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 .
Response to Amendment
The Amendment filed 04/28/2026 has been entered. Claims 3, 5, 12, 14, and 21-27 are cancelled. Therefore, claims 1-2, 4, 6-11, 13, and 15-20 remain pending in the application.
Response to Arguments
Applicant’s arguments filed on 04/28/2026 have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 101 abstract idea rejection, on pages 7-9, the Applicant asserts that the features of claim 1 provide for a practical application of improved machine translation that maintains translation quality while accounting for limitations in text length, which can be desirable in scenarios like digital content generation, user interfaces, or dubbing. The Application lists alternative approaches for controlling machine translation length, but they state that these result in low quality translations, inaccurate translation length, and/or increased latency in providing the translation. They assert that by employing a hybrid approach to control machine translation length, translation quality is maintained while the length of the translation is reduced to adhere to the length limit, as well as latency. They cite Ex Parte Desjardins, and further state that because of the aforementioned, since claim 1 improves computer technology due to what they have reference in the Specification, it is not directed to an abstract idea. The Applicant also states that, when considered as a whole, the claims provide for a particular solution to a problem, specifically how to maintain translation quality and latency in providing the translation while accounting for limitations in translation length. The Applicant also states that they amended the claim to further provide for a practical application of improved machine translation that maintains translation quality while accounting for limitations in text length.
The Examiner respectfully disagrees. The improvements the Applicant lists within their arguments and within the Specification itself are set forth in a conclusory manner. It seems that the Applicant is merely repeating these without specifically identifying what elements and how each limitation is significantly more. Regardless, the original claims, and the claims as amended, do not reflect the claimed, disclosed improvements as listed. The original claims, and the claims as amended, when taken apart or as a whole, are merely utilizing computer devices, in this specific case “a machine learning model unconstrained by length” and “a machine learning model constrained by the length token”, as tools to perform a method which is directed to an abstract idea. The claim, under its broadest reasonable interpretation, recite a method, system, and CRM of reading an initial text document, translating this document to create a translated text, comparing this translated text to a length value, deciding whether or not the translated text is longer than the length value, adding or writing a length token to the beginning of the initial text document to express its length limit, translating the initial text document again while adding more length tokens after each translated element to show the remained of length left, and presenting this final translated document. This is an abstract idea in the form of certain methods of organizing human activity (i.e. mental processes such as observation, evaluation, judgement, and opinion). The steps of receiving a text document and processing (i.e. translating) the text document, comparing the document length to a length limit, adding a length token to the beginning of the document, processing it again while adding a length token after each text element, and outputting the final document could be performed by a human using pen and paper or by purely mental reasoning. Further, the claims do not integrate the judicial exception into a practical application. The recitations of “a machine learning model unconstrained by length” and “a machine learning model constrained by the length token” are generic instructions to perform the abstract idea on/using a computer and do not impose a meaningful limit on the judicial exception. The machine learning models are recited at high-levels of generality and are merely used as tools to perform the abstract idea faster and more efficiently. The data gathering (receiving a text) and data outputting steps required to perform the method themselves do not add a meaningful limitation to the method. Mere data gathering and outputting do not provide an inventive concept. There is no improvement to the functioning of the translation, the machine learning models, or to any other technology or technical field reflected within the claims, original or amended. The claims, as written and amended, do not include more than mere instructions to perform the abstract method using generic computer components. Hence, Applicant’s arguments are not persuasive.
