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 .
Status of Claims
The office action is in response to arguments and amendments entered on September 4, 2025 for the patent application 17/888,144 originally filled on August 15, 2022. Claims 21-32 and 34-40 are pending. Claims 21-32, 34, and 35 are amended. Claim 33 is canceled. The first office action of September 4, 2025 is fully incorporated by reference into this final action.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-27, 28-34, and 35-40 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 21 is directed to a “non-transitory machine-readable storage medium”, claim 28 is directed to a “non-transitory machine-readable storage medium”, and claim 35 is directed to an “apparatus” (i.e., a machine), hence these claims are directed to one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes” for claims 21, 28, and 35.
The independent claims recite the following limitations:
Per Claim 21:
“A non-transitory machine-readable storage medium comprising machine-readable instructions that cause at least one programmable circuit to at least: execute a first machine learning model to detect an input in a first language, the input received during a communication session between a first user and a second user; responsive to the detection, execute a second machine-learning model to identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language; cause, based on a user setting, the input to be translated to the second language, the user setting identifying the second language, the second language different than the first language; and cause an electronic device to present an output associated with the translated input and the contextual parameter.”
Per Claim 28:
“A non-transitory machine-readable storage medium comprising machine-readable instructions that cause at least one programmable circuit to at least: execute a first machine learning model to detect an audio input, the audio input associated with a conversation between a first user and a second user, the audio input in a first language; and after the detection, execute a second machine-learning model to identify a contextual parameter associated with the conversation between the first user and the second user, the contextual parameter different than the first language and a second language based on a user setting and the contextual parameter, generate an output including information associated with the audio input, the information in the second language, the second language different than the first language; and cause an electronic device to present the output.”
Per Claim 35: “An apparatus comprising: memory; machine-readable instructions; and at least one programmable circuit to be programmed by the machine-readable instructions to: execute one or more Artificial Intelligence models to: detect an input in a first language, the input received during a communication session between a first user and a second user; identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language and generate, based on a user setting and the contextual parameter, a translation of the input in the second language, the user setting identifying the second language, the second language different than the first language; and cause the translation to be output for presentation.”
The non-highlighted sections of the above limitations, as drafted, define a process, that under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components. That is, other than the recitation of “non-transitory storage medium”, ”programmable circuit”, “machine learning model”, “electronic device” and “memory” nothing in the above limitations precludes the step from practically being performed between people or in the human mind. For example, but for the recited language, the limitations above encompass a translator listening to a communication between two users in a first language, observing a contextual parameter in the conversation outside of the words spoken (i.e. the age, gender, or social status of the conversing users), and outputting the communication in a second language alongside the observed contextual parameter.
If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion), then it falls within the “mental processes” grouping of abstract ideas. Hence, the limitations of independent claims 1, 28, and 35 are drawn to an abstract idea of “translating an input of a first language to a second language” which falls within the “mental processes” grouping of abstract ideas in terms of concepts performed in the human mind (including an observation, evaluation, judgment, opinion), as per MPEP 2106.04(a)(2) III. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the Applicant’s claimed elements of a “non-transitory storage medium”, ”programmable circuit”, “machine learning model”, “electronic device” and “memory” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed abstract idea, translating an input of a first language to an output of a second language, is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Furthermore, the claimed “non-transitory storage medium” (described on page 17, lines 9-19), ”programmable circuit” (described on page 16, lines 21-29), “machine learning model” (described on page 7, lines 9-24), “electronic device” (described on page 14, lines 25-30, and page 16, lines 1-2), and “memory” (described on page 17, lines 9-19) are reasonably interpreted as generic hardware and provide no details of anything beyond its use as ubiquitous standard equipment. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
Claims 22-27 are dependent from independent claim 21, claims 29-32 and 34 are dependent from independent claim 28, and claims 36-40 are dependent from claim 35. The dependent claims 22-27, 29-34, and 36-40 include all the limitations of their respective independent claims. Therefore, the dependent claims recite the same abstract idea. The limitation of the dependent claims fails to amount to significantly more than the judicial exception. For Example:
The limitations of claims 25-26, 29, and 38-39 clarify additional types of data input into the system pertaining to user settings. As such, these claims merely further recite the type of data included in the method and are therefore insignificant extra-solution activity. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 22-24, 30-32, and 26-37 clarify the contents and format of information output to the user. As such, this claim merely displays data related to the performance of the abstract idea and further recite the types of data displayed. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 27 and 34 further denote the utilization of a phone or personal computer (described on page 15, lines 30-31, and page 16, lines 1-11). These limitations generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). Furthermore, these claimed elements are reasonably interpreted as generic hardware and provide no details of anything beyond its use as ubiquitous standard equipment. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Independent claims 21, 28, and 35 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. Additionally, dependent claims 22-27, 29-32, 34, and 36-40 recite an abstract idea without significantly more and are not drawn to eligible subject matter. Therefore, claims 21-32 and 34-40 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
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.
