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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Reference Millen has been withdrawn. Note, the amendments do not contain a scope relative to IVR or customer service, therefore under BRI, no such interpretation is applied.
The rejection under 35 USC 101 has been overcome.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve using an LLM to provide multiple paths for a user to select supported by the specification, and reflected by the claims e.g. in spec: 0015 and 0044 In other words, the claims enable the invention to improve an LLM using rule-constrained path options for a user within multiple framework paths.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-6, 8, 9, 11-14, 16, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 12039431 B1 Deutsch; Noah et al. (hereinafter Deutsch) in view of US 20240012992 A1 Tamm; David M. et al. (hereinafter Tamm).
Re claim 1, Deutsch teaches
1. A method comprising: (fig. 5a)
generating, based at least on an input to a large language model (LLM), a first path summary and a second path summary, the input including user input data… (e.g. 510a and 510b are paths from user input image/text 310 (not limited to images), generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
causing the first path summary and the second path summary to be presented to a user; (presenting paths at the same time for the user fig. 4 & 5a-5b to select from under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
receiving a user interaction indicating a path summary selection; and (user selects a path, generating multiple paths at the same time fig. 4 & 5a-5b for the user to select from under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
performing one or more operations corresponding to the path summary selection to achieve the respective resulting outcome. (results from input and selection thereof… system responds/executes a selection fig. 4 & 5a-5b, generating multiple paths at the same time for the user to select from under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
However, while Deutsch teaches an LLM model for learning continuously based on user selection from simultaneously presented paths constrained by context per se, it fails to teach:
…and at least a portion of a framework model comprising a structured collection of domain-specific rules or constraints; wherein each of the first path summary and the second path summary comprises a sequence of steps to be taken within the framework model to achieve a respective resulting outcome; (Tamm producing summaries of natural language text or descriptors 0045 0072 0078 and 0105 derived and part of multiple paths that include steps in some capacity where a user makes a selection 0059 0063 0079 0083 with fig. 4, utilizing rules in context or for a domain per se 0106 with fig. 6, also utilizing historical or previous user data 0094 0103)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Deutsch to incorporate the above claim limitations as taught by Tamm to allow for a simple substitution of one known element such as contextual information in an LLM model for another, such as a framework model inclusive of the LLM model governed by constraint data, to obtain predication results, wherein context and constraint data can be utilized together to create rules for path creation, which improves the combination to utilize a framework model in-context or per context which by condensing complex, multi-step pathways into concise representations, these summaries enable domain experts, developers, or users to quickly identify critical trends in fields such as but not limited to safety analytics, healthcare, or finance, which also helps identify the most significant paths such that models are less susceptible to irrelevant information, reducing hallucinations and increasing factual precision when presenting and taking in selections.
Re claim 8, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
See fig. 1 of Deutsch containing hardware.
Re claim 16, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
See fig. 1 of Deutsch containing hardware.
Re claims 2, 9, and 17, Deutsch teaches
2. The method of claim 1, wherein the user input data includes text and the first path summary and the second path summary are presented as text. (generating multiple paths as text for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
Re claims 4, 11, and 19, Deutsch teaches
4. The method of claim 1, further comprising: adding at least a portion of the input and the path summary selection to a training dataset; and (the system learns continuously col 4 lines 1-17, generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b)
modifying the LLM based on the training dataset to generate improved path summaries. (adding/modifying/replacing, the system learns continuously col 4 lines 1-17, generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b)
Re claims 5, 13, and 20, Deutsch teaches
5. The method of claim 1, wherein the input further comprises context information related to a user and wherein the context information related to the user is stored on a device of the user. (context as in fig. 4, generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
Re claims 6 and 14, Deutsch teaches
6. The method of claim 5, wherein the context information comprises:
the user input data; (fig. 5a-5b user input e.g. image or text)
the first path summary; and (fig. 5a-5b first option 510a)
the second path summary. (fig. 5a-5b second option 510b)
12. The system of claim 8, wherein the system is comprised in at least one of:
an in-vehicle infotainment system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations; (col 7 lines 52-64)
a system implemented using an edge device;
a system for generating or presenting at least one of virtual reality content, mixed reality content, or augmented reality content; a system implemented using a robot;
a system for performing conversational AI operations;
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs); (col 19 lines 5-15)
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources. (col 18 lines 9-24)
Claims 3, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 12039431 B1 Deutsch; Noah et al. (hereinafter Deutsch) in view of US 20240012992 A1 Tamm; David M. et al. (hereinafter Tamm) and further in view of US 20250069617 A1 Carbune; Victor et al. (hereinafter Carbune).
Re claims 3, 10, and 18, Deutsch teaches
3. The method of claim 1, wherein the user input data includes audio and the first path summary and the second path summary include text, the method further comprising: (generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
applying a first machine learning model to the user input data to obtain a user input text; (selection of a specific model per requirement in context where an image or text are handled but not limited thereof col 4 lines 30-55 & col 6 lines 39-53 and for voice inputs for example col 16 line 50 to col 17 line 20…generating multiple paths for the user to select from fig. 4 & 5a-5b under an LLM model scheme for learning as a machine model fig. 8 & col 20 line 41 to col 21 line 67, such that the paths are presented are expressly shown to the user e.g. text form col 14 line – col 15 line 12 with fig. 4 & 5a-5b, the system therefore learns continuously col 4 lines 1-17)
However, while the combination teaches model selection for the scope of the user such as to produce text outputs or image output edits and presenting information simultaneously, it fails to teach known concepts of text-to-speech models and thus fails to teach:
applying a second machine learning model to the first path summary to obtain a first path summary audio; (Carbune in a chat with user requests for outputs as text or speech 0037 utilizing synthesized speech and a TTS model 0058)
applying the second machine learning model to the second path summary to obtain a second path summary audio; and (Carbune in a chat with user requests for outputs as text or speech 0037 utilizing synthesized speech and a TTS model 0058)
causing the first path summary audio and the second path summary audio to be presented. (Carbune in a chat with user requests for outputs as text or speech 0037 utilizing synthesized speech and a TTS model 0058)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Deutsch in view of Tamm to incorporate the above claim limitations as taught by Carbune to allow for simple substitution of the existing model group in Deutsch with another such as the TTS model in Carbune to allow for a multi-modal AI prompt system such that outputs can include text, images, as well as synthesized speech, thus improving the simultaneous output of information that a user can select from to include voice data.
Claims 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 12039431 B1 Deutsch; Noah et al. (hereinafter Deutsch) in view of US 20240012992 A1 Tamm; David M. et al. (hereinafter Tamm) and further in view of US 20210232644 A1 SHAH; Anal et al. (hereinafter SHAH).
Re claims 7 and 15, while the combination teaches AI chat for prompts, it fails to teach transfer to a human as follows:
7. The method of claim 5, wherein the user interaction indicates a request to interact with a human agent, the method further comprising: providing to the human agent at least a portion of the context information related to the user. (Shah human agent transfer expressly preserving conversation and context thereof 0110)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Deutsch in view of Tamm to incorporate the above claim limitations as taught by SHAH to allow for use of a known technique of transferring to a human agent to improve similar user interface/experience chats or interactions I the same way, wherein having a human transfer option allows an agent to continue the conversation in context to reduce user frustration, handle compliance/liability, and maintain privacy for sensitive data that shouldn’t be part of a model.
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230066100 A1 CHERUKARA; Joseph Joseph et al.
IVR display and user selection, learning models
US 20250119396 A1 Taheri; Shahriar
Framework system
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
Prosecution Insights