With respect to the 35 U.S.C. 102 rejection of claims 1-2, 5, 10-11, 14, and 19-20 under Sawayama et al. (US Patent Application Publication No. 2022/0207243), hereinafter referred to as Sawayama, the 35 U.S.C. 103 rejection of claims 3 and 12 under Sawayama in view of Lepeltier (US Patent Application Publication No. 2017/0075877), claims 4 and 13 under Sawayama in view of Rathinasamy et al. (US Patent Application Publication No. 2022/0164174), hereinafter referred to as Rathinasamy, and claims 6-9 and 15-18 under Sawayama, in view of Rathinasamy, and further in view of Lepeltier, the Applicant asserts that Sawayama fails to disclose all of the features of amended claim 1. The Applicant asserts that Sawayama does not disclose, teach, or suggest “translating, by the one or more processors, the source text using a machine learning model constrained by the length limit to generate data corresponding to a second translated text”. They also state that none of the cited reference disclose, teach, or suggest “adding, by the one or more processors, a length token to a beginning of the source text to represent the length limit” and “translating, by the one or more processors, the source text using a machine learning model constrained by the length token to generate data corresponding to a second translated text, wherein translating the source text using the machine learning model constrained by the length token comprises adding additional length tokens after each text element in the second translated text to represent a remainder of the length limit based on the length token”.
The Examiner respectfully disagrees. In response to Sawayama not disclosing or suggesting “translating, by the one or more processors, the source text using a machine learning model constrained by the length limit to generate data corresponding to a second translated text”, Sawayama Fig. 7 shows the text being translated, per reference character S5, following the numerical value range, i.e., the length limit, being changed in reference characters S2 and S3. Sawayama para [0054] states: “First, the translation unit 11 translates the original sentence using the translated sentence stored by storage unit 10 (step S1). Next, the setting unit 12 sets the numerical value range based on the translation result in S1 (step S2). Next, the change unit 13 changes the internal state based on the numerical value included in the numerical value range set in S2 (step S3), and the storage unit 10 stores the translation model including the changed internal state. Next, the internal state changing device 1 (or change unit 13) determines whether or not a predetermined condition is satisfied (step S4). The predetermined condition is, for example, whether or not the number of loops of the processes of S1 to S4 has reached a predetermined number. Further, for example, the predetermined condition is whether or not the translation quality of the translated sentence by the translation using the translation model including the internal state changed in S3 satisfies the predetermined quality. When the predetermined condition is satisfied in S4 (S4: YES), the translation unit 11 translates the original sentence using the translation model including the internal state changed in S3, and outputs the translated sentence (step S5). On the other hand, when the predetermined condition is not satisfied in S4 (S4: NO), the process returns to S1. In the process in S1 when returning to S1, the original sentence may be translated using the translation model including the internal state changed in S3”. This describes a process of translating the source text to receive data corresponding to a second translated text (S1), using a machine learning model constrained by the length limit, which continually changes in an iterative process until the desired quality is achieved. In response to none of the cited references disclosing, teaching, or suggesting “adding, by the one or more processors, a length token to a beginning of the source text to represent the length limit”, Lepeltier paragraph [0161] states "A text 1010 is displayed (the displayed text can change over time, it can correspond to the first text or one of its variants, e.g. second text or modified text). At time t1, the initial first text is associated with its initial length 1011 and its initial meaning 1012”, and Lepeltier para [0173] states “In one embodiment, the display of one or more comparisons is function of one or more parameters, said parameters comprising one or more predefined thresholds and/or one or more rules. The basis for comparisons can be word by word (with or without order), chunk by chunk, sentence by sentence, paragraph by paragraph (detected by line return), set of paragraphs. The associated threshold can be quantitative, e.g. 0.80 for 80%. For example if 80% of words in a paragraph of the description are identical to words of the claims, a predefined rule can specify that the entire paragraph will be graphically greyed. The associated threshold can be qualitative: as a result of a certain comparison, a ranking can be outputted (e.g. <<very different>>, <<different>>, <<loosely similar>>, <<similar>>, <<very similar>>, <<nearly identical>>, <<identical>>) and the user interface can restitute such assessments. The one or more thresholds can be both quantitative and qualitative (fuzzy logic). Graphical indications can be a function of text comparisons, for example according to identity of similarity of words or sentences, said comparisons being performed according to various granularity (word, chunk, sentence, paragraph).” Both of these show that a parameter (i.e. the length), can be displayed in the document itself. In response to none of the cited references disclosing, teaching, or suggesting “translating, by the one or more processors, the source text using a machine learning model constrained by the length token to generate data corresponding to a second translated text, wherein translating