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 Arguments
1. Regarding the rejection under 35 U.S.C. § 101, Applicant's arguments filed 04/28/2026 have been fully considered but they are not persuasive.
Step 2A Prong 1:
Applicant first argues on pg. 8 of the Remarks that the claims do not recite mental processes under Step 2A Prong 1, arguing that the claims reflect a technical improvement to a machine translation system rather than a mental process performable by a human. The Examiner respectfully disagrees. The claims as currently written recite several limitations which fall under the abstract idea grouping of mental process. A person can read a character string, analyze the string and write down language information, and use this information to select a particular method of translating. The person can then generate several intermediate translations before completing the translation (e.g. word by word translation). A person can monitor the accuracy of this translation as they translate, and decide to select a new method of translating or to modify the translation if the accuracy is low. Finally, a person can provide this translation to a user. Therefore, the claims recite mental processes under Step 2A Prong 1.
Step 2A Prong 2:
Applicant further argues that the claims integrate the judicial exception into a practical application. Specifically, Applicant argues on pg. 9 of the Remarks that the claims recite a technical improvement to functioning of a computer/technology in the form of an improvement to the operation of machine translation systems via feedback-driven, release-based translation framework. The Examiner respectfully disagrees. Under Step 2A Prong 2, the additional elements present in the claims do not integrate the judicial exception into a practical application. The only additional limitations in the claims are: 1) the receiving/analyzing/identifying/executing/monitoring/determining/outputting steps implemented by the device, executing “the translator model” to generate one or more translation releases, using a “different translator model” to perform a subsequent translation, and displaying the translation within a “user interface (UI)”. These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. No specific architectures/structure/components and/or interconnections between technical components are recited in the claims. The claims are merely using a generic device and generic translator models to perform steps a person can perform mentally with the aid of pen and paper. Further technical details would be needed in order to reflect a technical improvement.
Hence, Applicant’s arguments are not persuasive.
2. Regarding the rejection under 35 U.S.C. § 102, Applicant’s arguments with respect to claims 1-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
3. Regarding the objection to the drawings, Applicant has amended the drawings to address the minor informalities. Accordingly, the objection is withdrawn.
4. Regarding the claim objections, Applicant has amended claims 7 and 15 to address the minor informalities. Accordingly, the objections are withdrawn.
Claim Rejections - 35 USC § 101
5. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, “A method” is recited, which is directed to one of the four statutory categories of invention (process) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
receiving… a request to translate a character string, the character string comprising text: a person obtains and reads a text to be translated.
analyzing…the character string, and determining, based on the analysis, language information associated with the text: the person analyzes what they read and writes down information related to the language in the text.
identifying…based on the language information, a translator model: the person then uses the language information to determine a model (e.g. set of rules) for translating the text
…generate one or more translation releases…of the character string prior to completion of the translation: the person writes down several intermediate translations before completing translation
identifying…a translation release of the one or more translation releases comprising at least a portion of the text being translated from an original language to a target language: the person obtains a translation in the target language
monitoring…an output of the translation release…: a person reviews the translation generated
determining… an accuracy of the translation release, wherein a feedback mechanism is selectively enabled for the translation release based on the determined accuracy: the person analyzes the translation to determine how accurate it is, and decides to selectively use feedback in response to accuracy (e.g. in response to a low accuracy).
continuing, based at least in part on the feedback mechanism, translation of the character string by at least one of modifying the translation release…: a person modifies the translation (e.g. fixes grammatical errors) in response to feedback
and outputting… for display …, a translated output corresponding to the continued translation: a person writes down the final continued translation and presents it to a user.
Claim 1 does not contain any additional limitations which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are performing each step “by a device”, “executing, by the device, the translator model to generate one or more translation releases during machine translation…”, “performing a subsequent machine translation using a different translator model”, and “outputting..for display within a user interface (UI)…”. These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not provide any meaningful limits on practicing the abstract idea. Accordingly, the claim is directed to an abstract idea (Step 2A: YES).
Claim 1 does not contain any additional limitations which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claim 1 is not patent eligible.
Regarding dependent claims 2-10, “The method” is recited, which is directed to one of the four statutory categories of invention (process) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite further mental processes in addition to those recited in independent claim 1 which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
Claim 2:
wherein the feedback mechanism comprises functionality for feedback to be provided to at least a portion of the translation release, wherein the feedback mechanism is selectively enabled based on the determined accuracy: the person obtains the translated text using feedback depending on the accuracy (e.g. re-translate/obtain different translation if translation is poor accuracy).
