CTNF 18/177,467 CTNF 87883 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Claims 1-20 are pending. Claims 1, 17, and 20 are independent. This Application was published as U.S. 20240296296. Apparent priority: 2 March 2023. Note: “[0016] … A computer-readable medium or machine-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se ….” 35 U.S.C. 112(f) Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “processing unit” in Claims 17-19. These limitations are generic in the context of the art and don’t refer to any specific structure and only serve as placeholders for the structure that performs the associated function(s) without providing any information about what that structure is. MPEP 2181 I A says: For a term to be considered a substitute for "means," and lack sufficient structure for performing the function, it must serve as a generic placeholder and thus not limit the scope of the claim to any specific manner or structure for performing the claimed function. It is important to remember that there are no absolutes in the determination of terms used as a substitute for "means" that serve as generic placeholders. The examiner must carefully consider the term in light of the specification and the commonly accepted meaning in the technological art. Every application will turn on its own facts. Based on the ordinary skill in the art and description of functions of these components in the Specification, they refer to processors or a combination of processor and memory and possibly transducers such as microphones and displays or to a combination of software and hardware. PLEASE NOTE: This is NOT a rejection . Please don’t address it as a rejection. If the Applicant does not agree with the INTERPRETATION, he may argue or amend to replace the terms interpreted under 112(f) with structural terms such as “microphone” or “processor” as appropriately supported by the Specification. In the alternative, he may let the interpretation stand if the intent was to include a means plus function limitation in the Claim. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Step 1: The independent Claims are directed to statutory categories: Claim 1 is a method claim and directed to the process category of patentable subject matter. Claim 17 is a system claim and directed to the machine or manufacture category of patentable subject matter. Claim 20 is a computer-readable-storage device claim and is directed to the machine or manufacture category of patentable subject matter. Step 2A, Prong One: Does the Claim recite a Judicially Recognized Exception? Abstract Idea? Are these Claims nevertheless considered Abstract as a Mathematical Concept (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion), or Certain Methods of Organizing Human Activity (1-fundamental economic principles or practices (including hedging, insurance, mitigating risk), 2-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), 3- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and fall under the judicial exception to patentable subject matter?) The rejected Claims recite Mental Processes or Methods of Organizing Human Activity. Step 2A, Prong Two: Additional Elements that Integrate the Judicial Exception into a Practical Application? Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application. The rejected Claims do not include additional limitations that point to integration of the abstract idea into a practical application and are therefore directed to the abstract idea. Claim 1 is a generic automation of a mental process of selecting a rich text format to be used for a translated version of an input that is in rich text format and in a source language and is to be translated into a target language. 1. A computer-implemented method comprising: determining one or more candidate formats for source rich text; [User writes down a list of candidate formats (bold, italic, underlined) observed in a text that is in rich text format and includes such formatting already. The text is in English =source language.] (What are the candidate formats? How are they determined? Specify.) obtaining one or more images corresponding to the one or more candidate formats by rendering the source rich text in the one or more candidate formats; [User writes/renders or types in the text in bold, italic, or underline formats.] (What is the image? Is it’s a scanned text document? Specify.) (How are the images obtained? On a screen? Different versions of a document that is formatted differently? Specify.) selecting, from the one or more candidate formats, a target format for the source rich text by validating the one or more candidate formats based on the one or more images; and [User decides on the bold format.] (How is the selection made? What are the criteria for selection?) providing, based on the target format, a translation editing environment for editing a translation of the source rich text. [User tuns on the bold on his word processor and starts typing in Chinese = target language, the translation of the text which was originally in English.] (Translation has to be defined. Translation from digital to viewable text? Translation between formats? Translation from one natural language to another? Specify.) Step 2B: Search for Inventive Concept: Additional Elements Do not amount to Significantly More : The limitations of display, processors, memory, and computer programs, that appear in the counterpart system and CRM Claims 17 and 20 but not in the method Claim 1, are well-understood, routine, and conventional machine components that are being used for their well-understood, routine, conventional and rather generic functions. Additionally, these limitations are expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they are not sufficient to cause the Claim as a whole to amount to significantly more than the underlying abstract idea. The Dependent Claims do not add limitations that could integrate the abstract idea into a practical technological application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim: 2. The computer-implemented method of claim 1, wherein determining the one or more candidate formats for the source rich text comprises: determining the one or more candidate formats by performing a textual analysis of the source rich text. [User looks at the text and notices that it is italicized.] 