10, Kelkar in view of Ritchey and Luzhnica discloses the limitations of Claim 9. Kelkar does not explicitly disclose but Luzhnica does disclose wherein the summary is displayed to the user in the form of a dashboard or other summary display (Column 7, lines 3-35; Column 70, lines 11-32; Column 101, lines 27-56; Column 130, lines 45-65; Fig. 19; generating a summary of the CS based on the one or more likely relevant portions of the content; in aspects systems of the invention comprise ≥2 types of neural networks, each type of neural network performing a particular function (e.g., in the case of EPDNN(s) prediction semantic element probability distributions based on prompts and training data inputs and in the case of CANN(s) analyzing the content of system-generated messages to generate summaries thereof, summaries of elements thereof, or collections of semantic elements therefrom, etc.; systems can facilitate human review of system-generated messages to perform some or all of such techniques, e.g., a system can automatically send a number of system-generated messages to a system administrator/trainer or can promote an analysis by a user, or both; users can be provided with the capability to edit prompt data, such as instructional prompt data (and optionally also situational prompt data where a failure to consider/utilize situational prompt data is detected or suspected), e.g., the system can deliver system-generated messages to such human reviewers with the instruction to summarize statements contained therein; the system may suggest statements based on an analysis of messages (e.g., as performed by a CANN) for the user/reviewer to edit, select, etc.; the system may suggest changes to the prompts based on such analysis or based on input from the user; generate draft messages, resulting in user selection of messages after review, and possible editing/rating, etc.).
The rationale to combine Kelkar and Ritchey remains the same as for Claim 1. One of ordinary skill in the art would further have been motivated to include the AI output review and editing techniques of Luzhnica with the AI response system of Kelkar and Ritchey to better train the model(s) of the system such that better performance/outputs might be achieved (see at least Column 7, lines 3-35; Column 70, lines 11-32; Column 101, lines 27-56; and Column 130, lines 45-65 of Luzhnica).
Regarding Claim 12, Kelkar in view of Ritchey discloses the limitations of Claim 11. Kelkar does not explicitly disclose but Luzhnica does disclose wherein the generated content is displayed to the user prior to being sent to a party, via an interactive user interface that enables the user to modify the generated content prior to its being sent to the party (Column 101, lines 27-56; Column 113, lines 17-44; Column 130, lines 45-65; Figs. 1, 19; systems can facilitate human review of system-generated messages to perform some or all of such techniques, e.g., a system can automatically send a number of system-generated messages to a system administrator/trainer or can promote an analysis by a user, or both; users can be provided with the capability to edit prompt data, such as instructional prompt data (and optionally also situational prompt data where a failure to consider/utilize situational prompt data is detected or suspected), e.g., the system can deliver system-generated messages to such human reviewers with the instruction to summarize statements contained therein; the system may suggest statements based on an analysis of messages (e.g., as performed by a CANN) for the user/reviewer to edit, select, etc.; the system may suggest changes to the prompts based on such analysis or based on input from the user; the messages are presented to user via the interface, wherein the user can rank/evaluate, edit, or transmit the messages; once the user determines such messages are ready for sending, they are transmitted to audience members; generate draft messages, resulting in user selection of messages after review, and possible editing/rating, etc.). Kelkar additionally discloses wherein the party is the requesting party (0079, 0083-0085, 0093-0094; Fig. 1; when the ACAI System receives the incoming messages from users, it is able to predict the sentence-level auto-intent for every message; a Generative DNN model that generates the response using the user utterance, conversation context variables, result of the NLU model and the result of the DM model).
The rationale to combine remains the same as for Claim 10.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kelkar in view of Ritchey and Xiong (PGPub 20190138651) (hereafter, “Xiong”).
Regarding Claim 17, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar does not explicitly disclose but Xiong does disclose wherein the processor is further configured to identify the user as an expert with respect to the specific subject (Abstract; ¶ 0001, 0057, 0061, 0091; this disclosure relates generally to online systems, and more specifically to predicting a level of knowledge that a user of an online system has about a topic associated with a set of content items maintained in the online system; the weight determination module determines a weight of a connection to be established between a user of the online system 140 and a topic associated with one or more content items maintained in the online system; a value of 1 indicates that the user is an expert in the topic).
