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 10/27/2025 has been entered. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant’s arguments, see pages 7-8, with respect to the 35 U.S.C. 101 abstract idea rejection for claims 1-20 have been fully considered but are not persuasive. Applicant’s arguments, see pages 8-9, with respect to the 35 U.S.C. 102 and 103 rejections for claims 1-20 have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 101 abstract idea rejection, the Applicant asserts that the claims are not directed towards an abstract idea or a mental process. The Applicant asserts that the claims integrate the abstract concepts into a practical application, thus complying with Step 2A, prong two. The Applicant also attests that the independent claims constitute an improvement to computer technology, particularly through the use of a machine learning model for machine translation. The Applicant lists improvements of the claimed invention over those known within the art, such as the translation quality being maintained while the length of the translation is increased or reduced.
The Examiner respectfully disagrees. It appears that the Applicant is not specifically identifying what elements and how each limitation is significantly more. The claim, taken as a whole, is merely translating a document and comparing its length to an arbitrary value. This is purely the analysis of a text document. The Examiner has considered all of the limitations as noted by the Applicant as part of the abstract idea as mental activities. The Applicant has not provided any reasoning or evidence as to why the noted limitations are not mental activities. The Examiner also notes in the rejection noted below that the claims only recite a few additional limitations of “by one or more processors” and “using a machine learning model”. These elements, as stated below, are general purpose computing elements. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the 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 the cited art fails to teach or suggest the amended claims and their features. The Applicant asserts that neither Sawayama nor any of the cited references disclose “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” or “comparing, by the one or more processors, a length of the first translated text to a length limit; determining, by the one or more processors, the first translated text exceeds the length limit” with respect for claim 1.
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 Sawayama not disclosing or suggesting “comparing, by the one or more processors, a length of the first translated text to a length limit; determining, by the one or more processors, the first translated text exceeds the length limit”, Sawayama para [0041] states “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,…, 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)”. The predetermined length stated in Sawayama para [0041] is what is claimed as the length limit, and the translated text is determined to be longer than the predetermined length. This also inherently teaches the “comparing” limitation, as in order to determine that the translated text exceeds the predetermined limit, a comparison must be made.
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) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 10, and 19 recite “receiving, by one or more processors, data corresponding to a source text”, “translating, by the one or more processors, the source text”, “comparing, by the one or more processors, a length of the first translated text to a length limit”, “determining, by the one or more processors, the first translated text exceeds a length limit", “translating, by the one or more processors, the source text”, and “and outputting, by the one or more processors, the data corresponding to the second translated text”. These limitations, as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “by one or more processors” and “using a machine learning model”, nothing in the claimed elements preclude the steps from being practically performed by a person reading a document, translating the document, reasoning that the document is too long, and then translating a lesser amount of the document.
This judicial exception is not integrated into a practical application because the additional elements “by one or more processors” and “using a machine learning model” are generic computer components and are recited at such a high level of generality. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claims 1, 10, and 19 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “by one or more processors” and “using a machine learning model” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Step 2B).
Dependent claims 2-9, 11-18, and 20 are directed to further abstract details relating to the text translation and the use of the machine learning models. These limitations are also related to the abstract idea of “mental processes”. That is, nothing in the claimed elements preclude the steps from practically being performed by a person reading a document, translating the document, reasoning that the document is too long, and then translating a lesser amount of the document. The added limitation of “training, with the one or more processors, the machine learning model constrained by length” is not recited with sufficient specificity as to provide any details about how the machine learning model is trained or how the translating is performed. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-2, 5, 10-11, 14, and 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sawayama et al. (US Patent Application Publication No. 2022/0207243), hereinafter referred to as Sawayama.
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 first translated text exceeds the ("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]);
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 translating the text (reference character S5) following the numerical value range being changed (reference characters S2 and S3));
and outputting, by the one or more processors, the data corresponding to the second translated text (Sawayama Fig. 8 reference character 1006).
Regarding claim 2, Sawayama 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 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 ("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 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)).
Regarding claim 5, Sawayama discloses all of the limitations of claim 1. Sawayama further discloses increasing, with the one or more processors, a randomness value of the length limit ("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]).
As to claim 10, system claim 10 and method claim 1 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 10 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 11, system claim 11 and method claim 2 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 11 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 14, system claim 14 and method claim 5 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 19, computer-readable medium (CRM) claim 19 and method claim 1 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 20, CRM claim 20 and method claim 2 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to the method claim.
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) 3 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sawayama, in view of Lepeltier (US Patent Application Publication No. 2017/0075877).
Regarding claim 3, Sawayama discloses all of the limitations of claim 1. 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. 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 ("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 by including Lepeltier’s method of including text parameters gathered from the said text within the text itself. While the length is not an explicitly mentioned parameter by Lepeltier, it would have been an obvious inclusion to make considering that it is a common parameter of text within the art. Appending the length of the text to the beginning of the text would also have been an 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 as a translated document. 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 12, system claim 12 and method claim 3 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 12 is similarly rejected under the same rationale as applied above with respect to the method claim.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sawayama, in view of Rathinasamy et al. (US Patent Application Publication No. 2022/0164174), hereinafter referred to as Rathinasamy.
Regarding claim 4, Sawayama 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 ("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.
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.
Claim(s) 6-9 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sawayama, in view of Rathinasamy, and further in view of Lepeltier.
Regarding claims 6 and 9, Sawayama discloses all of the limitations of claim 1. 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. While the length is not an explicitly mentioned parameter by Lepeltier, it would have been an obvious inclusion to make considering that it is a common parameter of text within the art. Appending the length of the text to the beginning of the text would also have been an 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 as a translated document. 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 Rathinasamy and further in view of Lepeltier, discloses all of the limitations of claim 6. 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 ("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. While the length is not an explicitly mentioned parameter by Lepeltier, it would have been an obvious inclusion to make considering that it is a common parameter of text within the art. Appending the length of the text to the beginning of the text would also have been an 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 as a translated document. 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 Rathinasamy and further in view of Lepeltier, discloses all of the limitations of claim 6. 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. While the length is not an explicitly mentioned parameter by Lepeltier, it would have been an obvious inclusion to make considering that it is a common parameter of text within the art. Appending the length of the text to the end of the translated text would also have been an 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 as a translated document. 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 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 claim 16, system claim 16 and method claim 7 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 17, system claim 17 and method claim 8 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to the method claim.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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