3. The computer-implemented method of claim 2, wherein performing the textual analysis of the source rich text comprises: applying a rule-based parser to the source rich text. [User makes the determination based on the rules that he has been taught on the definition of italic, bold, underlined, etc.] 4. The computer-implemented method of claim 3, wherein performing the textual analysis of the source rich text further comprises: filling in a missing part of the source rich text to obtain complete raw data by applying an auto-completion engine to the source rich text. [User considers who to complete unfinished parts of the text. Alternatively, the user applies the autocomplete on his word processor. This Claim is not about the internal operations of autocomplete which would be a practical technological application and rather pertains to the use of an autocomplete process which is not technological or anyone typing into a chat would be an inventor.] 5. The computer-implemented method of claim 1, wherein validating the one or more candidate formats based on the one or more images comprises: performing, for an image of the one or more images, image recognition to recognize a plurality of elements in the image; [User looks at the text and can see if it is in bold or italics. Alternatively, user applies OCR to the text.] identifying a number of non-text elements from the plurality of elements in the image; and [User can look and see how many $$, ?, etc. there are in the text. (See Figure 5 and [0063]). Alternatively, OCR too identifies the images or leaves them unrecognized as boxes that can be counted to obtain a number.] validating a candidate format corresponding to the image in the one or more candidate formats based on the number of non-text elements in the image. [User determines that the number is too high and thus the text cannot be shown in an italicized format.] (Specification: “[0068] The image recognition module 420 may determine the validation result 415 based on the number of non-text elements in the image 404. A smaller number of non-text elements may indicate higher validness or reasonableness of the candidate format 402. In some embodiments, the number of non-text elements may be compared with a predetermined threshold number of non-text elements for the validation.”) 6. The computer-implemented method of claim 5, wherein identifying the number of non-text elements from the plurality of elements in the image comprises: identifying elements of semantically complete text from the plurality of elements by applying a natural language processing (NLP) model to the plurality of elements in the image; and [User can evaluate whether $$ makes sense in the context of the input text. The NLP is generically used: WERC: well-understood routine conventional component used in its well-understood routine conventional function. No new use or implementation.] identifying from the plurality of elements in the image, remaining elements other than the elements of semantically complete text as the number of non-text elements. [User can decide that % and # make no sense in context.] (Specification: “[0066] In some embodiments, the image recognition module 420 may apply a natural language processing (NLP) model to the plurality of elements in the image 404 to identify elements of semantically complete text from the plurality of elements in the image 404. The semantically complete text may comprise a complete word or sentence in term of semantics. The image recognition module 420 may identify, from the plurality of elements, remaining elements other than the elements of semantically complete text as the non-text elements 425.”) 7. The computer-implemented method of claim 6, wherein the target format is a first rich-text format, and the method further comprises: determining a second rich-text format for the remaining elements; and [User decides that the translated text should be in bold.] (This limitation has no criteria for a machine to determine the second format. How would a machine come up with the format? This can only be done by a person who does not need instructions.) identifying a rich-text segment corresponding to the second rich-text format from the source rich text. [User finds a bolded segment of the input text/source text.] 8. The computer-implemented method of claim 1, wherein validating the one or more candidate formats based on the one or more images comprises: applying, for an image of the one or more images, an image classifier to the image to validate if the source rich text is rendered in a correct rich-text format, [User looks to see if the word processor is typing the ? correctly in bold.] wherein the image classifier is trained based on a training dataset comprising positive examples and negative examples, wherein the positive examples comprise a first set of images each obtained from rendering respective information in a correct rich-text format and the negative examples comprise a second set of images each obtained from rendering respective information in an incorrect rich-text format. [This limitation expresses a standard training process which is expressed parenthetically by a wherein clause. The Claim is not training the image recognizer; the Claim is using a pre-trained image recognizer.] 