The rationale to combine Kelkar and Ritchey remains the same as for Claim 1. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the AI-based decision making techniques of Xiong with the AI response system of Kelkar and Ritchey because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Xiong are applicable to the base device (Kelkar and Ritchey), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kelkar in view of Ritchey, Xiong, and Tong et al (PGPub 20240419950, claiming benefit of Provisional 63508857) (hereafter, “Tong”).
Regarding Claim 18, Kelkar in view of Ritchey discloses the limitations of Claim 1. Kelkar does not explicitly disclose but Tong does disclose wherein the processor is configured to authenticate a determination at least in part by sending a query to each of a plurality of AI models, each configured to use generative artificial intelligence to generate a response (¶ 0073; AI platform performs truth checking of a machine learning output by means of dynamic collaborative consensus, in which a group of LLMs (e.g., ML agents utilizing respective LLMs) deliberate on a given question and answer set to determine the consistency and accuracy of the output; an output may be determined to be consistent and/or accurate if consensus is reached). Kelkar does not explicitly disclose but Xiong does disclose wherein the determination uses the plurality of content items to identify the user as an expert with respect to the specific subject (Abstract; ¶ 0001, 0057, 0061, 0091; this disclosure relates generally to online systems, and more specifically to predicting a level of knowledge that a user of an online system has about a topic associated with a set of content items maintained in the online system; the weight determination module determines a weight of a connection to be established between a user of the online system and a topic associated with one or more content items maintained in the online system; a value of 1 indicates that the user is an expert in the topic). Kelkar does not explicitly disclose but Ritchey does disclose wherein the plurality of AI models are digital twins, each configured to use generative artificial intelligence to generate a response on behalf of a respective user (Abstract; ¶ 0002, 0005, 0046, 0085; Figs. 4, 11; an enterprise system and method for maintaining and transitioning humans to a supplementary adaptable sentient self-reliant entity is presented, said system including at least one entity with an artificial neural network to at least one transform and maintain; in one objective embodiment the recipient biological system can operate as a personal assistant; in another objective embodiment the user to continue as an emulation of the parent user after his or her natural biological death; the enterprise system to produce a family of compatible recurrent capable biological, bio-mechatronic, and mechatronic systems that emulate at least one specific person or derivation of a person).
Kelkar does not explicitly disclose but Tong does disclose receive from each digital twin a corresponding response (¶ 0073; AI platform performs truth checking of a machine learning output by means of dynamic collaborative consensus, in which a group of LLMs (e.g., ML agents utilizing respective LLMs) deliberate on a given question and answer set to determine the consistency and accuracy of the output; an output may be determined to be consistent and/or accurate if consensus is reached).
Kelkar does not explicitly disclose but Xiong does disclose use a large language model to determine based at least in part on the responses that the determination is accurate (¶ 0073; AI platform performs truth checking of a machine learning output by means of dynamic collaborative consensus, in which a group of LLMs (e.g., ML agents utilizing respective LLMs) deliberate on a given question and answer set to determine the consistency and accuracy of the output; an output may be determined to be consistent and/or accurate if consensus is reached). Kelkar does not explicitly disclose but Tong does disclose wherein the determination is determining the user as an expert with respect to the specific subject (Abstract; ¶ 0001, 0057, 0061, 0091; this disclosure relates generally to online systems, and more specifically to predicting a level of knowledge that a user of an online system has about a topic associated with a set of content items maintained in the online system; the weight determination module determines a weight of a connection to be established between a user of the online system 140 and a topic associated with one or more content items maintained in the online system; a value of 1 indicates that the user is an expert in the topic).
The rationale to combine Kelkar, Ritchey, and Xiong remains the same as for Claim 17. One of ordinary skill in the art would further have been motivated to include the AI consensus techniques of Tong with the AI response system of Kelkar and Ritchey to mitigate hallucination issues sometimes associated with LLMs (see at least Paragraph 0073 of Tong).
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20190258747 – “Interactive Digital Twin,” Milev, disclosing a system for building interactive digital twins from assets of one of a variety of respective fields (e.g., transportation, healthcare), said digital twins capable of generating content regarding a subject in response to a query
US 12050712 – “Enterprise Knowledge Assistant with Permissions-aware Automated Responses,” Zhou et al, disclosing a system for providing an artificial intelligence-based real-time enterprise knowledge assistant that automatically responds to user comments and questions via a graphical user interface
Pascual et al, A Systematic Review on Human Modeling: Digging into Human Digital Twin Implementations, arXiv 2302.03593 (published 2/04/2023)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628