DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The following office action is a Final Office Action in response to the communications received on 04/02/2026.
Claims 1, 10 and 17-20 have been amended; and therefore, claims 1-20 are currently pending in this application.
Claim Rejections - 35 USC § 101
3. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 10 and 17 as representative claims, the following claimed limitations recite an abstract idea:
identify trouble words associated with a reader, wherein the trouble words comprise words classified as challenging with respect to a reading ability of the reader;
select one or more trouble words of the trouble words for a reading assignment;
[create] a reading passage tailored to the reading ability of the reader using the one or more trouble words;
include the one or more trouble words in the reading passage;
[present] the reading passage [with] the one or more trouble words highlighted in the reading passage.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, and/or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). For instance, the claims correspond to managing personal behavior, such as teaching, wherein a reading passage that is tailored to a user’s skill is created based on identifying one or more trouble words that are difficult for the user; wherein the reading passage is created by including the trouble words in the reading passage; and furthermore, the one or more trouble words are highlighted/emphasized when presenting the reading passage to the user, etc.
Similarly, given the limitations that recite the process of: identifying trouble words associated with a reader, wherein the trouble words comprise words classified as challenging with respect to a reading ability of the reader; selecting one or more trouble words for a reading assignment, etc., the claims also overlap with a mental process; for example, an evaluation, an observation and/or a judgment process, etc.
Step 2A [Wingdings font/0xE0] Prong two
The claims recite additional element(s), wherein a computer that comprises computer components (e.g., a processor, a non-transitory storage media, a display interface, etc.), including a remote computing device, are utilized—as a tool—to facilitate the recited functions/steps regarding: identifying/classifying words that are troublesome for a user (“identifying trouble words associated with a reader, wherein the trouble words comprise words classified as challenging with respect to a reading ability of the reader”); collecting user input from a remote device (“receive, from a client application executing on a remote device, user input comprising a selection of one or more trouble words of the trouble words for a reading assignment”); prompting/instructing a service provider to generate text based on the identified words (“generating a prompt with which to elicit a response from a foundation model service that includes a reading passage tailored to the reading ability of the reader, wherein the prompt includes instructions for generating the reading passage using the one or more trouble words . . . submit the prompt to the foundation model service via an application programing interface (API); receive, from the foundation model service via the API, the output comprising the reading passage”); analyzing the output from the service provider and displaying the generated reading passage based on the analysis (“parse the output from the foundation model service to identify the one or more trouble words tagged in the reading passage based on the specified format; and enabling display of the reading passage in a user interface . . . the display of the reading passage comprises the one or more trouble words highlighted in the reading passage”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
It is further worth to note that the utilization of the conventional computer/network technology to facilitate the process of generating and presenting one or more tailored content items to a user (e.g., textual, pictorial and/or audio/video material), based on the analysis of one or more parameters collected regarding the user and/or the material,
etc., is directed to a well-understood, routine or conventional activity in the art (e.g., see US 2016/0134694; US 2013/0145240; US 2010/0146398; US 2002/0156632, etc.).
Of course, the same is true regarding the process of presenting one or more content items—such as a reading text—according to one or more attributes pertinent to the user; such as, highlighting the difficult words/phrases, etc. (e.g. US 2013/0196292; US 2006/0110711, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-9, 11-16 and 18-20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
► Applicant’s arguments directed to section §101 have been fully considered (the arguments filed on 04/02/2026); however, the arguments are not persuasive at least for the following reasons:
Firstly, regarding the finding presented under Prong One, Applicant is asserting that “Firstly, claim 1, as amended, recites specific technical operations that cannot practically be performed in the human mind. Claim 1 recites ‘generate a prompt with which to elicit a response from a foundation model service . . . identifies the tagged trouble words for highlighting.’ This limitation requires generating a specifically structured prompt that instructs a foundation model service to produce output with tagged trouble words in a format suitable for automated parsing and highlighting. A human mind cannot practically generate prompts for submission to a foundation model service, nor can a human mind instruct a foundation model to tag words in a specified format for subsequent machine parsing . . . Secondly, claim 1 recites ‘submit the prompt to the foundation model service via an application programming interface (API)’ . . . API-based communication between an application service and a foundation model service is fundamentally a computer-based operation that cannot be performed as a mental process . . . Third, claim 1 recites ‘parse the output from the foundation model service to identify the one or more trouble words . . . Parsing structured output from a foundation model service to identify tagged elements based on a specified format requires computer processing . . . This parsing operation based on a specified format is not something a human could practically perform mentally, particularly when processing output from a foundation model service” (emphasis added). Applicant has also identified a section from the specification (i.e., [0054]) in order to support the assertions above.