9. The computer-implemented method of claim 1, wherein validating the one or more candidate formats based on the one or more images comprises: validating the one or more candidate formats based on layouts of elements in the one or more corresponding images. [User looks to see if with a particular candidate format (bold, e.g.) all the other components like Tables and Graphs end up in the correct place or the indentation gets messed up due to the change in font to bold.] 10. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises providing at least one of the following in a translation editor of the translation editing environment: a translation of a first rich-text segment in the source rich text, [User either translates the text himself or feeds it to a machine translator to get a translation of a segment.] a translation of a second rich-text segment in the source rich text, the translation of the second rich-text segment being formatted with a converted rich-text format based on cultural conventions associated with a source language of the source rich text and a target language of the translation of the source rich text, or a third rich-text segment in the source rich text without being translated. 11. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing an indication of converting a source rich-text format of a rich-text segment in the source rich text into a converted rich-text format for a translation of the rich-text segment in the translation of the source rich text. [User takes a yellow highlighter and highlights the segments that he is going to translate.] 12. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing an indication of avoiding modification to a rich-text segment in the translation of the source rich text. [User takes a pink highlighter and highlights the segments that should not be translated.] (Specification: “[0082] In some embodiments, the translation editor 600 may provide, in the translation editing window 620, an indication avoiding modification to a rich-text segment in the translation of the source rich text. For example, the translation editor 600 may provide an indication 651 beside the rich-text segment 631 formatted with a formula format to avoid inappropriate translation of the formula.”] 13. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing, in a translation editor of the translation editing environment, highlighting of a rich-text segment in the translation of the source rich text. [User takes a yellow highlighter and highlights the translations.] (Specification: “[0083] In some embodiments, the translation editor 600 may provide, in the translation editing window 620, an indication of converting a source rich-text format of the rich-text segment in the source language into the converted rich-text format of the rich-text segment in the target language. For example, the translation editor 600 may provide an indication 652 beside the rich-text segment 633 with the converted rich-text format.”) 14. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: updating the translation of the source rich text by removing an auto-completed part in the translation of the source rich text; and [User can edit his translations on paper or on a screen and take out translations of parts that he did not write but were inserted by the autocomplete.] providing the updated translation of the source rich text in a translation editor of the translation editing environment. [User can provide the updated/redacted version on paper or on the screen.] (Specification: “[0085] In some embodiments, when providing the translation of source rich text in the translation editing window 620, the translation editor 600 may update the translation of the source rich text by removing an auto-completed part in the translation of the source rich text. The translation editor 600 may provide, in the translation editing window 620, the updated the translation of the source rich text.”] 15. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing a translation preview window displaying an image obtained from rendering the translation of the source rich text in the target format. [User can see his translation on the screen including the image parts such as $$ or ?] (Figure 7, 740 and “[0104] In some embodiments, providing the translation editing environment may include providing, by the one or more processors, a translation preview window displaying an image obtained from rendering the translation of the source rich text in the target format.”) ( 16. The computer-implemented method of claim 1, wherein the source rich text comprises program integrated information (PII). [User inserts the PII when he is writing down the source text. ] (Specification: ‘[0039] The source rich text 210 may comprise source data formatted with one or more rich-text formats. In some embodiments, the source rich text 210 may comprise program integrated information (PII) comprising rich text. The term PII may refer to strings extracted from software codes . The PII may be stored in a PII file with a rich-text format, e.g., a HTML file, a YAML file, or a MARKDOWN file.” See also [0040] showing an example of PII which is in NL and can be written by a person.) With respect to Independent Claim 17 and independent Claim 20 , which have limitations similar to the limitations of Claim 1, the limitations of “processing unit” which is interpreted as being implemented by a processor and “a memory,”, or "machine readable storage medium" are well-understood, routine and conventional components that are engaged in their generic functions and are additionally expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they do not include additional limitations that can 1) integrate the Abstract Idea into a practical application or 2) cause the Claim as a whole to amount to more than the underlying abstract idea. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 9-10, 13, 15, 17-19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Asgekar (U.S. 20230142052) in view of Gaydaenko (U.S. 20150199422) . Regarding Claim 1 , Asgekar teaches: 1. A computer-implemented method comprising: determining one or more candidate formats for source rich text; [Asgekar, Figure 2, “media content receiver 230” and “formatting rules determiner 235.” “[0092] The formatting rules determiner 235 can determine initial formatting rules 275 for the media content 270 . The formatting rules determiner 235 can determine the initial formatting rules 275 based on the presentation attributes of each of the media modalities present in the media content 270. …” The “presentation attributes” of textual content are later taught to included font See also Figure 3, “media content 270” and “initial formatting rules 275” associated with the “media content 270.” Figure 4, “receive media content item 402” and “determine initial formatting rules 404.” “The present disclosure provides systems and methods for formatting and generating teaching media for different contexts. A system can receive, from a client computing device, media content having one or more media modalities each having presentation attributes. The system can determine initial formatting rules for the media content based on the presentation attributes of the one or more media modalitie s….” Abstract. “[0005] … identifying the destination format for the media content can include transmitting a set of potential destination formats to the client computing device. … identifying the destination format for the media content can include receiving, from the client computing device, a selection of the destination format from the set of potential destination formats….”] obtaining one or more images corresponding to the one or more candidate formats by rendering the source rich text in the one or more candidate formats; [Asgekar, “[0095] … In some implementations, the destination format identifier 240 can identify the destination format by transmitting one or more queries to the computing device that provided the media content 270 ( e.g., the provider device 260, the client device 220, etc.). The queries can include one or more potential destination formats, which can be presented in one or more user interfaces on a display of the provider device 260 . The user interfaces can have actionable objects that allow a user to select one or more of the destination formats ….” Figure 3, “adjusted images” indicates that the initial media content included images: “0090] For example, a presentation attribute for an image can be an image size, an image position, image colors (e.g., color depth, bit depth, grayscale, etc.), image interactive features (e.g., zoom, pan, etc.), or other presentation features. Presentation attributes for text can include font size, font typeface, font colors, character spacing, or other presentation features of text based content….”] selecting, from the one or more candidate formats, a target format for the source rich text by validating the one or more candidate formats based on the one or more images ; and [Asgekar, Figure 2, “destination format identifier 240.” The destination/target format may be selected by the provider or a user after viewing the images of the different formats on “a display of the provider device 260.” [0095] The destination format identifier 240 can identify a destination format for the media content 270 . The destination format can include at least one formatting requirement . A formatting requirement can correspond to a requirement for different types of content (e.g., different content modalities, etc.). … The destination formats can include, for example, word documents, presentation slides, flash cards, electronic textbook pages, webpages, native application resources, online quiz questions, online practice questions, or online exam questions, among others. Upon selecting one or more of the potential destination formats, the provider device 260 or the client device 220 can transmit a message that indicates each of the selected potential destination formats to the educational content system 205 ….” Figure 3, “optimal parameters for destination format,” and Figure 4, “identify destination format 406.” Figure 3, “Adjusted Fonts” and “Adjusted Images.”] providing, based on the target format, a translation editing environment for editing a translation of the source rich text. [Asgekar, Figure 2 shows the “client devices 220A, 220B” that include displays. the following teaching indicates presentation of the modified format text and image at destination: “[0006] … In some implementations, identifying the destination format can include modifying the initial formatting rules for the media content based on presentation capabilities of the device type on which the destination format will be presented .” This Claim limitation is not specifying that “Translation” is from one natural language to another. Asgekar teaches the translation of one format to another and thus teaches the Translation of this Claim: “[0130] … The in formation resources can be generate by parsing the formatting rules, which can be stored as a generic markup language that specifies different parameters of the media content, such as display position, display size, and other rendering information, and translate the generic markup language into an information that is suitable for an information resource . For example, Word processing documents can utilize XML markup to s pecify how content is displayed in word-processing documents. The educational content system can identify, or each presentation attribute of a given portion of the media content, each formatting rule having the highest priority, and t ranslate that formatting rule into suitable XML or other language suitable for the format of the destination information resource ….”] Asgekar in [0095] teaches selection of one of the target/destination formats by a provider (260) or user (220) on their screens but does not teach that “final information resource 280” is edited. Gaydaenko teaches: providing, based on the target format, a translation e diting environment for editing a