the source text using the machine learning model constrained by the length token comprises adding additional length tokens after each text element in the second translated text to represent a remainder of the length limit based on the length token”, Sawayama, as stated above, teaches “translating, by the one or more processors, the source text using a machine learning model constrained by the length limit to generate data corresponding to a second translated text wherein translating the source text using the machine learning model constrained by the length token” through Fig. 7 and para [0054]. However, Lepeltier teaches “comprises adding additional length tokens after each text element in the second translated text to represent a remainder of the length limit based on the length token” through para [0173], which states “In one embodiment, the display of one or more comparisons is function of one or more parameters, said parameters comprising one or more predefined thresholds and/or one or more rules. The basis for comparisons can be word by word (with or without order), chunk by chunk, sentence by sentence, paragraph by paragraph (detected by line return), set of paragraphs. The associated threshold can be quantitative, e.g. 0.80 for 80%. For example if 80% of words in a paragraph of the description are identical to words of the claims, a predefined rule can specify that the entire paragraph will be graphically greyed. The associated threshold can be qualitative: as a result of a certain comparison, a ranking can be outputted (e.g. <<very different>>, <<different>>, <<loosely similar>>, <<similar>>, <<very similar>>, <<nearly identical>>, <<identical>>) and the user interface can restitute such assessments. The one or more thresholds can be both quantitative and qualitative (fuzzy logic). Graphical indications can be a function of text comparisons, for example according to identity of similarity of words or sentences, said comparisons being performed according to various granularity (word, chunk, sentence, paragraph).” This excerpt from Lepeltier shows that parameters, in this case length, can be displayed within the document and can be compared “word by word, chunk by chunk, sentence by sentence, paragraph by paragraph”. Hence, the Applicant’s arguments are not persuasive.
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.
Claim(s) 1-2, 4, 10-11, 13, and 19-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claims 1, 10, and 19 recite a method, system, and computer-readable medium (CRM), respectively. These claims therefore invoke a statutory category (machine and process) in Step 1 of the Subject Matter Eligibility Test.
Step 2A, Prong One: Independent claims 1, 10, and 19, under their broadest reasonable interpretation, recite a method, system, and CRM of reading an initial text document, translating this document to create a translated text, comparing this translated text to a length value, deciding whether or not the translated text is longer than the length value, adding or writing a length token to the beginning of the initial text document to express its length limit, translating the initial text document again while adding more length tokens after each translated element to show the remained of length left, and presenting this final translated document. This is an abstract idea in the form of certain methods of organizing human activity (i.e. mental processes such as observation, evaluation, judgement, and opinion). The steps of receiving a text document and processing (i.e. translating) the text document, comparing the document length to a length limit, adding a length token to the beginning of the document, processing it again while adding a length token after each text element, and outputting the final document could be performed by a human using pen and paper or by purely mental reasoning.
Step 2A, Prong Two: The claims do not integrate the judicial exception into a practical application. The recitation of “a machine learning model unconstrained by length” and “a machine learning model constrained by the length token” are generic instructions to perform the abstract idea on/using a computer and do not impose a meaningful limit on the judicial exception. The machine learning models are recited at high-levels of generality and are merely used as tools to perform the abstract idea faster and more efficiently. The data gathering (receiving a text) and data outputting steps required to perform the method themselves do not add a meaningful limitation to the method. Mere data gathering and outputting do not provide an inventive concept. There is no improvement to the functioning of the translation, the machine learning models, or to any other technology or technical field.
Step 2B: The claims do not include any additional elements that amount to significantly more than the judicial exception. The only additional elements beyond the abstract idea are the machine learning models, both unconstrained by length and constrained by the length token, which perform generic computational functions such as receiving, analyzing, and outputting data. Such elements are well-understood, routine, and conventional within the field.
Accordingly, claims 1, 10, and 19 are directed to an abstract idea and do not include significantly more than the abstract idea itself.
With respect to claims 2, 11 and 20, the claim relates to the comparing of the final translated text to the length limit and decreasing the length limit iteratively, if the final translated text exceeds that limit, until the length limit is not exceeded by the final translated text. This could be performed by a human using a pen and paper or by purely mental reasoning. The only additional element is the “machine learning model constrained by the length limit”, which is a generic instruction to perform the abstract idea on/using a computing and does not impose a meaningful limit on the judicial exception. No additional elements are present.
