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
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5, and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception such as a natural phenomenon, abstract idea, or law of nature, without significantly more and/or a practical application per se, specifically with one or more of:
1) Not integrating a judicial exception into a practical application (see explanation below), and
2) Not reciting elements that would amount to significantly more than the judicial exception (see explanation below).
Accordingly, claims 1-3, 5, and 7 are directed towards patent ineligible subject matter under 35 U.S.C. 101.
The independent claims:
When taking the current claim limitations of the present invention, we see that they are directed to translating a first language to a second language and aligning words with a notation of translation accuracy or quality as well as if further action is needed e.g. delete? Replace? Such steps can be performed by a human or even mentally in low qualities of translation words. Similarly, a practical application or improvement is not demonstrated.
Regarding the claim limitations of claim(s) 1 and 5 as recited:
1. (Currently Amended) An estimation system comprising: a processor: and a memory storing therein a set of instructions which, when executed by the processor, cause the estimation system to:estimated between a source sentence and a translated sentence[[, and]]of the source sentence. the extended word alignment referring to a function to show, per word, whether or not the translated sentence is a correct translation of the source sentence, and also referring to a correctly or incorrectly translated or aligned word pair; and translation quality tags with which the source sentence and the translated sentence are labeled per word; and estimate an edit tag based on the extended word alignment and the translation quality tags.
5. (Currently Amended) A computer-implemented estimation method comprising: receiving, as inputs[[,]]:an extended word alignment estimated between a source sentence and a translated sentence[[, and]] of the source sentence. the extended word alignment referring to a function to show, per word, whether or not the translated sentence is a correct translation of the source sentence. and also referring to a correctly or incorrectly translated or aliened word pair; and translation quality tags, with which the source sentence and the translated sentence are labeled per word; and estimating an edit tag based on the extended word alignment and the translation quality tags.
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?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Regarding the independent claims. No, analogous to Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims are directed to translating a first language to a second language and aligning words with a notation of translation accuracy or quality as well as if further action is needed e.g. delete? Replace? such as lacking a clear improvement of function/technology wherein steps are fundamental in correcting or reviewing translations such as by an instructor for instance.
Further as demonstrated in Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims were to methods for electronically processing paper checks, all of which contained limitations setting forth receiving merchant transaction data from a merchant, crediting a merchant’s account, and receiving and scanning paper checks after the merchant’s account is credited. In part one of the Alice/Mayo test, the Federal Circuit determined that the claims were directed to the abstract idea of crediting the merchant’s account before the paper check is scanned. The court first determined that the recited limitations of “crediting a merchant’s account as early as possible while electronically processing a check” is a “long-standing commercial practice” like in Alice and Bilski. 931 F.3d at 1167, 2019 USPQ2d 281076, at *5 (Fed. Cir. 2019). The Federal Circuit then continued with its analysis under part one of the Alice/Mayo test finding that the claims are not directed to an improvement in the functioning of a computer or an improvement to another technology. In particular, the court determined that the claims “did not improve the technical capture of information from a check to create a digital file or the technical step of electronically crediting a bank account” nor did the claims “improve how a check is scanned.” Id.
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.
The claim which demonstrated improvements to technology and/or function recites: "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.".
The decision recites that “We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”
When considering the limitation decided upon, there are clear improvements to machine learning that are not rudimentary or a long-standing practice, for instance adjusting for optimization and protection of performance, as claimed, are improvements to a machine learning models operations, not simply a general mathematical or generic recitation, but rather an improvement to function.
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, specifically:
“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.”
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.
The second paragraph of MPEP § 2106.05(a), subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality:
xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
No. The claims are directed to translating a first language to a second language and aligning words with a notation of translation accuracy or quality as well as if further action is needed e.g. delete? Replace? Such steps can be performed by a human or even mentally in low qualities of translation words. Similarly, a practical application or improvement is not demonstrated.
• Collecting and comparing known information (Classen)
• Collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial)
• Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group; West View†)
• Comparing data to determine a risk level (Perkin‐Elmer)†
• Comparing new and stored information and using rules to identify options (Smartgene)†
Assistance for Applicant in amending to overcome 101:
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
To help in amending the claims and for analysis purposes, example claims 3 and 4 are listed below from the courts, however such example amendment potentials are not limited to the provided examples and alternative amendments are possible using i-vi from the courts. The example below show differences between eligible claims (court claim 4) and ineligible claims (court claim 3), which thus illustrates significantly more which is tied to hardware that is not generally recited in the art. In this case general changing of font size in claim 3 versus a significant step of conditionally changing font size tied to hardware in claim 4.