translation of the source rich text. [ Gaydaenko is directed to a document text editor that permits the user to edit the text on a screen and includes translated texts. “[0017] Document editing application supports can support a specific type of document. For example, if the "type" is "text document," then the formats Microsoft Word.TM., a rich text editor format, and OpenDocument Text are possible. …” “[0031] Described herein are systems and methods for a universal representation of documents that is suited for use by various editors and application software, particularly if the representation needs to be changed, such as if it needs to be translated into another language ….” “0032] The text data can represent text documents in various formats. A format is a binary representation of text data and non-text data. For instance, html, docx, xls are examples of binary formats. Unusual, but additional possible examples--executables (exe) and resource (rc) files. An editor allows these documents to be edited and exported in the original format while preserving all the data. Automated editing is possible, as is manual editing in a WYSIWYG editor. Embodiments allow analysis and modification of documents in various formats without loss of data. This is a problem faced by machine translation programs.”] Asgekar and Gaydaenko pertain to formatting of text documents and it would have been obvious to combine the text editor of Gaydaenko which can be used for different formats and languages in order to permit the user to edit the result. This combination falls under combining prior art elements according to known methods to yield predictable results or use of known technique to improve similar devices (methods, or products) in the same way. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Regarding Claim 9 , Asgekar teaches: 9. The computer-implemented method of claim 1, wherein validating the one or more candidate formats based on the one or more images comprises: validating the one or more candidate formats based on layouts of elements in the one or more corresponding images. [Asgekar, Figure 4, “content compatible with destination format? 408” teaches the validation of the Claim. “[0091] In some implementations, t he media content 270 received from the provider device 260 or the client device 220 can specify preliminary layout information . As described herein above, the media content 270 received from the provider device 260 or the client device 220 can include multiple media modalities (e.g., one or more images, text, vi deos, audio, etc.). Each of these items can make up the media content 270, and can include preliminary layout information. For example, if the media content 270 is a practice question that includes an image and text information, the media content 270 can further specify how the image should be presented relative to the text information . ….” “[0094] … For example, the formatting rules determiner 235 can determine the initial formatting rules 275 for each presentation attribute as corresponding to the preliminary layout information indicated in the above. The preliminary layout information can indicate, for example, the relative size of two images to one another, the relative position of two images to one another when presented as part of an information resource 280, among any other layout information described herein.” “[0103] In some implementations, the machine learning model can be trained to optimize the layout of the content by modifying the formatting rules 275 based on historic user interactions….”] Regarding Claim 10 , Asgekar teaches: 10. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises providing at least one of the following in a translation editor of the translation editing environment: a translation of a first rich-text segment in the source rich text, [Asgekar, Figure 3, the “final information resource 280” has translated the formatting rules to the “desired destination format information” and Figure 2 showing the presentation of the format translated content at different client devices. [0095] provided above talks about presenting the different destination/target/translated formats to a user or a provider and letting them select.] a translation of a second rich-text segment in the source rich text, the translation of the second rich-text segment being formatted with a converted rich-text format based on cultural conventions associated with a source language of the source rich text and a target language of the translation of the source rich text, or a third rich-text segment in the source rich text without being translated. Regarding Claim 13 , Asgekar does not teach the highlighting. Gaydaenko teaches: 13. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing, in a translation editor of the translation editing environment, highlighting of a rich-text segment in the translation of the source rich text. [Gaydaenko: “[0052] The universal representation can be used in a machine translation system. Other systems that require processing and modification of documents into documents in different formats can use the universal representation. For example, [0053] a check that highlights and corrects mistakes; o r [0054] prevention of data loss.” “[0070] FIG. 3A illustrates an example of source text in a web browser. FIG. 3B shows objects extracted from the text. This text is broken down into blocks (301, 302, 303, 304 and 305). The extracted objects also include links (311, 312, 313 and 314), highlighted text (321 and 322) a nd text with strikethrough (331). Other objects that are not shown can be extracted. For example, text that has been bolded, italicized, etc., can be extracted. FIG. 3C shows source HTML code that corresponds to this text.”] Rationale as provided for Claim 1. The presentation on the editor was added from Gaydaneko. Details of this feature also come from Gaydaenko under the same rationale. Regarding