With respect to claims 4 and 13, the claims relate to estimating the length of the first translated text. This could be performed by a human using pen and paper or by purely mental reasoning. No additional elements are present.
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.
Claim(s) 1-2, 10-11, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sawayama et al. (US Patent Application Publication No. 2022/0207243), hereinafter referred to as Sawayama, in view of Lepeltier (US Patent Application Publication No. 2017/0075877).
Regarding claim 1, Sawayama discloses a method for length-constrained machine translation, comprising: receiving, by one or more processors, data corresponding to a source text ("More specifically, the translation unit 11 receives the input of the original sentence," Sawayama para [0031]);
translating, by the one or more processors, the source text using a machine learning model unconstrained by length to generate data corresponding to a first translated text ("More specifically, the translation unit 11 receives the input of the original sentence and applies the input original sentence to the translation model stored by storage unit 10 to acquire the translated sentence output," Sawayama para [0031]);
comparing, by the one or more processors, a length of the first translated text to a length limit ("The setting unit 12 may set the numerical value range based on a length (sentence length) of the translated sentence translated using the translation model," Sawayama para [0041] and "For example, the setting unit 12…, may narrow the numerical value range (than the predetermined numerical value range) as the length of the translated sentence is long (than a predetermined length," Sawayama para [0041]));
determining, by the one or more processors, the length of the first translated text exceeds the length limit ("The setting unit 12 may set the numerical value range based on a length (sentence length) of the translated sentence translated using the translation model," Sawayama para [0041] and "For example, the setting unit 12…, may narrow the numerical value range (than the predetermined numerical value range) as the length of the translated sentence is long (than a predetermined length," Sawayama para [0041]);
("The numerical value range is, for example, a range in which a random number (scalar value) used when changing the internal state of the translation model as described later is generated," Sawayama para [0038]);
translating, by the one or more processors, the source text using a machine learning model constrained by the length token to generate data corresponding to a second translated text, wherein translating the source text using the machine learning model constrained by the length token (Sawayama Fig. 7 shows translating the text (reference character S5) following the numerical value range being changed (reference characters S2 and S3) and “The learned model is a model generated by learning by machine learning and is a combination of a computer program and a parameter,” Sawayama para [0024]);
and outputting, by the one or more processors, the data corresponding to the second translated text (Sawayama Fig. 8 reference character 1006).
However, Sawayama fails to disclose adding, by the one or more processors, a length token to a beginning of the source text to represent the length limit; comprises adding additional length tokens after each text element in the second translated text to represent a remainder of the length limit based on the length token.
Lepeltier teaches a method for handling a text expressed in a natural language.
Lepeltier teaches adding, by the one or more processors, a length token to a beginning of the source text to represent the length limit ("A text 1010 is displayed (the displayed text can change over time, it can correspond to the first text or one of its variants, e.g. second text or modified text). At time t1, the initial first text is associated with its initial length 1011 and its initial meaning 1012," Lepeltier para [0161] and Lepeltier para [0173]);
comprises adding additional length tokens after each text element in the second translated text to represent a remainder of the length limit based on the length token (Lepeltier para [0173]-[0174]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device by including Lepeltier’s method of including text parameters gathered from the said text within the text itself. Appending the length of the text to the beginning of the text or after each text element in the second text would also have been a beneficial and obvious inclusion, as this would allow the user to more easily understand the value that the length limit is currently at by observing the output and comparing it to the limit. This appended length would be beneficial for longer strings of text, as it would allow the user to solely note the length token and not count the number of tokens themselves. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 2, Sawayama, in view of Lepeltier discloses all of the limitations of claim 1. Sawayama further discloses comparing, by the one or more processors, a length of the second translated text to the length limit (Sawayama Fig. 7 shows the translation occurring once the predetermined condition is satisfied, i.e., translating constrained by a certain length, comparing is inherent in determining that the length limit hasn’t been surpassed);
determining, by the one or more processors, the length of the second translated text exceeds the (Sawayama Fig. 7 shows the translation occurring once the predetermined condition is satisfied, i.e., translating constrained by a certain length);
and decreasing, by the one or more processors, the length limit ("For example, the setting unit 12…, may narrow the numerical value range (than the predetermined numerical value range) as the length of the translated sentence is long (than a predetermined length," Sawayama para [0041]);
wherein the length limit is iteratively decreased until a translated text translated using the machine learning model constrained by the length limit does not exceed the length limit (Sawayama Fig. 7 shows the numerical value being iteratively changing until the condition is satisfied (reference character S4)).