However, Applicant’s arguments suffer from a fundamental flaw. In particular, Applicant appears to fail to properly apply the inquiry under Prong One of Step 2A. This is because Applicant is entirely relying on the computer elements, which are part of the additional elements, to challenge the Office’s findings under Prong One of Step 2A. In contrast, the inquiry under Prong One does not require one to consider any of the claimed computer elements (e.g., the use of the foundation model service or server; the use of an application programming interface (API) to establish communications between computers, etc.). Instead, while excluding the claimed computer elements, Prong One of Step 2A requires one to identify only the limitations that recite the judicial exception; such as, an abstract idea; MPEP 2106.07(a), (emphasis added).
For Step 2A Prong One, the rejection should identify the judicial exception by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an exception. For example, if the claim is directed to an abstract idea, the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim and explain why it is an abstract idea.
Thus, when evaluating whether a claim is reciting an abstract idea, such as the group mental processes, one is required to identify only the limitations that can practically be performed in the human mind and/or using a pen and paper. Of course, besides the limitations that recite the abstract idea, the claim may also recite limitations that signify computer elements; however, this does not necessarily make the claim immune from the eligibility analysis set forth under Prong One. It is important to note the inquiry regarding mental processes; see MPEP 2106.04(a)(2)(III)(A) (emphasis added),
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699 (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that ‘‘could not, as a practical matter, be performed entirely in a human’s mind’’).
Accordingly, when a claim does not contain limitations that can practically be performed in the human mind, the claim does not recite a mental process. However, if the claim does contain limitations that can practically be performed in the human mind, the claim does recite a mental process. For instance, considering claim 1 as an example, a human—such as a teacher—can perform the following limitations mentally and/or using a pen and paper:
the teacher can identify (e.g., based on records related to the reader, and/or the grade level of the reader, etc.), trouble words associated with the reader (e.g., a student); wherein the trouble words comprise words classified as challenging with respect to a reading ability of the reader (e.g., words listed on a page, etc.);
the teacher also selects one or more of the trouble words above in order to prepare a reading assignment;
the teacher subsequently drafts, using a pen and paper, a reading passage tailored to the reader; and the reading passage includes one or more of the selected trouble words above;
the teacher further emphasizes one or more of the trouble words in the reading passage (e.g., by highlighting each trouble word with a yellow highlighter, or underlining each trouble word, etc.);
the teacher finally presents the page above, which contains the reading passage with one or more emphasized words, to the reader to read.
The observation above confirms that the current claims recite an abstract idea; such as, a mental process, since it contains limitations that can practically be performed in the human mind (and/or using a pen and paper). Consequently, Applicant’s arguments are not persuasive.
Applicant has also attempted to compare the current claims with Example 39 of the USPTO SME examples. Applicant asserts, “the claims are analogous to USPTO Subject Matter Eligibility Example 39 (Method for Training a Neural Network for Facial Detection), which was found to be patent eligible because the claims recited specific technical steps that could not be performed mentally . . . Like Example 39, claim 1 recites a specific technical process involving interactions with an AI model (the foundation model service) through defined technical mechanisms (API submission, specified output formats, parsing operations) that are inherently technological in nature” (emphasis added).
However, unlike Applicant’s assertion, none of the current claims is analogues to Example 39. In particular, Example 39 does not even recite any judicial exception (i.e., no abstract idea is recited). In contrast, the current claims do recite an abstract idea. In fact, the exemplary analysis discussed above already demonstrates the limitations that recite a mental process (e.g., see the example above, which shows how a teacher can practically perform the identified limitations mentally and/or using a pen and paper). Consequently, Applicant’s arguments are not persuasive.