See below examples based on MPEP and not on the current claim set, to help amend to overcome 101 rejections:
Regarding independent claim examples:
For instance in the example claims, for example claims 3 and 4 below:
Ineligible
3. A computer‐implemented method of resizing textual information within a window displayed in a graphical user interface, the method comprising:
(not significant) generating first data for describing the area of a first graphical element;
(not significant) generating second data for describing the area of a second graphical element containing textual information;
(not significant) calculating, by the computer, a scaling factor for the textual information which is proportional to the difference between the first data and second data.
The claim recites that the step of calculating a scaling factor is performed by “the computer” (referencing the computer recited in the preamble). Such a limitation gives “life, meaning and vitality” to the preamble and, therefore, the preamble is construed to further limit the claim. (See MPEP 2111.02.)
However, the mere recitation of “computer‐implemented” is akin to adding the words “apply it” in conjunction with the abstract idea. Such a limitation is not enough to qualify as significantly more. With regards to the graphical user interface limitation, the courts have found that simply limiting the use of the abstract idea to a particular technological environment is not significantly more. (See, e.g., Flook.)
Whereas in similar claim 4:
Eligible
4. A computer‐implemented method for dynamically relocating textual information within an underlying window displayed in a graphical user interface, the method comprising:
displaying a first window containing textual information in a first format within a graphical user interface on a computer screen;
displaying a second window within the graphical user interface;
constantly monitoring the boundaries of the first window and the second window to detect an overlap condition where the second window overlaps the first window such that the textual information in the first window is obscured from a user’s view;
determining the textual information would not be completely viewable if relocated to an unobstructed portion of the first window;
calculating a first measure of the area of the first window and a second measure of the area of the unobstructed portion of the first window;
calculating a scaling factor which is proportional to the difference between the first measure and the second measure;
scaling the textual information based upon the scaling factor;
(significant step) automatically relocating the scaled textual information, by a processor, to the unobscured portion of the first window in a second format during an overlap condition so that the entire scaled textual information is viewable on the computer screen by the user;
(significant step) automatically returning the relocated scaled textual information, by the processor, to the first format within the first window when the overlap condition no longer exists.
These limitations are not merely attempting to limit the mathematical algorithm to a particular technological environment. Instead, these claim limitations recite a specific application of the mathematical algorithm that improves the functioning of the basic display function of the computer itself. As discussed above, the scaling and relocating the textual information in overlapping windows improves the ability of the computer to display information and interact with the user.
The dependent claims are rejected as follows, for the same reasoning as being directed towards patent ineligible subject matter under 35 U.S.C. 101, and not adding eligible subject matter to the respective parent claim.
Claims 2, 3, and 7 are directed to a model analogous to a human brain and tags which are sub-contexts of an instructor editing a translation per se noting if further action is needed e.g. delete? Replace? Such steps can be performed by a human or even mentally in lower qualities of translation words. Similarly, a practical application or improvement is not demonstrated.
NOTE: Regarding claim 7:
If an when independent claims 1 and 5 are remedied, dependent claim 7 may still be rejected under 35 USC 101 as follows under 2106.07 Formulating and Supporting Rejections For Lack Of Subject Matter Eligibility [R-10.2019]:
“When evaluating a claimed invention for compliance with the substantive law on eligibility, examiners should review the record as a whole (e.g., the specification, claims, the prosecution history, and any relevant case law precedent or prior art) before reaching a conclusion with regard to whether the claimed invention sets forth patent eligible subject matter. The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application. For example, even if an independent claim is determined to be ineligible, the dependent claims may be eligible because they add limitations that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception recited in the independent claim. And conversely, even if an independent claim is determined to be eligible, a dependent claim may be ineligible because it adds a judicial exception without also adding limitations that integrate the judicial exception or provide significantly more. Thus, each claim in an application should be considered separately based on the particular elements recited therein.”
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 1-3, 5, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190266249 A1 Xu; Jian-ming et al. (hereinafter Xu) in view of US 20100057439 A1 Ideuchi; Masao et al. (hereinafter Ideuchi).