Claim 2 contains no additional limitations.
Claim 3:
receiving the feedback, wherein the feedback is used as a basis for continuing … translation of the character string: the person receives feedback to determine to continue translating the text.
Claim 3 contains the additional limitations of “continuing machine translation”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 4:
wherein the continued translation comprises at least one of modification of the translation release and re-translation of the character string: the person continues translation by modifying the text of the translation and re-translating the text (e.g. using a different model)
Claim 4 contains no additional limitations.
Claim 5:
identifying another translator model; and performing another … translation …: the person chooses a different model (e.g. one that might yield higher accuracy), and then re-translates the text using the different model.
Claim 5 contains the additional limitations of “another translator model” and “another machine translation based on the other translator model”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 6:
Claim 6 contains the additional limitation “wherein the feedback is provided by the device based on autonomous analysis of the translation release.”. This limitation is recited broadly, and amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 7:
wherein the feedback comprises user input corresponding to least a portion of the translation release: the person uses user input about the translation as feedback.
Claim 7 contains no additional limitations.
Claim 8:
wherein the language information comprises information selected from a group consisting of: a type of language, format of language, source of language, grammar, language pairs, syntax, phrases, punctuation, paragraphs, wild-cards, format of the text and/or character string, relationship to a target language and identity of a dictionary defining terms of the language: the person can identify language information such as type of language (e.g. English, French), format of language (e.g. what kind of alphabet, symbols), source of language (e.g. what kind of content item), grammar, language pairs, syntax, phrases, punctuation, paragraphs, wild-cards, format of the text and/or character string, relationship to a target language and identity of a dictionary defining terms of the language.
Claim 8 contains no additional limitations.
Claim 9:
wherein the translation release is identified during the performance of the … translation: the person determines the translation while performing the translation.
Claim 9 contains the additional limitations of “machine translation”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 10:
Claim 10 contains the additional limitation “wherein the translation release is an output of the translator model at a conclusion of an iteration of the execution of the translator model”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 2-10 do not contain any additional limitations which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are those discussed above, which amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not provide any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A: YES).
Claims 2-10 do not contain any additional limitations which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 2-10 are not patent eligible.
Regarding claim 11, “A system” is recited, which is directed to one of the four statutory categories of invention (machine) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite limitations similar to those found in claims 1-2, and thus also recites mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES) (see above analysis for independent claim 1 and dependent claim 2).
Claim 11 does not contain any additional limitations which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are those described regarding claim 1 (see above explanation for claim 1), and “A system comprising: one or more processors and one or more non-transitory computer readable media, the one or more non-transitory computer readable media comprising program instructions stored thereon that when executed cause the one or more computers to”. These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not provide any meaningful limits on practicing the abstract idea. Accordingly, the claim is directed to an abstract idea (Step 2A: YES).
Claim 11 does not contain any additional limitations which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claim 11 is not patent eligible.
Regarding dependent claims 12-15, “The system” is recited, which is directed to one of the four statutory categories of invention (machine) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite limitations similar to those recited in: claims 3 and 4 (Claim 12), claims 3 and 4 (Claim 13), claim 5 (Claim 14), and claims 7 and 9-10 (Claim 15), and thus also recite further mental processes which fall under the category of abstract idea (Step 2A Prong 1: YES) (see above analysis for claims 3-5, 7, and 9-10).
Claims 12-15 do not contain any additional limitations which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are those discussed above with regards to dependent claims 3-5, 7, and 9-10, which amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not provide any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A: YES).
Claims 12-15 do not contain any additional limitations which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 12-15 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
6. Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Pierard et al. (US 10,108,610 B1, hereinafter Pierard) in view of Arivazhagan et al. (US 2022/0092274 A1, hereinafter Arivazhagan) and further in view of Huang (US 2017/0169015 A1, hereinafter Huang).