Note also that Applicant’s attempt to challenge the finding under Prong One of Step 2A, while repetitively emphasizing the computer elements, is also not persuasive. This is again because none of the clamed computer elements is considered as part of the abstract idea. Instead, the computer elements are excluded from consideration during the inquiry under Prong One. Consequently, none of Applicant’s assertions directed to the computer elements, including the alleged “specific technical process”, which supposedly involves interactions with an AI model via the allegedly “defined technical mechanisms”, etc., has anything to do with challenging the abstract idea identified under Prong One of Step 2A. Consequently, Applicant’s arguments are once again not persuasive.
Secondly, while attempting to challenge the Office’s findings under Prong Two of Step 2A, Applicant is asserting that “the claims integrate any such abstract idea into a practical application by providing specific technical improvements to the functioning of reading instruction applications and their interaction with foundation model services. As described in the specification, ‘[t]echnical effects may be appreciated from the technology disclosed herein include a streamlined process and interface . . . to create customized reading passages on demand.’ Specification, paragraph [0037]. Claim 1, as amended, recites a specific technical solution that improves the functioning of the reading instruction application . . . reducing data traffic and processing overhead. As described in the specification, ‘[p]rompts tailored according to the disclosed technology reduce the amount of data traffic between the application service and the foundation model service . . . reducing the number of interactions with the LLM to generate a desired result.’ Specification, paragraph [0038] . . . By instructing the foundation model service to tag trouble words in a specified format within the generated output, the application eliminates the need for additional round-trip communications or post-processing to identify and locate trouble words within the passage” (emphasis added).
However, neither the sections identified from the specification nor Applicant’s assertion demonstrates any technological improvement over the relevant existing technology; namely, the existing computer/network technology. For instance, unlike Applicant’s assertion, the claimed—and the disclosed—system/method does not appear to achieve any reduction in “data traffic and processing overhead”, which supposedly makes the claimed (or the disclosed) system an advance over the existing computer/network technology. This is because the reduction in “data traffic and processing overhead” is referring to the number and/or duration of interaction that a user (e.g., a teacher) is making with the model (see [0038], emphasis added).
“. . . the disclosed technology streamlines the interaction between the user (e.g., a teacher) and the application service by generating prompts which keep the LLM on task and reduce the incidence of erroneous, inappropriate, or off-target replies. The disclosed technology also promotes more rapid convergence, that is, reducing the number of interactions with the LLM to generate a desired result”
Accordingly, the above has nothing to do with improving data traffic and/or processing overhead. It is merely indicating an interaction that the user is making with the application. In this regard, regardless of whether the application is providing proper prompts to the user (e.g., reducing prompts that are erroneous, inappropriate or off-target), the user may simply continue to interact with the application or abruptly end the interaction depending on the user’s interest or preference. Accordingly, such interaction session and/or interval, which the user extends or shortens based on his/her preference, has nothing to do with technological improvement. In contrast, a technological improvement that improves data traffic does not rely on the user’s preference. One exemplary case is Amdocs—i.e., Amdocs (Israel) Ltd. v. Openet Telecom, Inc. (Fed.Cir. 2016). In particular, Amdocs implements a particular distributed network architecture that reduces network congestion while generating data records; and this solves a technological problem (e.g., massive record flows which previously required massive databases). In contrast, while mistaking the interaction duration and/or interval, which the user extends or shortens based on his/her preference, for a technological improvement, Applicant is attempting to portray an alleged technological improvement in terms of data traffic or overhead processing. Consequently, Applicant’s arguments are not persuasive. This is again because neither the current claims nor the original disclosure is providing a technological improvement. Instead, while utilizing the existing computer/network technology as a tool, the currently claimed (and the originally disclosed) method/system is facilitating the presentation of information to the user; namely, information in the form of a reading passage that contains one or more highlighted words, wherein the highlighted words represent words that are considered to be challenging with respect to the user’s reading ability, etc. (e.g., see claim 1).