As to claims 10-11, system claims 10-11 and method claims 1-2 are related as method and system of using same, with each claimed element’s function corresponding to the method step, respectively. Accordingly, claims 10-11 are similarly rejected under the same rationale as applied above with respect to the method claims.
As to claims 19-20, computer-readable medium (CRM) claims 19-20 and method claims 1-2 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step, respectively. Accordingly, claims 19-20 are similarly rejected under the same rationale as applied above with respect to the method claims.
Claim(s) 4, 6-9, 13, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sawayama, in view of Lepeltier, and further in view of Rathinasamy et al. (US Patent Application Publication No. 2022/0164174), hereinafter referred to as Rathinasamy.
Regarding claim 4, Sawayama, in view of Lepeltier, discloses all of the limitations of claim 1. However, Sawayama fails to disclose estimating, by the one or more processors, a length of the first translated text. Rathinasamy teaches a method for training a neural machine translation model.
Rathinasamy teaches estimating, by the one or more processors, the length of the first translated text ("Based on the number of tokens in the first fragment of the source statement, the number of tokens in the corresponding first fragment in the target statement may be estimated," Rathinasamy para [0073]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device by including Rathinasamy’s method of estimating a length of a target statement. This ability for estimation would allow for a better determination of the accuracy of the translation. For machine translation, the longer a document to translate, the lower the accuracy of the translation itself. Estimating the length of the translated text allows for the length limit to be set at a lower value, translating the text in manageable pieces to thereby increase translation accuracy. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claims 6 and 9, Sawayama, in view of Lepeltier, discloses all of the limitations of claim 1. However, Sawayama fails to disclose training, with the one or more processors, the machine learning model constrained by length using training data comprising a plurality of pairs of source text and translated text, each added with one or more length tokens.
Rathinasamy teaches training, with the one or more processors, the machine learning model constrained by the length limit using training data comprising a plurality of pairs of source text and translated text (Rathinasamy Fig. 4 shows training a model based on pairs of source statements and target statements less than or equal to a token limit).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device by including Rathinasamy’s method of training a model with source and target text pairs. Training a model using pairs of source and target text improves translation accuracy, reduces the total training requirements necessary to get a full functioning model, and is more computationally and thereby cost effective. This inclusion would have been obvious to one of ordinary skill in the art.
However, Sawayama, in view of Rathinasamy, fails to disclose each added with one or more length tokens.
Lepeltier teaches each added with one or more length tokens ("In one embodiment, the display of one or more comparisons is function of one or more parameters, said parameters comprising one or more predefined thresholds and/or one or more rules," Lepeltier para [0173] and "Graphical indications can be a function of text comparisons, for example according to identity of similarity of words or sentences, said comparisons being performed according to various granularity (word, chunk, sentence, paragraph)," Lepeltier para [0173]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device and Rathinasamy’s method of training a model with source and target text pairs by including Lepeltier’s method of including text parameters gathered from the said text within the text itself. Appending the length of the text to the beginning of the text would have been a beneficial and obvious inclusion, as this would allow the user to more easily understand the value that the length limit is currently at by observing the output and comparing it to the limit. This appended length would be beneficial for longer strings of text, as it would allow the user to solely note the length token and not count the number of tokens themselves. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 7, Sawayama, in view of Lepeltier and further in view of Rathinasamy, discloses all of the limitations of claim 6. However, Sawayama fails to disclose wherein the source text of each pair comprises a length token added to a beginning of the source text to represent the text length limit.