Regarding claim 1, Pierard discloses A method comprising: receiving, by a device (Fig. 1, 106), a request to translate a character string (Col. 4 Lines 27-34 “For example, and without limitation, a user of the computing device 106 can request to view the content page 108 in a human-readable language other than the language in which the content items 110 are expressed. For instance, the content items 110 can be text expressed in the English language, and a user of the computing device 106 can request to view the content page 108 in Spanish or French.”; Col. 4 Lines 35-38 “Responsive to receiving a request 104 for the page 108, the front-end service 102 can simultaneously submit translation requests 116 for each of the content items 110 on the page 108 to multiple machine translation services 114.”), the character string comprising text (Col. 4 Lines 4-6 “The content items 110 can include text content or images. For example, and without limitation, the content items 110 might be text expressed in the English language.”;); analyzing, by the device, the character string, and determining, based on the analysis, language information associated with the text (Col. 9 Lines 30-40 “The machine translation service 114 to be utilized to translate a particular content item 110 can also be selected based upon an estimated difficulty of translation of a content item 110. For example, long spans of text in certain languages (e.g. Japanese or Chinese), can be very difficult to translate. Consequently, these types of content items 110 can be initially sent to the machine translation service 114A for translation and updated in the manner described above. Spans of text that are estimated to be easier to translate can be transmitted to the machine translation service 114C for immediate high quality translation.”); identifying, by the device, based on the language information, a translator model (Col. 9 Lines 30-40 “The machine translation service 114 to be utilized to translate a particular content item 110 can also be selected based upon an estimated difficulty of translation of a content item 110. For example, long spans of text in certain languages (e.g. Japanese or Chinese), can be very difficult to translate. Consequently, these types of content items 110 can be initially sent to the machine translation service 114A for translation and updated in the manner described above. Spans of text that are estimated to be easier to translate can be transmitted to the machine translation service 114C for immediate high quality translation.”); executing, by the device, the translator model to generate one or more translation releases during machine translation of the character string…(Col. 4 Lines 50-52 “As illustrated in FIG. 1, the machine translation services 114 are configured to generate machine translations of content items 110 at varying levels of expected quality.”); identifying, by the device, a translation release of the one or more translation releases comprising at least a portion of the text being translated from an original language to a target language (Col. 6 Lines 16-27 “It is to be appreciated that the application 120 can utilize various mechanisms to determine whether translated content items 118 are available from the front-end service 102. For example, and without limitation, a “pull” mechanism can be utilized where the application 120 periodically polls the front-end service 102 to determine whether new translated content items 118 are available. Alternately, a “push” mechanism can be utilized where the front-end service 102 can inform the application 120 that new translated content items 118 are available. The application 120 can then retrieve the translated content items 118 from the front-end service or another location.”); determining, by the device, an accuracy of the translation release (Col. 6 Lines 36-40 “In one configuration, the machine translation services 114 can also compute a quality estimate for the translations that they generate. For instance, in the example shown in FIG. 1, the machine translation service 114C has computed the quality estimate 124.”)…; and outputting, by the device, for display within a user interface (UI) (Fig. 1, 122), a translated output… (Col. 6 Lines 62-67 and Col. 7 Lines 1-3 “In some configurations, the request 104 specifies a threshold quality for translations that the computing device 106 can accept. In these configurations, the front-end service 102 can utilize the threshold and the quality estimates 124 to identify those translated content items 118 that are to be provided to the computing device 106. Translated content items 118 that do not have a quality estimate 124 in excess of the threshold will not be provided to the computing device 106.”; Col. 5 Lines 58-64 “When the front-end service 102 subsequently receives the translated content item 118B, the front-end service 102 can provide the translated content item 118B to the computing device 106. The application 120 can then replace the previously displayed translated content item 118A with the translated content item 118B in the UI 122 (i.e. when the UI 122 is a visual UI).”).
Pierard does not specifically disclose:
[executing, by the device, the translator model to generate one or more translation releases during machine translation of the character string] prior to completion of the translation;
monitoring, by the device, an output of the translation release during execution of the translator model.