Similarly, it is part of the existing computer/network technology (e.g., cloud computing, etc.) to manage or reduce processing overhead that a computing device is facing. In particular, as part of the existing computer/network technology, the computing device may offload part of its computation task or workload to a server, so that the server performs the computation task and delivers the generated result(s) back to the computing device. Although a reference is not necessarily required to demonstrate the above fact, one of the references—namely, Chan (US 2014/0136662)—cited in one of the past office actions (e.g., office action dated 10/24/2024) already confirms the above fact. In particular, Chan’s system executes an implementation that reduces the workload of the computing device. For instance, according to Chan’s implementation, one or more background components of a computer application(s) running on the computing device are migrated to the cloud server ([0023] to [0025]); so that the cloud server performs the background component(s); and such implementation helps the computing device to efficiently operate since it frees up local resources of the computing device to handle local applications and operating system (see [0030] to [0032]). Accordingly, the above is yet another exemplary scenario that demonstrates an implementation that reduces processing overhead of a computing device. In particular, the above has nothing to do with the user’s preference. In contrast, none of the current claims appear to implement even the above existing technology. Consequently, Applicant’s alleged technological improvement is not persuasive. In particular, despite relying merely on the user’s preference to continuously or intermittently interact with the application, Applicant is attempting to demonstrate an alleged technological improvement (e.g., reducing data traffic, reducing processing overhead, and/or reducing the number of interactions with LLM, etc.). Thus, Applicant’s arguments are once again not persuasive.
Applicant further asserts, “[t]he second technical improvement relates to optimizing foundation model performance . . . ‘[i]n addition, the disclosed technology focuses the generative activity of the foundation model to improve the performance of the foundation model without overwhelming the foundation model (e.g., by exceeding a token limit). For example, the disclosed technology balances prompt size (e.g., the number of tokens in the prompt which must be processed by the foundation model) with providing sufficient information to generate a useful response. Other technical benefits accruing from streamlined interaction, more rapid convergence, and optimized prompt sizing include reduced data traffic, faster performance by the foundation model, reduced latency, and concomitant improvements to productivity costs and to the end-user experience.’ Specification, paragraph [0039] . . . the foundation model's generative activity to produce output that is immediately usable by the application service without requiring additional processing cycles . . . The third technical improvement relates to the integrated technical architecture. Claim 1 recites submitting the prompt ‘via an application programming interface (API)’ . . . the prompt is submitted via an API supported by the foundation model service. The application service receives output including the custom reading passage from the foundation model service based on the prompt.’ Specification, paragraph [0055]. This specific technical architecture enables efficient machine-to-machine communication between the application service and the foundation model service” (emphasis added).
However, Applicant’s alleged optimization appears to reflect the technological limitations that the claimed—and disclosed—foundation model is facing. In particular, it is signifying that the foundation model is incapable of handling a prompt that exceeds a preset token limit since it overwhelms the model. Nevertheless, such process of limiting the size of a prompt, which a user can provide during interaction, does not signify a technological improvement regardless of whether Applicant (or the specification) labels the above as an optimization. This is again because it is already part of the existing computer/network technology (e.g., chatbots, social medial apps, etc.) to limit the maximum size of a prompt that a user can provide (and/or be provided with) during interaction. Of course, the existing technology utilizes various methodologies to implement the above requirement. For instance, besides implementing a token limit, some existing chatbots are designed in such a way that the size of the text window is limited to a predetermined maximum number of characters; and therefore, a user cannot provide number of characters that exceed the preterminal size limit, etc. Thus, Applicant’s alleged optimization, which supposedly “improve[s] the performance of the foundation model without overwhelming the foundation model”, is in fact one of the features of the exiting computer/network technology. Consequently, Applicant’s attempt to portray an alleged technological improvement, while labeling the feature(s) of the existing technology as a technological improvement, is not persuasive. Note also that the alleged “concomitant improvements to productivity costs and to the end-user experience” also appears to be a conclusory assertion since no rationale and/or evidence is presented to demonstrate whether the above relates to a particular technology, much less a technological improvement.