Claim 15 , references Asgekar does not discuss a translation window. Gaydaenko teaches: 15. The computer-implemented method of claim 1, wherein providing the translation editing environment comprises: providing a translation preview window displaying an image obtained from rendering the translation of the source rich text in the target format. [Gaydaneko: “[0026] FIG. 7 illustrates an example of a text subsystem interface that allows the user to see the source text and the text of the translation into a different language in two windows opened in parallel in accordance with one embodiment.”] Rationale as provided for Claim 1. Claim 17 is a system claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale. Additionally: 17. A system comprising: a processing unit; and [Askegar, Figures 1A-1D including “main processor 121.”] a memory coupled to the processing unit and storing instructions thereon, the instructions, when executed by the processing unit, cause the processing unit to perform actions comprising: [Askegar, Figures 1A-1D including “main memory 122.”] … Claim 18 is a system claim with limitations corresponding to the limitations of Claim 15 and is rejected under similar rationale. Claim 19 is a system claim with limitations corresponding to the limitations of Claim 10 and is rejected under similar rationale. Claim 20 is a computer program product system claim with limitations corresponding to the limitations of method Claim 1 and is rejected under similar rationale. Additionally: 20. A computer program product stored on a machine-readable storage medium and comprising machine-executable instructions, the instructions, when executed on a device, cause the device to perform actions comprising: [Askegar, “[0016] … For example, by appropriate computer programs, which may be carried on appropriate carrier media (computer readable media), which may be tangible carrier media (e.g. disks) or intangible carrier media (e.g. communications signals). Aspects may also be implemented using suitable apparatus, which may take the form of programmable computers running computer programs arranged to implement the aspect. As used in the specification and in the claims, the singular form of ‘a’, ‘an’, and ‘the’ include plural referents unless the context clearly dictates otherwise.”] … 07-21-aia AIA Claims 2-3 are rejected under 3 5 U.S.C. 103 as being unpatentable over Asgekar and Gaydaenko i n view of Jaquinta (U.S. 20130024765). Regarding Claim 2 , Asgekar determines the formats by parsing the formatting rules . See Figure 4, 404. “[0116] The educational content system can determine initial formatting rules (e.g. the initial formatting rules 275, etc.) for the media content (STEP 404). The educational content system can determine the initial formatting rules based on the p resentation attributes of each of the media modalitie s present in the media content. In some implementations, the educational content system can determine the initial formatting rules in response to receiving the media content from the provider device or from the client device. … For example, if the media content provided to the educational content system is a practice question that includes text data, image data, and video data, the formatting rules determine can identify (e.g., parse, extract, etc.) each presentation attribute of each of the text data , image data, and video data in the practice question ….” “[0130] … The in formation resources can be generate by parsing the formatting rules , which can be stored as a generic markup language that specifies different parameters of the media content, such as display position, display size, and other rendering information, and translate the generic markup language into an information that is suitable for an information resource….” Gaydaen parses the source document to identify the format: “[0034] Filters can be used to transform a source document into the universal representation of the text document. A filter of a format is a tool of transforming a document in the format into the universal representation of the document and vice-versa. For example, a filter can parse through the source document and create corresponding elements of the universal representation of the text document. In addition, filters can translate the universal representation into different formats. Accordingly, supporting a new file format can be accomplished by the creation of a filter supporting the new file format. …” The analysis of Gaydaen is arguably a textual analysis and teaches the language of Claim 2 but an even more express reference is cited. Jaquinta teaches: 2. The computer-implemented method of claim 1, wherein determining the one or more candidate formats for the source rich text comprises: determining the one or more candidate formats by performing a textual analysis of the source rich text. [Jacquinta, Figures 5 and 6. Rich Text Data is input to a “Plain text extractor 510” and then to a “rich text attribute extractor 511” and a record is created by the “plain text and richness attribute record generator 512.” Figure 7, 703: “[0058] … The rich text processor 507 may further scan the rich text input data for rich text segments at step 703. In the HTML language, rich data are delimited by the "<" and ">" symbols. For example, the input text <b>car</b> indicates that the word "car" is in boldface when rendered, and the text <i>truck</i> indicates that the word "truck" is italicized when rendered….” Figure 8, 802 and 803. “[0061] In addition, the rich text attribute extractor 511 of the rich text processor 507 may analyze the rich text input data 801 to identify and extract rich text attributes from the input data, which include the word "weather' in itatic, the word "cold" in boldface, and red text for the word "rainy". The record generator 512 may generate a data record 803 that includes these rich text attributes. …”] Asgekar/Gaydaenko and Jaquinta pertain to formatting of text documents and it would have been obvious to modify the textual analysis of the combination with the specifics of the added reference in order to have a specialized textual analyzer. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Regarding Claim 3 , Asgekar teaches: 3. The computer-implemented method of claim 2, wherein performing the textual analysis of the source rich text comprises: applying a rule-based parser to the source rich text. [Asgekar, Figure 3, “Initial Formatting Rules 275” and “Desired Destination Format Information.” The “presentation attributes” are extracted/parsed from the “initial formatting rules 275” and include: “[0114] … Presentation attributes for text can include font size, font typeface, font colors, character spacing, or other presentation features of text based conten t….” Asgekar does not include the phrase “rich text” but the “presentation attributes” that it lists for “text” are the attributes that generate a “rich text.”] 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Asgekar and Gaydaenko in view of Jaquinta and Shevchenko (U.S. 11,258,734) . Regarding Claim 4 , Asgekar, Gaydaenko and Jaquinta do not discuss autocomplete which is tangential to the main idea. Shevchenko teaches: 4. The computer-implemented method of claim 3, wherein performing the textual analysis of the source rich text further comprises: filling in a missing part of the source rich text to obtain complete raw data by applying an auto-completion engine to the source rich text. [Shevchenko: “… The processor may use at least one of a machine learning model, deep learning model, or other statistical learning algorithm for creating the compositional change. The compositional change may be an auto-generated textual completion; the auto-generated textual completion may be a phrasal completion, and the processor may generate the compositional change by optimizing generated language as determined by the processor from the second user communication attribute . The processor may generate the compositional change by replicating a communication style of the first user as determined by the processor from the first user communication attribute ….” 45:54-63.] Asgekar/Gaydaenko/Jaquinta and Shevchenko pertain to formatting of text documents and it would have been obvious to modify the system of the combination with the specifics of the added reference in order to accommodate the autocomplete feature which is common in word processors. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396 . 07-21-aia AIA Claim s 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Asgekar and Gaydaenko in view of Jayaraman (U.S. 20240330895) . Regarding Claim 5 , Asgekar teaches: 5. The computer-implemented method of claim 1, wherein validating the one or more candidate formats based on the one or more images comprises: performing, for an image of the one or more images, image recognition to recognize a plurality of elements in the image; identifying a number of non-text elements from the plurality of elements in the image; and [Asgekar, Figure 3, the input multi-media content can include text, image, and video that are separately handled and thus identified. “… A system can receive, from a client computing device, media content having one or more media modalities each having presentation attributes….” Abstract. Video and image would be the non-text elements of the overall content. Or, to map this limitation, a “media content 270” that includes image and text modalities only is separated into image and text.] validating a candidate format corresponding to the image in the one or more candidate formats based on the number of non-text elements in the image. [Asgekar teaches that the formatting rules 275 are used to determine that the content (including text) is stored according to “appropriate formatting rules” which teaches the “validating a candidate format” of the Claim. “[0087] The database 215 can store or maintain one or more information resources 280. T he information resources can be resources that present specified media content (e.g., specified by instructions in the information resources 280, etc.), according to appropriate formatting rules 275. Said another way, each information resource 280 can specify one or more items of media content 270, and the educational content system 205 can determine which formatting rules 275 to apply to the specified media content 270 to display the specified media content 270 in the information resource 280….” See also Figure 4, 408 determining whether the content is compatible with destination format 408 which teaches the validating of the Claim: “[0112] … determine whether the content is compatible with a format (DECISION 408), modify each item of media content (STEP 410) ….”] Askegar does not discuss details of image analysis. Neither does Gaydaeonko. Jayaraman teaches: performing, for an image of the one or more images, image recognition to recognize a plurality of elements in the image ; [Jayaraman, Figure 2, “image data” is captured by Camera and subjected to the steps of analysis shown in Figure 2. “[0063] The system processes the image data using a content recognition analysis to determine individual content elements in the data, such as letters, numbers, characters, or symbols. …”] identifying a number of non-text elements from the plurality of elements in the image; and [Jayaraman identifies text and non-text elements: “[0089] The segmentation analysis can also divide a transfer instrument or document by splitting the image data into text components, non-text components , encoded components, or standard non-encoded components. A non-text components may be an image or other drawing. Examples of a non-text components could include a provider logo or symbol, a handwritten or digital signature, a holographic image or watermark for security, a personalized mark for a user (e