Arivazhagan teaches [executing, by the device, the translator model to generate one or more translation releases during machine translation of the character string] prior to completion of the translation (para. 0042 “To enable long-form speech translation, the computer system 110 maintains state as time progresses. The state information includes a cumulative transcription 136 and a cumulative translation 145 of the audio data stream 108 received so far. As new bytes of audio data stream in, they are sent to the ASR module 130 which emits transcribed text in real-time as and when the confidence score indicates sufficiently high confidence. The emitted transcription results are appended to the continuously growing cumulative transcription 136. On every such update, the computer system 110 takes the last, potentially incomplete sentence from the cumulative transcription 136 and sends it to the MT module 140 for translation. The result from the MT module 140 is appended to the cumulative translation 145, replacing a previous translation of the same sentence if one was present. The computer system 140 then generates and sends output text for presentation, such as the last sentence from the cumulative translation 145, potentially masking one or more tokens at the end of the sentence.”; see Fig. 2, several translation releases (208) generated at different updates numbers 202 prior to a completion of a translation) and monitoring, by the device, an output of the translation release during execution of the translator model (para. 0083 “To measure and improve the system for long-form spoken language retranslation, an evaluation framework can be used to measure performance. For example, the quality, latency, and stability of the system's translated output can be measured. In general, evaluation can be done at the document level wherever possible. To better track performance, the computer system 110 can generate an event log. This can be an ordered list of events, where the i.sup.th event is a record that includes (1) s.sub.i the source text recognized so far, (2) o.sub.i the translated output text currently displayed, and (3) t.sub.i a time-stamp. Events are logged every time the source text (e.g., ASR result) or output (e.g., translation output) changes. Each event i records the entire session, or spoken document, up to time t.sub.i, allowing us to work and evaluate without providing our system gold-standard segmentations. ...”).
Pierard and Arivazhagan are considered to be analogous to the claimed invention as they both are in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard to incorporate the teachings of Arivazhagan in order to specifically generate one or more translation releases during machine translation of the character string prior to completion of the translation, and to monitor, by the device, an output of the translation release during execution of the translator model. Doing so would be beneficial, as this translation approach would exhibit low latency and high final quality of translation (para. 0033), and the monitoring evaluation framework would allow for improvement for long-form language translation.
Pierard in view of Arivazhagan does not specifically disclose:
[determining, by the device, an accuracy of the translation release,] wherein a feedback mechanism is selectively enabled for the translation release based on the determined accuracy; continuing, based at least in part on the feedback mechanism, translation of the character string by at least one of modifying the translation release or performing a subsequent machine translation using a different translator model; [and outputting, by the device, for display within a user interface (UI), a translated output] corresponding to the continued translation.
Huang teaches [determining, by the device, an accuracy of the translation release,] wherein a feedback mechanism is selectively enabled for the translation release based on the determined accuracy (Fig. 5, 518; para. 0029 “The extracted features can be provided to one of the confidence models 350, along with a representation of the translation or source, and the model 350 can produce a confidence score indicating a quality level of the translation.”; para. 0047 “At block 518, process 500 can determine whether the confidence score computed at block 514 or 516 is below a translation upgrade threshold. In some implementations, this translation upgrade threshold can be a score of 3.5 or less out of five. In some implementations, this translation upgrade threshold can be computed based on a comparison of translation precision scores to number of acceptable translations at a given score. This comparison is discussed in greater detail below in relation to FIG. 6. If the score is above the translation upgrade threshold process 500 can continue to block 522; and if the score is at or below the translation upgrade threshold, process 500 can continue to block 520.”); continuing, based at least in part on the feedback mechanism, translation of the character string by at least one of modifying the translation release or performing a subsequent machine translation using a different translator model (para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”); [and outputting, by the device, for display within a user interface (UI), a translated output] corresponding to the continued translation (Huang teaches generation of continued translation: para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”; Pierard discloses display of translated output (see above claim mapping)).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically have a feedback mechanism be selectively enabled for the translation release based on the determined accuracy, and to continue, based at least in part on the feedback mechanism, translation of the character string by at least one of modifying the translation release or performing a subsequent machine translation using a different translator model. Doing so would be beneficial, as doing so would provide a method for determining whether translations are acceptably accurate to be provided to users or whether alternate translations need to be generated (para. 0013), improving quality of the translations.