Rathinasamy teaches wherein the source text of each pair comprises a length token (Rathinasamy Fig. 4 shows training a model based on pairs of source statements and target statements less than or equal to a token limit).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device by including Rathinasamy’s method of including the length of a source text. This inclusion would allow for a better determination of the accuracy of the translation. For machine translation, the longer a document to translate, the lower the accuracy of the translation itself. Including the length of the source text allows for the length limit to be set at a lower value, translating the text in manageable pieces to thereby increase translation accuracy. This inclusion would have been obvious to one of ordinary skill in the art.
Lepeltier teaches added to a beginning of the source text to represent the length limit ("In one embodiment, the display of one or more comparisons is function of one or more parameters, said parameters comprising one or more predefined thresholds and/or one or more rules," Lepeltier para [0173] and "Graphical indications can be a function of text comparisons, for example according to identity of similarity of words or sentences, said comparisons being performed according to various granularity (word, chunk, sentence, paragraph)," Lepeltier para [0173]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device and Rathinasamy’s method of including the length of a source text by including Lepeltier’s method of including text parameters gathered from the said text within the text itself. Appending the length of the text to the beginning of the text would have been a beneficial and obvious inclusion, as this would allow the user to more easily understand the value that the length limit is currently at by observing the output and comparing it to the limit. This appended length would be beneficial for longer strings of text, as it would allow the user to solely note the length token and not count the number of tokens themselves. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 8, Sawayama, in view of Lepeltier and further in view of Rathinasamy, discloses all of the limitations of claim 6. However, Sawayama fails to disclose wherein the translated text of each pair comprises one or more length tokens added after each tokenized text element to represent a remainder of the text length limit.
Rathinasamy teaches wherein the translated text of each pair comprises one or more length tokens (Rathinasamy Fig. 4 shows training a model based on pairs of source statements and target statements less than or equal to a token limit).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device by including Rathinasamy’s method of including the length of a translated text. This inclusion would allow for a better determination of the accuracy of the translation. For machine translation, the longer a document to translate, the lower the accuracy of the translation itself. Including the length of the translated text allows for the length limit to be set at a lower value, translating the text in manageable pieces to thereby increase translation accuracy. This inclusion would have been obvious to one of ordinary skill in the art.
Lepeltier teaches added after each tokenized text element to represent a remainder of the ("In one embodiment, the display of one or more comparisons is function of one or more parameters, said parameters comprising one or more predefined thresholds and/or one or more rules," Lepeltier para [0173] and "Graphical indications can be a function of text comparisons, for example according to identity of similarity of words or sentences, said comparisons being performed according to various granularity (word, chunk, sentence, paragraph)," Lepeltier para [0173]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sawayama’s method of machine translation with an internal state changing device and Rathinasamy’s method of including the length of a translated text by including Lepeltier’s method of including text parameters gathered from the said text within the text itself. Appending the length of the text to the end of the translated text would have been a beneficial and obvious inclusion, as this would allow the user to more easily understand the value that the length limit is currently at by observing the output and comparing it to the limit. This appended length would be beneficial for longer strings of text, as it would allow the user to solely note the length token and not count the number of tokens themselves. This inclusion would have been obvious to one of ordinary skill in the art.
As to claim 13, system claim 13 and method claim 4 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 13 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claims 15 and 18, system claim 15 and method claim 6 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claims 16-17, system claims 16-17 and method claims 7-8 are related as method and system of using same, with each claimed element’s function corresponding to the method step, respectively. Accordingly, claims 16-17 are similarly rejected under the same rationale as applied above with respect to the method claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent No. 10,346,548
US Patent Application Publication No. 2011/0307245
US Patent Application Publication No. 2022/0284196
Niehues, “Machine Translation with Unsupervised Length-Constraints”, 04/07/2020
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM MICHAEL WEAVER whose telephone number is (571)272-7062. The examiner can normally be reached Monday-Friday, 8AM-5PM EST.
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, Richemond Dorvil can be reached at (571) 272-7602. 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.
/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights