DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 03/17/2026 has been entered.
3. Currently claims 1-3 have been amended; claims 4-19 have been canceled; and new claims—claims 20-31—have been added. Therefore, claims 1-3 and 20-31 are currently pending in this application.
Claim Rejections - 35 USC § 101
4. 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 1-3 and 20-31 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 1-3 as representative claims, the following claimed limitations recite an abstract idea:
obtain a first question input by a first user;
obtain a second question input by a second user;
[present] a first answer based on the first question using a model, the first answer comprising a first [option];
[present] a second answer based on the second question using a model, the second answer comprising a second [option];
[obtain] a first selection of the first [option] by the first user;
[obtain] a second selection of the second [option] by the second user;
[collect] first evaluation data comprising the first question associated with the first selection;
[collect] second evaluation data comprising the second question associated with the second selection;
calculate a similarity between piece of first evaluation data and piece of the second evaluation data; and
update parameters of the model by using a score of the similarity, the first evaluation data and the second evaluation data.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to mental processes and/or certain methods of organizing human activity, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). For instance, regarding the group mental processes, a human—such as an instructor—can perform the limitations mentally—and/or using a pen and paper. In particular, the instructor first acquires a respective question from each of one or more users; and subsequently, once analyzing each question using one or more models (e.g., one or more templates/rules that correlate one or more questions with one or more possible answers, etc.), the instructor provides a respective answer to each of the users, wherein each answer comprises the name and/or location of a source (e.g., a document) that provides the user with access to additional information; and accordingly, based on information that the instructor is gathering regarding the corresponding source that each user has accessed, the instructor notes a corresponding evaluation data (e.g., the instructor records: (i) a first evaluation data comprising the first question associated with the first accessed source; (ii) a second evaluation data comprising the first question associated with the first accessed source, etc.); and furthermore, once the instructor has calculated a similarity score representing the similarity between part of the first evaluation data and part of the second evaluation data, the instructor updates—using the score, the first evaluation and the second evaluation—the parameters of one or more of the modes (e.g., adjusting one or more parameters of the template/rule that is being utilized to select a corresponding answer to a given question, etc.); so that the accuracy of one or more subsequent answers, which the instructor acquires from the model(s), would be improved.
The observation above confirms a mental process, i.e., a concept that can be performed in the human mind and/or using a pen and paper; such as, an observation, an evaluation, a judgement, etc.
The limitations identified above also correspond to managing personal behavior; such as teaching, under the group certain methods of organizing human activity. This is because each user is presented with a corresponding answer, based on a respective question that each user is providing; and furthermore, each answer includes the name and/or location of a source/document that provides the user with further information.
(Step 2A) [Wingdings font/0xE0] Prong Two:
The claims recite additional element(s), wherein a computing system that comprises one or more of a server, a processor, information processing device, etc., is utilized to perform the recited steps/functions regarding: collecting information from each of one or more users (e.g., “obtaining a first question input by a first user; obtaining a second question input by a second user”), presenting relevant result based on the collected information (e.g., “outputting a first answer based on the first question using a machine-learned model, the first answer comprising a first link; outputting a second answer based on the second question using a machine-learned model, the second answer comprising a second link”); evaluating or detecting user interaction (e.g., “detecting a first selection of the first link by the first user; detecting a second selection of the second link by the second user”); acquiring evaluation data based on the interaction (“storing first evaluation data comprising the first question associated with the detected first selection; storing second evaluation data comprising the second question associated with the detected second selection”); calculating an attribute—namely, similarity—between parts of the acquired evaluation data (e.g., “calculating a similarity between piece of first evaluation data and piece of the second evaluation data”); updating parameters of an algorithm (e.g., “updating parameters of the machine-learned model by using a score of the similarity, the first evaluation data and the second evaluation data as training data, thereby improving an accuracy of the machine-learning model for outputting subsequent output”), etc.
Accordingly, the additional elements fail to integrate the abstract idea into a patent-eligible practical application since the additional elements are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional elements 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 current claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the current 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. Thus, none of the claims recites an element—or a combination of elements—directed to an inventive concept.
It is also worth noting, per the original disclosure, the current claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, the specification is providing a generic description regarding a conventional computer (see [0080]) that is utilized to facilitate the claimed—and disclosed—process of providing information to the user.
It is further worth to note that the utilization of the conventional computer and/or network technology to facilitate the delivery of information to a user, including the process of providing pertinent question(s) and/or answer(s) to the user based on the analysis of the user’s input(s)/question(s) using one or more machine-learning algorithms/models, etc., is already directed to a well-understood, routine or conventional activity in the art (e.g., see US 2012/0130910; US 2011/0004588; US 2010/0191686; US 2004/0254917, etc.).
It is worth noting that the analysis above already encompasses each of the current dependent claims (i.e., claims 20-31). 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(s) utilized to facilitate the abstract idea.
The observation above confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea. Accordingly, none of the current claims is implementing an element—or a combination of elements—directed to an inventive concept (i.e., none of the claims, when considered as a whole, implements 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 response filed on 03/17/2026). However, the arguments are not persuasive at least for the following reasons:
Firstly, regarding Prong Two of Step 2A, Applicant asserts that “[e]ven if it is assumed claim 1 recites a judicial exception, which Applicant does not concede, Applicant respectfully submits that the claim is patent eligible under prong two of the revised Step 2A of the Alice test because the claim integrates the alleged judicial exception into a practical application . . . Claim 1 recites ‘obtaining a first question input by a first user; obtaining a second question input by a second user; outputting a first answer based on the first question using a machine-learned model . . . thereby improving an accuracy of the machine-learned model for outputting subsequent output’ . . . a system that stores data in a form usable for model parameter updates based on user actions where the user, whom the system finds hard to understand, selected a response . . . Rather than updating from simply collected data, the system computes which data are important for parameter updates by ‘calculating a similarity between piece of first evaluation data and piece of the second evaluation data; and updating parameters of the machine-learned model by using a score of the similarity, the first evaluation data and the second evaluation data as training data, thereby improving an accuracy of the machine-learned model for outputting subsequent output’ . . . accuracy is improved by updating the model's parameters using high-quality training data to improve the accuracy of the next output” (emphasis added).
However, except for the attempt made to describe how the claimed process is operating and/or how it is processing collected information, Applicant still fails to identify
a technological feature (if any)—or a combination of technological features (if any)—that allegedly integrates the abstract idea into a patent-eligible practical application. Note that simply describing how the claimed (or the disclosed) system/method is operating, and/or simply emphasizing the type of inputs it is collecting and/or the algorithm it is utilizing to generate an output(s), etc., does not necessarily demonstrate whether any of the claimed—or the disclosed—system/method is integrating the abstract idea into a patent-eligible practical application. In particular, given the fact that the claimed (and the originally disclosed) system/method is relying on the computer/network technology, an integration (if any) of the abstract idea into a patent-eligible practical application is demonstrated if any of the claims, when considered as a whole, is implementing an element—or a combination of elements—that provides a technological improvement over the existing computer/network technology. In contrast, as evident from the original disclosure, the disclosed system/method as a whole is relying merely on the existing computer/network technology to facilitate an abstract idea. (e.g., see the specification: [0013], [0080], etc.). Accordingly, due to the lack of technological improvement, none of the claims is implementing an element—or a combination of elements—that integrates the abstract idea into a patent-eligible practical application. Consequently, Applicant’s arguments are not persuasive.
It is further noted that Applicant is simply emphasizing the features of the existing computer/network technology in attempt to promote the alleged eligibility of the current claims. For instance, the process of storing data (e.g., textual data and/or a selection gathered from the user, etc.) into a memory/database of a computer system; including
the process of allowing one or more algorithms—such as a machine-learning model—to use the stored data for further data analysis and/or updating its parameters, etc., is already part of the existing computer/network technology. In fact, the training data, which existing machine-learning models (e.g., artificial neural networks, etc.) normally utilize, is already stored in the memory/database in a format usable by such algorithms. Similarly, again as part of the existing computer/network technology, machine-learning models normally update one or more of their parameters over time, based on filtering newly collected or updated information; and such process inherently improves the accuracy of the results that the machine-learning models are generating. In fact, the term “machine-learning” already confirms the learning process that the algorithm is performing in order to improve the accuracy of the results that it is generating. Moreover, depending on the field of use, machine-learning models perform various types of data analysis, including the process of comparing and/or scoring the similarity between two or more items (e.g., two words, two images, etc.) using one or more techniques (e.g. cosine similarity, Euclidean Distance, etc.), so that one or more desired procedures are performed based on the similarity results, etc.
Accordingly, the observations above confirm that none of Applicant’s conclusory assertions (e.g., the alleged storing of data in a form usable for model parameter updates; the alleged similarity calculation that the system is performing in order determine which data is important for parameter updates; the alleged high-quality training data that supposedly improves the accuracy of the model; the alleged “technical improvement” to the mechanism of machine learning technology, which relies on the process of “recording useful data for parameter updates from user behavior, evaluating the importance of that data through similarity calculations, and then updating the model”, etc.), is even remotely relevant to challenge—much less negate—the Office’s findings under Prong Two of Step 2A.
Secondly, regarding Prong Two of Step 2A, Applicant is asserting that “even if the claim were directed to an abstract idea, Applicant submits that the combination of elements in claim 1, as amended, provides significantly more than any allegedly abstract idea. For at least these reasons discussed above, Applicant respectfully submits that claim 1 and similarly claims 2 and 3 are directed to patent eligible . . . Claims 20-31 are patentable at least by virtue of its dependency” (emphasis added).
However, except for the generic conclusion, Applicant does not present a rationale (if any) to substantiate the alleged “combination of elements”, which supposedly render the current clams “significantly more” than an abstract idea. In contrast, an inventive concept (if any), which renders a given claim “significantly more” than an abstract idea, is demonstrated if the claim is directed to the non-conventional and non-generic arrangement of the additional elements. In contrast, due to the fact that the current claimed—and the originally disclosed—system/method is directed merely to the conventional computer/network technology, each of the current claims, considered as a whole, is directed merely to the conventional and generic arrangement of the additional elements. Consequently, none of the current claims, when considered as a whole, implements an inventive concept. Of course, besides the conventional and generic arrangement above, the lack of technological improvement also confirms the lack of inventive concept, see MPEP 2106.05(a).
Thus, at least for the reasons above, the Office concludes that none of the current claims, when considered as a whole, implements an inventive concept that amounts to “significantly more” than an abstract idea.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C.112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C.112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
● Claims 1-3 and 20-31 are rejected under 35 U.S.C.112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Each of current claims 1-3 recites, “outputting a first answer based on the first question using a machine-learned model . . . outputting a second answer based on the second question using a machine-learned model . . . updating parameters of the machine-learned model” (emphasis added).
Thus, it is unclear whether each claim is utilizing two different machine-learned models—i.e., (a) a first machine-learned model to generate the first answer based on the first question, and (b) a second machine-learned model to generate the second answer based on the second question.
Similarly, due to the deficiency above, it is unclear which machine-learned model is being updated based on: the score of the similarity, the first evaluation data and the second evaluation data (as recited per the last paragraph of each of claims 1-3).
Nevertheless, for examination purpose, just one machine-learned model is assumed to be utilized per each of the current claims.
Applicant is further advised to evaluate each of the current claims and make appropriate corrections if additional discrepancies are discovered.
Prior Art
● Considering each of the current claims as a whole, the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office action dated 05/21/2025).
Conclusion
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 on 7:00AM-3:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached on (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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