Regarding claim 2, Pierard in view of Arivazhagan and Huang discloses wherein the feedback mechanism comprises functionality for feedback to be provided to at least a portion of the translation release, wherein the feedback is selectively enabled based on the determined accuracy (Huang, Fig. 5, 518; para. 0029 “The extracted features can be provided to one of the confidence models 350, along with a representation of the translation or source, and the model 350 can produce a confidence score indicating a quality level of the translation.”; para. 0047 “At block 518, process 500 can determine whether the confidence score computed at block 514 or 516 is below a translation upgrade threshold. In some implementations, this translation upgrade threshold can be a score of 3.5 or less out of five. In some implementations, this translation upgrade threshold can be computed based on a comparison of translation precision scores to number of acceptable translations at a given score. This comparison is discussed in greater detail below in relation to FIG. 6. If the score is above the translation upgrade threshold process 500 can continue to block 522; and if the score is at or below the translation upgrade threshold, process 500 can continue to block 520.”; para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically have a feedback mechanism be selectively enabled for the translation release based on the determined accuracy. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 3, Pierard in view of Arivazhagan and Huang discloses receiving the feedback, wherein the feedback is used as a basis for continuing translation of the character string (Huang, Fig. 5, 518; para. 0029 “The extracted features can be provided to one of the confidence models 350, along with a representation of the translation or source, and the model 350 can produce a confidence score indicating a quality level of the translation.”; para. 0047 “At block 518, process 500 can determine whether the confidence score computed at block 514 or 516 is below a translation upgrade threshold. In some implementations, this translation upgrade threshold can be a score of 3.5 or less out of five. In some implementations, this translation upgrade threshold can be computed based on a comparison of translation precision scores to number of acceptable translations at a given score. This comparison is discussed in greater detail below in relation to FIG. 6. If the score is above the translation upgrade threshold process 500 can continue to block 522; and if the score is at or below the translation upgrade threshold, process 500 can continue to block 520.”; para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically receive the feedback, wherein the feedback is used as a basis for continuing translation of the character string. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 4, Pierard in view of Arivazhagan and Huang discloses wherein the continued translation comprises at least one modification of the translation release and re-translation of the character string (Huang, para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically have the continued translation be at least one of a modification of the translation release and re-translation of the character string. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 5, Pierard in view of Arivazhagan and Huang discloses identifying another translator model (para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”); and performing another machine translation based on the other translator model (para. 0047 “At block 520, process 500 can upgrade the translation of the content item. In various implementations, upgrading the translation of the content item can include using a more sophisticated machine translation engine or providing the content item to a human to manually translate the content item.”).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically identify another translator model and perform the subsequent machine translation based on the other translator model. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 6, Pierard in view of Arivazhagan and Huang discloses wherein the feedback is provided by the device based on autonomous analysis of the translation release (Huang, Fig. 5, 518, feedback autonomously analyzes translation for accuracy/quality; para. 0029 “The extracted features can be provided to one of the confidence models 350, along with a representation of the translation or source, and the model 350 can produce a confidence score indicating a quality level of the translation.”; para. 0047 “At block 518, process 500 can determine whether the confidence score computed at block 514 or 516 is below a translation upgrade threshold. In some implementations, this translation upgrade threshold can be a score of 3.5 or less out of five. In some implementations, this translation upgrade threshold can be computed based on a comparison of translation precision scores to number of acceptable translations at a given score. This comparison is discussed in greater detail below in relation to FIG. 6. If the score is above the translation upgrade threshold process 500 can continue to block 522; and if the score is at or below the translation upgrade threshold, process 500 can continue to block 520.”).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pierard in view of Arivazhagan to incorporate the teachings of Huang in order to specifically have the feedback be provided by the device based on autonomous analysis of the translation release. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 7, Pierard in view of Arivazhagan and Huang discloses wherein the feedback comprises user input corresponding at least a portion of the translation release (Pierard, Col. 9 Lines 15-23 “The machine translation service 114 to be utilized to translate a particular content item 110 can also be selected based upon preferences specified by the user of the application 120. For example, the user might indicate that they are willing to wait for higher quality translations and that, therefore, content items 110 are to be transmitted to the machine translation service 114C for translation. The order of translation of the content items 110 can also be selected based upon user preferences.”; Col. 13 Lines 23-34 “For example, and without limitation, a user's interaction (e.g. mouse cursor movement, keyboard input, etc.) with the UI 220 can be monitored and content items 110 for preemptive machine translation can be selected based upon the interaction. For example, if a user hovers the mouse cursor over a hyperlink to a page 108, the content items 110 on that page might be preemptively machine translated in the manner described above. As another example, if the user scrolls a page 108 to the bottom of the UI 122, content items 110 in pages 108 located at the bottom of the page 108 can be preemptively machine translated in the manner described above.”).
Regarding claim 8, Pierard in view of Arivazhagan and Huang discloses wherein the language information comprises information selected from a group consisting of: a type of language, format of language source of language, grammar, language pairs, syntax, phrases, punctuation, paragraphs, wild-cards, format of the text and/or character string, relationship to a target language and identity of a dictionary defining terms of the language (Pierard, Col. 9 Lines 30-40 “The machine translation service 114 to be utilized to translate a particular content item 110 can also be selected based upon an estimated difficulty of translation of a content item 110. For example, long spans of text in certain languages (e.g. Japanese or Chinese), can be very difficult to translate. Consequently, these types of content items 110 can be initially sent to the machine translation service 114A for translation and updated in the manner described above. Spans of text that are estimated to be easier to translate can be transmitted to the machine translation service 114C for immediate high quality translation.”).