Of course, the fact above further invalidates Applicant’s alleged “[o]ther technical benefits”, namely the alleged “more rapid convergence, and optimized prompt sizing include reduced data traffic, faster performance by the foundation model, reduced latency, and concomitant improvements to productivity costs and to the end-user experience”, etc. In particular, except for simply declaring a list of alleged technological improvements, the specification does not even articulate whether the “foundation model” implements any advanced feature in order to achieve the alleged technological improvements. If anything, it appears to be a preemptive remark prepared in anticipation of an imminent eligibility challenge during an examination. This is because the specification does not even contemplate—much less positively implement—any advanced training methodology to train the foundation model, which is assumed to be an AI model. Instead, it is providing a generic remark regarding the use of a generative AI to generate relevant reading passages to students (see [0020], emphasis added),
“ The application service is directed to automatically generating highly targeted reading passages, such as for students in a class, using generative artificial intelligence (Al). The passages may be generated to allow practicing of words that student(s) have been known to have trouble with, to practice certain phonics rules, etc.
In fact, the specification appears to positively admit that the disclosed technology as a whole, which includes the “foundation model” that Applicant is promoting, relies on already developed technology (e.g., see [0030], [0032] to [0035] and [0040], emphasis added),
“ Foundation models of implementations of the technology disclosed herein include large-scale generative artificial intelligence (Al) models trained on massive quantities of diverse, unlabeled data using self-supervised, semi-supervised, or unsupervised learning techniques. Foundation models may be based on a number of different architectures, such as generative adversarial networks (GANs), variational auto-encoders (VAEs), and transformer models, including multimodal transformer models . . . Foundation models include BERT (Bidirectional Encoder Representations from Transformers) and ResNet (Residual Neural Network) . . .”
“ Multimodal models include visual-language foundation models, such as CLIP (Contrastive Language-Image Pre-training), ALIGN (A Large-scale ImaGe and Noisy-text embedding), and ViLBERT (Visual-and-Language BERT), for computer vision tasks. Visual multimodal or foundation models also include DALL-E, DALL-E 2, Flamingo, Florence, and NOOR . . .”
“ Large language models (LLMs) are a unimodal type of foundation model which processes and generates natural language text. These models are trained on massive amounts of text data and learn to generate coherent and contextually relevant responses . . . Types of LLMs include language generation models, language understanding models, and transformer models”
“ Transformer models, including transformer-type foundation models and transformer-type LLMs, are a class of deep learning models used in natural language processing (NLP) . . . GPT (Generative Pre-trained Transformer) models, ERNIE (Enhanced Representation through kNowledge IntEgration) models, T5 (Text-to-Text Transfer Transformer), and XLNet models are types of transformer models . . .”
“ In some implementations, the technology disclosed herein incorporates a foundation model service, such as a multimodal model service hosting a multimodal model, to teach a variety of subjects beyond reading instruction, such as subjects in the social sciences (e.g., history, geography), scientific subjects (e.g., biology, chemistry, astronomy), math subjects (e.g., geometry, statistics, game theory), or subjects in the visual arts, (e.g., fine art appreciation, photography, art history) . . .”
“ Figure 1 illustrates operational environment 100 for an application service with an LLM integration for generating customized or personalized reading passages in an Implementation . . . Foundation model service 130 hosts a generative pretrained transformer (GPT) computing architecture such as GPT-3®, GPT-3.5, ChatGPT®, or GPT-4”
The excerpts above demonstrate that the disclosed system/method is relying on technologies that are already developed; and thus, there is no evidence to show whether the disclosed implementation is attempting to improve any of the technologies that are already available. Consequently, the various alleged benefits, which Applicant and/or the specification are implying (e.g., balancing of the prompt size to prevent overwhelming of the model; achieving a more rapid convergence, etc.), are indeed the direct results of the existing technology.
Of course, Applicant’s attempt to label application programming interface (AP) as the alleged “specific technical architecture”, which supposedly provides the “third technical improvement”, is also not valid. This is because API is one of the basic features of the existing computer/network technology. Although the concept of API dates back to the dawn of computing, various APIs were implemented as web-based tools back in the year 2000 or 2004 (e.g., companies like Salesforce, eBay, Amazon implemented API as web-based tools). In contrast, while simply disregarding such implementation of API, Applicant is attempting to portray API as the alleged “specific technical architecture”, which provides the so-called “third technical improvement” that the claimed (or the disclosed) system/method is implementing. Consequently, Applicant’s arguments are once again not persuasive.
Applicant appears to declare yet another technological improvement, “[t]he fourth technical improvement relates to automated display generation. Claim 1 recites ‘parse the output from the foundation model service to identify the one or more trouble words tagged in the reading passage based on the specified format’ and ‘enable display of the reading passage in a user interface to the client application, wherein the display of the reading passage comprises the one or more trouble words highlighted in the reading passage.’ As described in the specification, ‘[u]pon receiving the output, in an implementation, the application service parses the output to extract the custom reading passage and enables display of the reading the passage in the user interface of the client application.’ Specification, paragraph [0056]. By receiving pre-tagged output in a specified format, the application can automatically generate the enhanced display with highlighted trouble words without requiring additional natural language processing or text analysis operations” (emphasis added).
Accordingly, Applicant appears to consider yet another feature of the existing computer technology—namely the process of highlighting one or more words of a text, as the alleged technological improvement. In contrast, it is already part of the existing computer/network technology to parse one or more words of a text (e.g., a story, etc.) and highlight one or more of the words—such as, words that satisfy a specified parameter. Of course, a reference is not necessarily required to demonstrate the above fact. Nevertheless, at least one of the references, e.g., Julia (US 2006/0110711), which is cited as part of the analysis under Step 2B, already confirms the fact above. For instance, Julia (a publication available to the public for more than a decade prior to Applicant’s disclosed method/system) teaches such a system that highlights one or more words that are troubling for the user. In fact, Julia also implements a more robust technique to accurately determine the words that are troubling for the user. In particular, as the user is reading a text (e.g., a story), the system captures the user’s speech and analyzes the speech via voice recognition in order to identify one or more words and phonemes that are troubling for the user (see [0093]; [0095]; [0097]). Of course, once the system identifies the troubling words, the system not only presents the user with qualitative and quantitative feedback (see [0098]), but also presents the user with a new reading material that incorporates the identified trouble words; and furthermore, the trouble words are highlighted ([0099]; [0100]). In this regard, given the facts above, PHOSITA readily recognizes that Julia’s system already executes one or more known/existing routines for parsing the text, including tagging the identified troubling words for highlighting, etc.
Note also that Julia’s system is not necessarily limited to a standalone device since it also implements distributive computing, so that one or more of the tasks above are performed over the Internet ([0074]; [0075]; [0146]).
Thus, the evidence above effectively invalidates Applicant’s assertion regarding the alleged technological improvement. In particular, neither Applicant’s assertions nor any of the sections from the specification demonstrates a technological improvement over the existing computer/network technology. In contrast, the analysis presented above confirms that the claimed (and disclosed) system/method is still relying on the existing computer/network technology—merely as a tool—to facilitate an abstract idea; such as, presenting information to the user in the form of a reading passage, wherein one or more words that are troubling to the user are highlighted, etc. (e.g., see above the abstract idea identified under Prong One of Step 2A for detail). Consequently, Applicant’s arguments are not persuasive.
It is also noted, given Applicant’s subjective assumptions above, that the disclosed (or the claimed) system/method allegedly has more than seven technological improvements, as opposed to the typical one or two technological improvements that most patent-eligible cases implement (e.g., see Enfish, McRO, etc.). Accordingly, the observation above is indeed confirming the fact that Applicant is attempting to portray almost each and every feature of the existing computer/network technology as an alleged technological improvement.
Thirdly, Applicant also appears to be attempting to compare the current claims with Example 42 of the USPTO examples. Applicant asserts, “[t]hese technical improvements are analogous to those found . . . [in] Example 42 . . . Similarly, claim 1 recites a specific technical solution—prompting for tagged output in a specified format, API-based communication, and parsing the tagged output—that improves the functioning of the reading instruction application and its interaction with the foundation model service” (emphasis added).
