DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to the amendment filed April 16, 2026. Claims 1-5, 8-12, and 15-19 have been amended. Claims 6-7, 13-14, and 20 have been cancelled. Claims 1-5, 8-12, and 15-19 remain pending.
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 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 8, and 15 are directed to method, computer program product, and system for training, by one or more processors, one or more deep learning models using data representations of one or more pre-defined user interfaces, the one or more deep learning models for classifying proximal elements in user interfaces on both a semantic relationship of user interface elements and a location relationship of user interface elements; retrieving, by one or more processors, a user interface for translation to a second language, wherein the user interface comprises a plurality of user interface elements in a first language; determining, by the one or more processors associated with the trained one or more deep learning models, at least one semantic cluster of the plurality of elements; determining, by the one or more processors associated with the trained one or more deep learning models, at least one location cluster of the plurality of user interface elements; and generating, by the one or more processors associated with the trained one or more deep learning models, a translation of the plurality of elements in the first language to the second language, wherein the translation of the plurality of user interface elements maintains proximity of i) the at least one semantic cluster of the plurality of user interface elements or ii) the at least one location cluster of the plurality of user interface elements. The limitation for “training…deep learning models…using data representations of one or more pre-defined user interfaces…’ is a mathematical process that can be achieved by the person, using pen and paper, to determine element/word similarity and determine appropriate groupings. The limitation for “retrieving..a user interface for translation…comprising a plurality of elements..” is a data gathering step that can be achieved by a person obtaining a translation request of multiple elements from a display or that is written on a piece of paper. The limitation for “determining….at least one semantic cluster…” can be achieved by the person reading the elements and using natural language processing, organizing the elements/words based on contexts/meanings and organizing the elements into similar groupings. The limitation for “determining …a location cluster…” can be achieved by the person reviewing the elements and grouping elements based on location within the document. The step for “generating a translation…”can be achieved by the person, using pen and paper, translating the elements/words and maintaining the organizational/hierarchy/location of the elements within the paper. The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, system, computer program product, and generic computer components (processor/media/computer instructions). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer, system, computer program product, and generic computer components (processor/media/computer instructions). amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic computer, system, computer program product, and generic computer components (processor/media/computer instructions). to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-5, 9-12, and 16-19 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating semantic/location data of the elements/words; implementing natural language processing rules or principles to compare elements and performing mathematical calculations to determine element/word similarity and determine appropriate groupings.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 8-10, 15-17 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Ren et al (WO 2019/041084), hereinafter Ren.
Ren discloses translating text in user interfaces based on user interface contexts. Regarding claim 1, Ren teaches a method comprising: training, by one or more processors, one or more deep learning models using data representations of one or more pre-defined user interfaces, the one or more deep learning models for classifying proximal elements in user interfaces on both a semantic relationship of user interface elements and a location relationship of user interface elements [ para 0042, the UI translation engine can be a machine learning engine that can learn over time, see also para 0031-0032, 0045; regarding semantic relationships, see the hierarchical relationships, relating items in categories –see para 0016-0019, based on meanings – para 0027; and proximity – see para 0020, defining spatial relationships]; retrieving, by one or more processors, a user interface for translation to a second language, wherein the user interface comprises a plurality of user interface elements in a first language [Fig. 1; para 0014]; determining, by the one or more processors associated with the trained one or more deep learning models [para 0042, the UI translation engine can be a machine learning engine that can learn over time, see also para 0031-0032, 0045], at least one semantic cluster of the plurality of user interface elements [hierarchical relationship – para 0016-0019; 0023-0028; 0030; 0039; 0043]; determining, by the one or more processors associated with the trained one or more deep learning models [para 0042, the UI translation engine can be a machine learning engine that can learn over time, see also para 0031-0032, 0045], at least one location cluster of the plurality of user interface elements [proximity relationship -- para 0020; 0029; 0030; 0039; 0043]; and generating, by the one or more processors associated with the trained one or more deep learning models [para 0042, the UI translation engine can be a machine learning engine that can learn over time, see also para 0031-0032, 0045], a translation of the plurality of user interface elements in the first language to the second language, wherein the translation of the plurality of user interface elements maintains proximity of i) the at least one semantic cluster of the plurality of user interface elements or ii) the at least one location cluster of the plurality of user interface elements [Fig 1; para 0015; translating (at 104) , based on an identified relationship between UI elements in the UI context, text in the UI from a first language to a second language different from the first language].