Claims 21-22, 24, and 27-37 are rejected under 35 U.S.C. 103) as being anticipated over Nelson et al. (Document ID US 20180101823 A1; 2018-04-12) in view of Wang et al. (Document ID US 20180314689 A1; 2018-11-01).
Regarding claim 21, Nelson et al. teaches:
A non-transitory machine-readable storage medium comprising machine-readable instructions that cause at least one programmable circuit to at least: execute a first machine learning model (Para. [0075], shows that the meeting intelligence apparatus may be implemented using IBM Watson, i.e. an artificial intelligence service that utilizes machine learning) to detect an input in a first language, the input received during a communication session between a first user and a second user (Para. [0190], shows that an input of first meeting data content that is gathered during a conversation); cause, based on a user setting, the input to be translated to a second language (Para. [0190], shows that the system may take an input of first meeting content data 302 to generate an output of second meeting data 312, which includes a translation of first meeting content data 302 into a different language), the user setting identifying the second language (Para. [0190], shows that users at each participating node may specify a language for their node, which influences what language is output to them), the second language different than the first language; and cause an electronic device to present an output associated with the translated input (Para. [0187], shows that the electronic meeting content is displayed by an electronic meeting application; Para. [0084], specifies that the application may be run on a smartphone, i.e. an electronic device) and the contextual parameter (Para. [0217] -[0224], and fig. 7c, shows that come contextual things such as a participant’s sentiment and role may be displayed to the user).
Nelson et al. fails to explicitly teach:
responsive to the detection, execute a second machine-learning model to identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language.
Wang et al. teaches:
responsive to the detection, execute a second machine-learning model to identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language (Para. [0234]-[0236] and [0265] shows that several contextual parameters regarding users, other than the language being spoken, can be identified, such as age, gender, socioeconomic status, and emotion, and further shows that various classifiers may be derived using machine learning techniques).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Wang et al., regarding a virtual assistant’s detection of a user’s contextual features during a conversation, to the similar device of Nelson et al., a method of managing electronic meetings that utilizes the assistance of artificial intelligence, to yield the predictable result of providing more information to the participants of an online meeting. One of ordinary skill in the art would be motived to incorporate the known technique of Wang et al. with the similar device of Nelson et al. as the interpretation of various contextual features such as a participants age, gender, socioeconomic status, and emotion would provide the meeting’s participants with context that may better facilitate communication between said users.
Regarding claim 22, Nelson et al. teaches:
The machine-readable storage medium of claim 21, wherein the output is a visual output (Para. [0190], shows that the output may take the form of subtitles, i.e. a visual output).
Regarding claim 24, Nelson et al. teaches:
The machine-readable storage medium of claim 21, wherein the output is an audio output (Para. [0190], shows that the output may take the form of a dub, i.e. an audio output).
Regarding claim 27, Nelson et al. teaches:
The machine-readable storage medium of claim 21, wherein the electronic device is at least one of a smartphone or a personal compute device (Para. [0084], specifies that the application may be run on a smartphone, i.e. an electronic device).
Regarding claim 28, Nelson et al. teaches:
A machine-readable storage medium comprising machine-readable instructions that cause at least one programmable circuit to at least: execute a first machine learning model to (Para. [0075], shows that the meeting intelligence apparatus may be implemented using IBM Watson, i.e. an artificial intelligence service that utilizes machine learning) detect an audio input, the audio input associated with a conversation between a first user and a second user (Para. [0190], shows that an input of first meeting data content 302 that is gathered during a conversation; Para. [0149], shows that the first meeting data 302 is derived from audio/visual data, i.e. containing audio input; Para. [0189], shows that voice data, i.e. audio input, may be analyzed by machine intelligence apparatus 102), the audio input in a first language; and based on a user setting, generate an output including information associated with the audio input (Para. [0190], shows that the system may take an input of first meeting content data 302 to generate an output of second meeting data 312, which includes a translation of first meeting content data 302 into a different language), the information in the second language, the second language different than the first language; and cause an electronic device to present the output (Para. [0187], shows that the electronic meeting content is displayed by an electronic meeting application; Para. [0084], specifies that the application may be run on a smartphone, i.e. an electronic device).
Nelson et al. fails to explicitly teach:
after the detection, execute a second machine-learning model to identify a contextual parameter associated with the conversation between the first user and the second user, the contextual parameter different than the first language and a second language
Wang et al. teaches:
after the detection, execute a second machine-learning model to identify a contextual parameter associated with the conversation between the first user and the second user, the contextual parameter different than the first language and a second language (Para. [0234]-[0236] and [0265] shows that several contextual parameters regarding users, other than the language being spoken, can be identified, such as age, gender, socioeconomic status, and emotion, and further shows that various classifiers may be derived using machine learning techniques).
Regarding claim 29, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 28, wherein the user setting identifies the second language (Para. [0190], shows that users at each participating node may specify a preferred language, i.e. a user setting, for their node, which determines what language is output to them).
Regarding claim 30, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 28, wherein the output is an audio output (Para. [0190], shows that the output may take the form of a dub, i.e. an audio output).
Regarding claim 31, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 28, wherein the output is a visual output (Para. [0190], shows that the output may take the form of subtitles, i.e. a visual output).
Regarding claim 32, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 31, wherein the output includes text (Para. [0190], shows that the output may take the form of subtitles, i.e. a textual output).
Regarding claim 34, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 28, wherein the electronic device includes at least one of a smartphone or a personal compute device (Para. [0084], specifies that the application may be run on a smartphone, i.e. an electronic device).
Regarding claim 35, Nelson et al. teaches:
An apparatus comprising: memory; machine-readable instructions; and at least one programmable circuit to be programmed by the machine-readable instructions to: execute one or more Artificial Intelligence models to (Para. [0075], shows that the meeting intelligence apparatus may be implemented using IBM Watson, i.e. an artificial intelligence service that utilizes machine learning): detect an input in a first language, the input received during a communication session between a first user and a second user (Para. [0190], shows that an input of first meeting data content that is gathered during a conversation); generate, based on a user setting, a translation of the input in a second language (Para. [0190], shows that the system may take an input of first meeting content data 302 to generate an output of second meeting data 312, which includes a translation of first meeting content data 302 into a different language), the user setting identifying the second language (Para. [0190], shows that users at each participating node may specify a language for their node, which influences what language is output to them), the second language different than the first language; and cause the translation to be output for presentation (Para. [0187], shows that the electronic meeting content is displayed by an electronic meeting application).
Nelson et al. fails to explicitly teach:
identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language and generate, based on a user setting and the contextual parameter, a translation of the input in the second language.
Wang et al. teaches:
identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language and generate (Para. [0234]-[0236] and [0265] shows that several contextual parameters regarding users, other than the language being spoken, can be identified, such as age, gender, socioeconomic status, and emotion, and further shows that various classifiers may be derived using machine learning techniques), based on a user setting and the contextual parameter, a translation of the input in the second language (Para. [0200], shows that the system may be able to identify and output a textual output of both spoken words and the emotional content from an audio input; Para. [0378] and para. [0283]-[0285], shows that the translation engine may utilize statistical models when translating the text and that statistical model may utilize classifiers such as user age, gender, and socioeconomic status).
Regarding claim 36, Nelson et al. teaches:
The apparatus of claim 35, wherein one or more of the at least one programmable circuit is to cause the translation to be output in a visual format (Para. [0190], shows that the output may take the form of subtitles, i.e. a visual format).
Regarding claim 37, Nelson et al. teaches:
The apparatus of claim 35, wherein one or more of the at least one programmable circuit is to cause the translation to be output in an audio format (Para. [0190], shows that the output may take the form of a dub, i.e. an audio format).
Claims 23 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson et al. (Document ID US 20180101823 A1; 2018-04-12) in view of Wang et al. (Document ID US 20180314689 A1; 2018-11-01) and in further view of Gliessner et al. (Document ID US 20040152055 A1; 2004-08-05).
Regarding claim 23, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 22, wherein the machine-readable instructions are to cause one or more of the at least one programmable circuit to present the output as overlaying content (Para. [0190], shows that the output may take the form of subtitles that are added, i.e. overlaying, on to first meeting content data 302).
Nelson et al. does not explicitly teach:
The non-transitory machine-readable storage medium of claim 22, wherein the machine-readable instructions are to cause one or more of the at least one programmable circuit to present the output as overlaying content.
Gliessner et al. teaches:
The non-transitory machine-readable storage medium of claim 22, wherein the machine-readable instructions are to cause one or more of the at least one programmable circuit to present the output as overlaying content (Fig. 3, para. [0026], and para. [0031], show the overlaying of captions, i.e. subtitles, onto a video).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Gliessner et al., regarding the overlaying of caption to audio/video content, to the similar device of Nelson et al., a method of managing electronic meetings that outputs audio/video content, to yield the predictable result of overlaying captions onto the audio video content of Nelson et al. One of ordinary skill in the art would be motived to incorporate the known technique of Gliessner et al. with the similar device of Nelson et al. as the overlaying of captions would ensure that a portion of the screen is not dedicated to either subtitles or text alone, thus making a cleaner user interface.
Regarding claim 40, Nelson et al. teaches:
The apparatus of claim 35, wherein one or more of the at least one programmable circuit is to present the output as overlaying content (Para. [0190], shows that the output may take the form of subtitles that are added on to first meeting content data 302).
Nelson et al. does not explicitly teach:
The apparatus of claim 35, wherein one or more of the at least one programmable circuit is to present the output as overlaying content.
Gliessner et al. teaches:
The apparatus of claim 35, wherein one or more of the at least one programmable circuit is to present the output as overlaying content (Fig. 3, para. [0026], and para. [0031], show the overlaying of captions, i.e. subtitles, onto a video).
Claims 25, 26, 38, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson et al. (Document ID US 20180101823 A1; 2018-04-12) in view of Wang et al. (Document ID US 20180314689 A1; 2018-11-01) and in further view of Fang et al. (Document ID US 20150057994 A1; 2015-02-26).
Regarding claim 25, Nelson et al. teaches:
The non-transitory machine-readable storage medium of claim 21, wherein the user setting is a first user setting and the machine-readable instructions are to cause one or more of the at least one programmable circuit to translate the input based on a second user setting (Abstract, para. [0007], para. [0070], para. [0085]-[0086], para. [0091], show that the user can specify rules, requirements, and constrains for various aspects of the meetings; Para. [0190], shows that for translations that users can specify a preferred language, i.e. the first user setting, to select what language inputs should be translated to, conversely the preferred or detected language of the other participants, i.e. a second user setting, dictates what the language is being translated from).
Fang et al. additionally teaches:
The non-transitory machine-readable storage medium of claim 21, wherein the user setting is a first user setting and the machine-readable instructions are to cause one or more of the at least one programmable circuit to translate the input based on a second user setting (Para. [0200]-[0204], show the utilization of a profanity filter, i.e. a language restriction, in the translation engine; Para. [0203], shows that different user groups may be given a different profanity library, i.e. a setting associated with different users or a user setting).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Fang et al., regarding the usage of profanity filters when translating an input, to the similar device of Nelson et al., a method of managing electronic meetings that may translate imputed content, to yield the predictable result of adding the ability for users to toggle a profanity future. One of ordinary skill in the art would be motived to incorporate the known technique of Fang et al. with the similar device of Nelson et al. as the utilization of a profanity filter option would allow electronic meeting hosts to ensure a professional environment in meeting types that would demand it.