Re claim 1, Xu teaches
1. (Currently Amended) An estimation system comprising: a processor: and a memory storing therein a set of instructions which, when executed by the processor, cause the estimation system to: (fig. 6-7 CPU based)
receive, as inputs, an extended word alignment estimated between a source sentence and a translated sentence of the source sentence. the extended word alignment referring to a function to show, per word, whether or not the translated sentence is a correct translation of the source sentence, and also referring to a correctly or incorrectly translated or aligned word pair; and (aligning source and target words and showing quality thereof as well as alternatives or what needs to be replace as a suggestion as in fig. 6-7 with 0062-0066 where the user can edit as the system utilizes a learning quality model for alignment to boost quality and estimation as in 0020 and 0024-0025 with 0058 with 0071 user editing)
translation quality tag with which the source sentence and the translated sentence are labeled per word; and (for instance the keys showing quality in fig. 6 or 7…aligning source and target words and showing quality thereof as well as alternatives or what needs to be replace as a suggestion as in fig. 6-7 with 0062-0066 where the user can edit as the system utilizes a learning quality model for alignment to boost quality and estimation as in 0020 and 0024-0025 with 0058)
However, while a user can edit, and the system suggests a replacement option that user must select which inherently deletes the original otherwise and can reasonably read upon insertion per se dependent ton quality analysis and tags thereof, it fails to teach an edit tag per se:
estimate an edit tag based on the extended word alignment and the translation quality tags. (Ideuchi fig. 15 and 0117-0119, tags such as buttons are utilized to show options as buttons for a user e.g. delete or replace/adopt)
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 Xu to incorporate the above claim limitations as taught by Ideuchi to allow for combining prior art elements according to known methods to yield predictable results, for instance combining the express buttons as tags in Ideuchi e.g. fig. 15 for the existing user decisionto accept in Xu e.g. 0020 & 0071, wherein since a user is already involved in a decision, quality of candidates are shown, alignment of translation words are shown, and edits to replace or not the source word, the concept of providing a means for the user to make such decisions is provided in Ideuchi, such that inherencies are now made express for a user to edit and also select thereof on a screen per se as in Ideuchi.
Re claim 5, 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
Re claim 2, Xu teaches
2. (Currently Amended) The estimation system according to claim 1, wherein the processor further causes the estimation system to estimate the extended word alignment between the source sentence and the translated sentence based on a pre-trained extended word alignment model. (model driven aligning source and target words and showing quality thereof as well as alternatives or what needs to be replace as a suggestion as in fig. 6-7 with 0062-0066 where the user can edit as the system utilizes a learning quality model for alignment to boost quality and estimation as in 0020 and 0024-0025 with 0058 with 0071 user editing)
Re claim 3, Xu teaches
(Currently Amended) The estimation system according to claim 1, wherein the processor further causes the estimation system to estimate at least one of the following tags as the edit tag based on the extended word alignment and the translation quality tags: (aligning source and target words and showing quality thereof as well as alternatives or what needs to be replace as a suggestion as in fig. 6-7 with 0062-0066 where the user can edit as the system utilizes a learning quality model for alignment to boost quality and estimation as in 0020 and 0024-0025 with 0058 with 0071 user editing)
However, while a user can edit, and the system suggests a replacement option that user must select which inherently deletes the original otherwise and can reasonably read upon insertion per se dependent ton quality analysis and tags thereof, it fails to teach an edit tag per se:
a replace tag that indicates that a word in the translated sentence needs to be replaced with a correct translation of a word in the source sentence; (Ideuchi replace analogous to adopt by replacing the word and inserting it in its place per se,fig. 15 and 0117-0119, tags such as buttons are utilized to show options as buttons for a user e.g. delete or replace/adopt)
an insert tag that indicates that a translation of a word in the source sentence needs to be inserted in the translated sentence; or (Ideuchi fig. 15 and 0117-0119, insert analogous to adopt by inserting and replacing per se, tags such as buttons are utilized to show options as buttons for a user e.g. delete or replace/adopt)
a delete tag that indicates that a word needs to be deleted from the translated sentence (Ideuchi delete fig. 15 and 0117-0119, tags such as buttons are utilized to show options as buttons for a user e.g. delete or replace/adopt)
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 Xu to incorporate the above claim limitations as taught by Ideuchi to allow for combining prior art elements according to known methods to yield predictable results, for instance combining the express buttons as tags in Ideuchi e.g. fig. 15 for the existing user decisionto accept in Xu e.g. 0020 & 0071, wherein since a user is already involved in a decision, quality of candidates are shown, alignment of translation words are shown, and edits to replace or not the source word, the concept of providing a means for the user to make such decisions is provided in Ideuchi, such that inherencies are now made express for a user to edit and also select thereof on a screen per se as in Ideuchi.
Re claim 7, Xu teaches
7. (Currently Amended) A computer-readable non-transitory recording medium storing therein a program that, when executed by a computer, causes a computer person the method of claim 5. (aligning source and target words and showing quality thereof as well as alternatives or what needs to be replace as a suggestion as in fig. 6-7 with 0062-0066 where the user can edit as the system utilizes a learning quality model for alignment to boost quality and estimation as in 0020 and 0024-0025 with 0058)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220318523 A1 Sheinin; Vadim et al.
Clause extraction
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