Regarding claim 9, Pierard in view of Arivazhagan and Huang discloses wherein the translation release is identified during the performance of the machine translation (Arivazhagan, para. 0042 “To enable long-form speech translation, the computer system 110 maintains state as time progresses. The state information includes a cumulative transcription 136 and a cumulative translation 145 of the audio data stream 108 received so far. As new bytes of audio data stream in, they are sent to the ASR module 130 which emits transcribed text in real-time as and when the confidence score indicates sufficiently high confidence. The emitted transcription results are appended to the continuously growing cumulative transcription 136. On every such update, the computer system 110 takes the last, potentially incomplete sentence from the cumulative transcription 136 and sends it to the MT module 140 for translation. The result from the MT module 140 is appended to the cumulative translation 145, replacing a previous translation of the same sentence if one was present. The computer system 140 then generates and sends output text for presentation, such as the last sentence from the cumulative translation 145, potentially masking one or more tokens at the end of the sentence.”; see Fig. 2, several translation releases (208) identified at different updates numbers 202 prior to a completion of a translation).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Arivazhagan in order to specifically have the translation release be identified during the performance of the machine translation. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 10, Pierard discloses wherein the translation release is an output of the translator model at a conclusion of an iteration of the execution of the translator model (Arivazhagan, para. 0042 “To enable long-form speech translation, the computer system 110 maintains state as time progresses. The state information includes a cumulative transcription 136 and a cumulative translation 145 of the audio data stream 108 received so far. As new bytes of audio data stream in, they are sent to the ASR module 130 which emits transcribed text in real-time as and when the confidence score indicates sufficiently high confidence. The emitted transcription results are appended to the continuously growing cumulative transcription 136. On every such update, the computer system 110 takes the last, potentially incomplete sentence from the cumulative transcription 136 and sends it to the MT module 140 for translation. The result from the MT module 140 is appended to the cumulative translation 145, replacing a previous translation of the same sentence if one was present. The computer system 140 then generates and sends output text for presentation, such as the last sentence from the cumulative translation 145, potentially masking one or more tokens at the end of the sentence.”; see Fig. 2, several translation releases (208) identified at different updates numbers 202 prior to a completion of a translation, each of these translations is an output of the model at a particular iteration/update).
Pierard, Arivazhagan, and Huang are considered to be analogous to the claimed invention as they are all in the same field of machine translation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Arivazhagan in order to specifically have the translation release be an output of the translator model at a conclusion of an iteration of the execution of the translator model. Doing so would be beneficial, given the same rationale as for claim 1.
Regarding claim 11, claim 11 is a system claim with limitations similar to method claims 1-2, and is thus rejected under similar rationale.
Furthermore, Pierard discloses A system (Fig. 12) comprising: one or more processors (Fig. 12, 1204) and one or more non-transitory computer readable media (Col. 19 Lines 49-56 “In addition to the mass storage device 1218 described above, the computer 1200 can have access to other computer-readable storage media to store and retrieve information, such as program modules, data structures, or other data. It should be appreciated by those skilled in the art that computer-readable storage media is any available media that provides for the non-transitory storage of data and that can be accessed by the computer 1200.”), the one or more non-transitory computer readable media comprising program instructions stored thereon that when executed cause the one or more computers to (Col. 20 Lines 17-30 “In one configuration, the mass storage device 1218 or other computer-readable storage media is encoded with computer-executable instructions which, when loaded into the computer 1200, transform the computer from a general-purpose computing system into a special-purpose computer capable of implementing the configurations described herein. …According to one configuration, the computer 1200 has access to computer-readable storage media storing computer-executable instructions which, when executed by the computer 1200, perform the various processes described above with regard to FIGS. 1-8.”).
Regarding claim 12, claim 12 contains limitations similar to claims 3-4, and thus is rejected under similar rationale.
Regarding claim 13, claim 13 contains limitations similar to claims 3-4, and thus is rejected under similar rationale.
Regarding claim 14, claim 14 contains limitations similar to claim 5, and thus is rejected under similar rationale.
Regarding claim 15, claim 15 contains limitations similar to claims 7 and 9-10, and thus is rejected under similar rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CODY DOUGLAS HUTCHESON/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