Regarding claim 26, Nelson et al teaches:
Abstract, para. [0007], para. [0070], para. [0085]-[0086], para. [0091], show that the user can specify rules, requirements, and constrains for various aspects of the meetings. Para. [0190], shows that for translations that users can specify a preferred language, i.e. the first user setting, to select what language inputs should be translated to.
Nelson et al. does not explicitly teach:
The non-transitory machine-readable storage medium of claim 21, wherein the user setting includes a language restriction.
Fang et al. teaches:
The non-transitory machine-readable storage medium of claim 21, wherein the user setting includes a language restriction (Para. [0200]-[0204], show the utilization of a profanity filter, i.e. a language restriction, in the translation engine; Para. [0203], shows that different user groups may be given a different profanity library, i.e. a setting associated with different users or a user setting).
Regarding claim 38, Nelson et al. teaches
The apparatus of claim 35, wherein the user setting is a first user setting and one or more of the at least one programmable circuit is to generate the translation based on a second user setting (Abstract, para. [0007], para. [0070], para. [0085]-[0086], para. [0091], show that the user can specify rules, requirements, and constrains for various aspects of the meetings; Para. [0190], shows that for translations that users can specify a preferred language, i.e. the first user setting, to select what language inputs should be translated to, conversely the preferred or detected language of the other participants, i.e. a second user setting, dictates what the language is being translated from).
Fang et al. additionally teaches:
The apparatus of claim 35, wherein the user setting is a first user setting and one or more of the at least one programmable circuit is to generate the translation based on a second user setting (Para. [0200]-[0204], show the utilization of a profanity filter, i.e. a language restriction, in the translation engine; Para. [0203], shows that different user groups may be given a different profanity library, i.e. a setting associated with different users or a user setting).
Regarding claim 39, Nelson et al. teaches
Abstract, para. [0007], para. [0070], para. [0085]-[0086], para. [0091], show that the user can specify rules, requirements, and constrains for various aspects of the meetings. Para. [0190], shows that for translations that users can specify a preferred language, i.e. the first user setting, to select what language inputs should be translated to.
Nelson et al. does not explicitly teach:
The apparatus of claim 35, wherein the user setting includes a language restriction.
Fang et al. teaches:
The apparatus of claim 35, wherein the user setting includes a language restriction (Para. [0200]-[0204], show the utilization of a profanity filter, i.e. a language restriction, in the translation engine; Para. [0203], shows that different user groups may be given a different profanity library, i.e. a setting associated with different users or a user setting).
Summary
No claim is allowed.
Claim 1-20 and 33 are cancelled.
Claims 21-32 and 34-40 are rejected under 35 USC § 101
Claims 21-32 and 34-40 are rejected under 35 USC § 103
Response to Arguments
The Applicants arguments filed on December 24, 2025 related to claims 21-32 and 34-40 are fully considered, but are not fully persuasive.
Rejections under 35 USC § 101
Applicant amended claims 21 and 28 to be directed to statutory subject matter, i.e. non-transitory machine readable storage mediums.
Examiner has removed this portion of the rejection under 35 USC § 101, however maintains a rejection under 35 USC § 101 as claims 21-32 and 34-40 are directed to an abstract idea without significantly more.
Applicant respectfully argues the following: “The human mind cannot execute a machine-learning model, nor can a human execute a machine-learning model using pen and paper”, “The claims are not directed to an abstract mental process that can be performed within the human mind”, and “The allegations of the Office action are clearly in factual error and untethered from the actual claim language”.
Examiner respectfully disagrees. As per MPEP 2106.04(a)(2) III C, “claims can recite a mental process even if they are claimed as being performed on a computer”. While examiner agrees that a human mind cannot “execute a machine learning-model using pen and paper”, the execution of a machine learning model is not the abstract idea. The execution of the machine learning model is representative of the generic computer performing the abstract idea. The abstract idea being represented by the following sections of claim language: “detect an input in a first language, the input received during a communication session between a first user and a second user”, “identify a contextual parameter associated with communication between the first user and the second user, the contextual parameter different than the first language and a second language”, “cause, based on a user setting, the input to be translated to the second language”, and “present an output associated with the translated input”. That is, other than the recitation of “non-transitory storage medium”, ”programmable circuit”, “machine learning model”, “electronic device” and “memory” nothing in the limitations precludes the step from practically being performed between people or in the human mind. For example, but for the recited language, the limitations encompass a translator listening to a communication between two users in a first language, observing a contextual parameter in the conversation outside of the words spoken (i.e. the age, gender, or social status of the conversing users), and outputting the communication in a second language alongside the observed contextual parameter.
Applicant respectfully argues that like Director Squire’s explanation in Desjardin, where he explained that the court in Enfish articulated that “the claims are nor simply directed to any form of storing tabular data, but instead are specifically directed to a self-referential table for a computer database,” Enfish, 822 F.3d at 1336, the subject matter of claim 21 is not simply directed to any manner of gathering and analyzing data, but provides translation of an input received during a communication session between a first user and a second user and presentation of output in a specific matter.
Examiner respectfully disagrees. The “self-referential table recited in Enfish is a specific type of data structure designed to improve the wat a computer store and retrieves data in memory” (pg. 18, para. 2) and the “the specification also teaches that the self-referential table functions differently than conventional database structures” (pg. 15, para. 2). Applicant’s claims do reflect an example of data gathering (i.e., “detect an input in a first language”), analysis (i.e., “identify a contextual parameter” and “cause, based on a user setting, the input to be translated to the second language”), and output (i.e., “cause an electronic device to present an output associated with the translated input”). The means of which this is accomplished (i.e., utilization of a “first machine learning model”, a “second machine learning model”, and an “electronic device”) are described in the limitations in a non-specific and generic manner. When looking at applicant specification, machine learning models may consists of neural networks such as CNNs, RNNs, etc. (Page 7:9-24), i.e. a generic machine learning model; and the electronic device may consist of a “smartphone, wearable computer, portable computing device, etc. (Page 14:25-30 and Page 16:1-2), i.e. a generic computer.
Applicant respectfully argues that the allegations that the claims recite "collecting information, analyzing it, and displaying certain results of the collection analysis," are themselves an abstraction and are not found in the actual language of the claim, and that the Office action ignores Director Squires caution against the dangers of "essentially equating any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation".
Examiner respectfully disagrees. Applicant’s claims do reflect an example of data gathering (i.e., “detect an input in a first language”), analysis (i.e., “identify a contextual parameter” and “cause, based on a user setting, the input to be translated to the second language”), and output (i.e., “cause an electronic device to present an output associated with the translated input”). However Examiner argues that the claims as amended do not as cleanly fit into this descriptor, view the updated reject under 35 USC § 101 above. Furthermore, Examiner agrees that machine learning does not automatically equate to an unpatentable algorithm. However, the limitations as presented in the independent claims do not go into any further detail as to the content of the machine learning algorithms other then simply denoting what they are utilized to do. As such, Applicant’s claims are merely using computer as a tool to perform an abstract idea per MPEP §2106.05(f) and generally link the use of a judicial exception to (1) a particular technological environment or (2) field of use per MPEP §2106.05(h). In contrast, the claim language of, for example, independent claim 1 of Desjardins defines a method from training a machine learning model and goes into detail as to how the machine learning algorithms are trained, as opposed to simply stating that a machine training model is used to “compute, based on the first values of the plurality of parameters… an approximation of a posterior distribution over possible values of the plurality of parameters”.
For the reasons above, Examiner respectfully maintains the prior rejections under 35 USC § 101.
Rejections under 35 USC § 102 and 35 USC § 103
Applicant respectfully argues that the amended independent claims (21, 28, and 35) are not taught by Nelson et al.
Examiner agrees that the independent claims are not anticipated by Nelson et al. alone, and the rejections under 35 USC § 102 are withdrawn. However, in light of a new search, which was necessitated by the Applicant’s amendments, the independent claims are rejected under 35 USC § 103, see said rejections above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/A.J.B./Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715