However, none of the current claims is even remotely analogous to Example 42 of the USPTO example. In particular, Example 42 is not directed to such process of generating a document pertinent to a user, based on the evaluation of one or more inputs or attributes collected from the user, regardless of whether the document being generated is a reading passage that highlights the troubling words for the user. Instead, Example 42 is directed e to a conversion process implemented to mitigate document accessibility issues due to the differences in the hardware and/or software configurations of different devices. For instance, a given document, which is generated using a first device that has a first hardware/software configuration, is converted into a standard format; so that the document can be accessed using a second device that has a second different hardware/software configuration. In contrast, Applicant’s claimed—and disclosed—system/method does not even contemplate the process of evaluating compatibility issues due to different hardware and/or software configurations, much less positively implement a mechanism to resolve such compatibility issues. Instead, as repeatedly pointed out above, the claimed (and the disclosed) system/method is directed to the process of generating content pertinent to the user—namely, content in the form of a reading passage, wherein one or more of the words that are troubling for the user are highlighted, etc. (e.g., see claim 1).
So far, Applicant fails to demonstrate whether any of the current claims, when considered as a whole, implements an element—or a combination of elements—that provides a technological improvement. Instead, Applicant is simply emphasizing the features of the existing computer/network technology—such as: the process of tagging words that need to be highlighted, the use of API (see discussion above regarding API), the process of parsing text or document, etc. Thus, Applicants’ attempt to substantiate an alleged technological improvement, despite the absence of a new/advanced claimed (or disclosed) feature(s), is not persuasive.
Regarding Step 2B, Applicant asserts, “[w]hen taken as a whole, claim 1 recites an inventive concept directed to a specific technical implementation for generating customized reading passages with integrated trouble word tagging and highlighting. The combination of elements recited by claim 1 is not conventional. Claim 1 recites: (1) generating a prompt that ‘tasks the foundation model service with generating an output that includes the one or more trouble words tagged in the reading passage in a specified format that identifies the tagged trouble words for highlighting’; (2) submitting this prompt ‘via an application programming interface (API)’ (3) receiving output ‘from the foundation model service via the API’; and (4) ‘pars[ing] the output from the foundation model service to identify the one or more trouble words tagged in the reading passage based on the specified format.’ This ordered combination of technical steps represents a non-conventional arrangement of elements that achieves specific technical benefits” (emphasis added).
However, except for the attempt to summarize the clamed steps, Applicant does not identify the element (if any)—or the combination of elements (if any)—that provides the alleged inventive concept. Note that an inventive concept (if any) is demonstrated when the claimed system/method is implementing a non-generic and non-conventional arrangement of the additional elements. Of course, an inventive concept also relies on a technological improvement (if any) that the claims are implementing (see MPEP 2106.05(a), emphasis added),
While improvements were evaluated in Alice Corp. as relevant to the search for an inventive concept (Step 2B), several decisions of the Federal Circuit have also evaluated this consideration when determining whether a claim was directed to an abstract idea (Step 2A). See, e.g., Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-16, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016); Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017). Thus, an examiner should evaluate whether a claim contains an improvement to the functioning of a computer or to any other technology or technical field at Step 2A Prong Two and Step 2B, as well as when considering whether the claim has such self-evident eligibility that it qualifies for the streamlined analysis.
Thus, the summary that Applicant presented above, which summarizes the steps that the claimed system/method is performing when generating a reading passage to the user, has nothing to do with demonstrating an inventive concept. Consequently, Applicant’s alleged inventive concept is not persuasive.
In addition, when evaluating whether a claim is directed to a well-understood, routine, conventional activity (hereinafter WRCA) in the art, such inquiry is not influenced by the new abstract idea that the claim is reciting. This is because the WRCA test is evaluating the underlying technology, which the claim is implementing to facilitate the new abstract idea. Thus, despite the lack of new or advanced technology, the current claims may overcome the prior art due to the new abstract idea they are reciting. However, this does not necessarily mean the claims are beyond WRCA. This is once again because a claim for a new abstract idea is still an abstract idea. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016). See MPEP 2106.06 (I) (emphasis added),
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) . . . See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a §101 inventive concept is thus distinct from demonstrating §102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103 . . . patentability of the claimed invention under 35 U.S.C.102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C.101.
Thus, Applicant’s attempt to emphasize the lack of art rejection to substantiate an alleged inventive concept is not persuasive. In particular, Applicant is asserting that “the Examiner has also indicated that ‘the prior art does not teach or suggest the current claims.’ Office Action, page 8. This acknowledgment that the prior art does not teach the claimed combination directly contradicts the assertion that the claims are directed to well-understood, routine, or conventional activity. If the specific combination of elements recited by the claims—particularly the prompting for tagged output in a specified format, API-based submission and receipt, and parsing based on the specified format—were truly conventional, the prior art would teach or suggest such a combination” (emphasis added). However, as already pointed out above, the alleged novelty (section §102) or the alleged non-obviousness (section §103) of a claim does not necessarily indicate eligibility.
Applicant also appears to be repeatedly mistaking the features of conventional computer/network technology for an advanced technological feature. For instance, the process of prompting a device to perform a particular task is already part of the existing technology. In particular, as part of the conventional distributive computing, different tasks are allocated to different devices over a network in order to maintain a more efficient computing environment. For instance, a first device (also known as a coordinator or client device) identifies a specific task(s) that a second device is required to perform; and furthermore, the first device prompts the second device to perform the task. Thus, unlike Applicant’s repetitive assumptions, the claimed prompt, which elicits a response from the foundation model service, is merely an instruction—i.e., an instruction for generating a reading passage using trouble words, and tasking the foundation model service to generate an output that includes trouble words tagged in a specified format for highlighting, etc. Thus, the above feature, when considered individually or in any ordered combination with the rest of the claimed features, does not even remotely suggest an inventive concept.
Of course, the same is true regarding the implementation of API, which is an old and well-known software tool—or bridge—that allows devices and/or software modules to interact with one another smoothly. For instance, as part of the conventional computer/network technology, an API defines rules and protocols (e.g., the HTTP protocol, etc.) that allow two different software modules to exchange data seamlessly even if the two software modules are written in different programming languages (also see the discussion presented above regarding API). Thus, Applicant’s repetitive attempt to portray the above old and well-known feature as a technological improvement is indeed unfounded.
Similarly, the process of parsing or analyzing words in a text/document, including identifying one or more attributes based on such analysis, etc., is also part of the existing computer/network technology. In fact, one of the references discussed above already confirms the above fact. Thus, Applicant’s attempt to substantiate an alleged inventive concept, despite relying on such feature of the conventional technology, is also unfounded.
The observation above demonstrates that none of the claimed (or disclosed) features that Applicant is emphasizing is directed to an inventive concept, regardless of whether the features are considered individually or in any ordered combination.
Applicant further asserts, “the technical benefits achieved by the claimed combination confirm that the claims amount to significantly more. As described in the specification, ‘[p]rompts tailored according to the disclosed technology reduce the amount of data traffic between the application service and the foundation model service for generating useful content for a reader.’ Specification, paragraph [0038]. Additionally, ‘[o]ther technical benefits accruing from streamlined interaction, more rapid convergence, and optimized prompt sizing include reduced data traffic, faster performance by the foundation model, reduced latency, and concomitant improvements to productivity costs and to the end-user experience.’ Specification, paragraph [0039]. These concrete technical improvements demonstrate that the claimed combination provides significantly more than an abstract idea” (emphasis added).
However, the above is merely a repetition of the same argument; namely, Applicant’s alleged “first technical improvement” and “second technical improvement”, which are already addressed above. In particular, the discussion presented above already demonstrates that none of Applicant’s assertions demonstrates a technological improvement over the relevant existing technology. In fact, the analysis presented above already provides sufficient evidence, including sections cited directly from the specification, which confirm that Applicant’s claimed—and disclosed—system/method is relying on the features of already developed technology to facilitate the process of generating a reading material to the user. In particular, neither the current claims nor the original disclosure as a whole provides a new technology or an improvement to an already existing technology (see the response presented above for detail). Of course, given a lack of technological improvement, including the generic and conventional arrangement of the additional elements, none of the current claims is implementing an element—or a combination of elements—directed to an inventive concept. Therefore, Applicant’s arguments above are still not persuasive.
Thus, at least for the reasons discussed above, the Office concludes that none of the current claims—when considered as a whole—amounts to “significantly more” than an abstract idea.
Prior Art
● Considering each of claims 1, 10 and 17 as a whole (including the respective dependent claims), the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office-action dated 04/24/2025).
Conclusion
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 filled 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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