Regarding claim 2, Ren teaches the at least one semantic cluster is determined based on a semantic similarity between labels or values of the plurality of user interface elements in the user interface [hierarchical relationship – para 0016-0019; 0023-0028; 0030; 0039; 0043].
Regarding claim 3, Ren teaches one location cluster of the plurality of user interface elements is determined based on a rendered location of the plurality of user interface elements in the user interface [proximity relationship -- para 0020; 0029; 0030; 0039; 0043].
Claims 8-10, 15-17 are rejected under similar rationale as claims 1-3.
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 4-5, 11-12, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Jayaraman et al (US Patent Application Publication No. 2023/0153342).
Regarding claims 4-5, 11-12, and 18-19, Ren teaches processing the elements using correlation techniques [para 0043], but fails to specifically teach processing the semantic clusters in comparison to thresholds. In a similar field of endeavor, Jayaraman teaches clustering and dynamic reclustering of similar textual documents [para 0007; 0190; 0193; 0203]. One having ordinary skill in the art would have recognized the advantages of implementing the clustering/threshold processing suggested by Jayaraman, in the system of Ren, and the results would be predictable in improving the clustering processing ensuring the elements are appropriately grouped, thus ensuring correct translations are generated, thereby improving the system and the user’s experience.
Response to Arguments
Applicant's arguments filed 4/16/2026 have been fully considered but they are not persuasive.
As per applicants arguments toward the 35 USC 101 rejection:
Regarding the “Step 2A, Prong One” argument presentation, found on pp 12-16 of the response, examiner argues that looking at data in one language, and organizing the data elements according to meaning, and translating into a second language, CAN be performed by mental steps in a timely manner. Applicants have added additional claim language toward “deep learning models using data representations …for classifying proximal elements in user interfaces on both a semantic relationship of use interface elements and a location relationship of user interface elements”; however, following in the claims, is, “one or more processors” ASSOCIATED with the “trained….models” and furthermore, in the initial step of using deep learning models to classify the elements, that classification is not executed/used in the following steps in the claims. In other words, the claim elements do NOT implement the data steps toward proximity/location and semantic relations; noting the parallel mapping to “Example 39” by applicant, examiner argues that the elements of Example 39 perform transformations of modified image data into training data sets via the neural network, actively modifying/transforming the data to a second training set. Such active steps of modifying/changing, transforming practical information, is NOT found in the current claim scope.
Regarding the “Step 2A, Prong 2A” argument presentation, found on pp 16-18(top 1/3rd) of the response, examiner argues that the recited claim elements do NOT improve the function of the processor itself (ie, does not operate the processor more efficiently/faster speed/etc.). Furthermore, the recited sections of the disclosure, especially on pp 17 of the response, toward “technical problem to be solved”, examiner argues that the physical locations of the elements based on pagination, render size, and other factors, are not currently claimed. Due to the broadness of the current claim scope, the arguments presented under Step2A Prong 2A are not persuasive.
Regarding the “Step2B” arguments on pp 18-20 of the response, toward “recite additional elements that are sufficient to amount to significantly more than the judicial exception”; examiner notes that the arguments are focused on “search for an inventive concept”. MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More; see II “Eligibility Step 2B: Whether the Additional Elements Contribute an ‘Inventive Concept’”, examiner notes that the MPEP clearly states:
In Step 2B, examiners should:
• Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and
• Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Examiner repeats the arguments above in Step2AProng2 and at this juncture, the claim scope including the machine learning models is considered to be conventional, understood, routine activity. Furthermore, examiner notes that the specification paragraph provided by applicant, that discusses specific improvements of certain features; these features are not currently claimed/cannot be tied to the recited claims
As to applicants arguments presented on pp 21-22 of the response, examiner notes that these arguments are toward the newly presented amended claim language. These additional elements have been mapped to the Ren reference as indicated in the rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Desir can be reached at 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
Prosecution Insights