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 .
Applicant’s amendments dated 7/14/26 are hereby entered.
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 1, 9-11, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 9-11, and 19-20 are directed to an abstract idea without significantly more. The claims recite a mental process that can be performed by human being and/or recite a method of organizing human activity and/or training/employing a machine learning model in a particular technological environment.
In regard to Claims 1 and 11, the following limitations can be performed as a mental process by a human being in terms of claiming collecting data, analyzing that data, and providing outputs based on that analysis which has been held by the CAFC to be an abstract idea in decisions such as, e.g., Electric Power Group, University of Florida Research Foundation, and Yousician v Ubisoft (non-precedential); and/or recite a method of organizing human activity in terms of claiming the teaching/training/evaluation of a human subject’s which has been identified by MPEP 2106.04(a)(2)(II) as being a method of organizing human activity, in terms of the Applicant claiming:
[a] method of generating a user attribute score, the method comprising:
receiving a discussion topic; and
generating a first prompt as a function of the discussion topic by:
training [a first algorithm] on a training dataset, wherein the training dataset correlates example discussion topics with example prompts;
inputting the discussion topic into the prompt generation [algorithm];
receiving, as an output, from the prompt generation [algorithm], the first prompt; receiving feedback in a form of a cost function, wherein the feedback is used to train the prompt generation [algorithm]];
wherein the prompt generation [algorithm] is configured to output prompts similar to the first prompt as a function of low-cost function values; and
wherein the prompt generation [algorithm] is configured to output prompts dissimilar to the first prompt as a function of high- cost function values;
presenting to a first user a first prompt […];
receiving from the first user device a first discussion datum, wherein the first discussion datum comprises audio data captured during a synchronous discussion including speech of the first user and speech of at least one additional user;
generating […] first-user attributed text from the audio data by:
transcribing the audio data into a plurality of transcribed statements;
attributing each transcribed statement of the plurality of transcribed statements to a corresponding speaker of a plurality of users participating in the synchronous discussion; and
selecting, from the plurality of transcribed statements, first-user transcribed statements attributed to the first user;
generating a first user attribute score as a function of the first discussion datum by:
training an attribute generation [algorithm] on a training dataset including example discussion data associated with example user attribute scores;
generating an accuracy score for the attribute generation [algorithm], wherein the accuracy score indicates a degree of retraining needed for the attribute generation [algorithm];
comparing one or more new training examples of the accuracy score to a predetermined threshold;
triggering retraining when the predetermined threshold is exceeded:
retraining the attribute generation [algorithm] as a function of the accuracy score until the accuracy score meets a predetermined threshold;
generating a certainty score for the first user as a function of the first-user transcribed statements, wherein the certainty score indicates a degree of confidence that the first-user transcribed statements contain sufficient data for generating the first user attribute score;
determining that the first-user transcribed statements contain insufficient data for the first user when the certainty score fails to satisfy a certainty score threshold;
in response to determining that the first-user transcribed statements contain insufficient data for the first user;
generating a context datum indicating that the first user has generated insufficient data to be evaluated;
inputting the discussion topic and the context datum into the prompt generation [algorithm];
receiving, as an output from the prompt generation [algorithm], a second prompt directed to the first user;
transmitting to the first user […], during the synchronous discussion and before the first user attribute score is output, a second signal […] to display the second prompt;
receiving from the first user […] an additional discussion datum comprising an additional audio response by the first user to the second prompt;
transcribing the additional audio response into one or more additional transcribed statements; and
attributing the one or more additional transcribed statements to the first user; inputting the first-user transcribed statements and the one or more additional transcribed statements into the attribute generation [algorithm];
receiving, as an output, from the attribute generation [algorithm] the first user attribute score; and
determining a user group as a function of the first user attribute.
In regard to Claims 1 and 11, Applicant claims training/employing a machine learning model in a particular technological environment, which was held to be abstract by the CAFC in, e.g., Recentiv Analytics.
In regard to the dependent claims, they also claim an abstract idea to the extent that they merely claim further limitations that likewise could be performed as a mental process by a human being and/or claim training/employing a machine learning model in a particular technological environment.
Furthermore, this judicial exception is not integrated into a practical application because to the extent that additional elements are claimed either alone or in combination such as, e.g., embodying Applicant’s abstract idea as computer software stored on a “memory” and executing on “a processor”, user devices, automatic speech recognition, a chatbot, and/or training/employing machine learning models, these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering), to embody the abstract idea on a general purpose computer, and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In this regard, see MPEP 2106.04(d)(I) in regard to “courts have also identified limitations that did not integrate a judicial exception into a practical application…”
Furthermore, the claims do not include additional elements that taken individually, and also taken as an ordered combination, are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., embodying Applicant’s abstract idea as computer software stored on a “memory” and executing on “a processor”, user devices, automatic speech recognition, a chatbot, and/or training/employing machine learning models, these are generic, well-known, and conventional elements and are claimed for the generic, well-known, and conventional functions of collecting and processing data and/or providing an analysis/outputs based on that processing. To the extent that an apparatus is claimed as an additional element said apparatus fails to qualify as a “particular machine” to the extent that it is claimed generally, merely implements the steps of Applicant’s claimed method, and is claimed merely for purposes of extra-solution activity or field of use. See MPEP 2106.05(b). As evidence that these additional elements are generic, well-known, and conventional, Applicant’s specification discloses the support for these elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See, e.g., F7 in Applicant’s specification; see, e.g., p30 specifically in regard to automatic speech recognition; see, e.g., p132 in regard to a chatbot; and/or see, e.g., p57 and 72 regarding training/employing machine learning models.
Response to Arguments
Applicant argues on page 5 of its Remarks in regard to the rejections made under 35 USC 101:
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Applicant’s arguments are not persuasive. A number of these limitations (“digital multi-speaker audio using an automatic speech recognition system”, “machine learning model”) are not identified in the 101 rejection as being part of the alleged abstract idea. Furthermore, collecting audio data, analyzing that data to attribute it to a certain speaker, and then outputting that data to an algorithm are abstract as a mental process. See, e.g., the CAFC opinions cited supra in that regard.
Applicant’s claimed invention is according to Applicant’s PGPUB directed to “forming optimal student groups in order to facilitate discussions” and thereby can be characterized as a method of organizing human activity in terms of teaching/training human beings. See MPEP 2106.04(a)(2)(II).
Applicant argues that it has claimed a “practical application” because its claimed invention is allegedly analogous to that of Desjardins. The APR held in Dejardins that the specific improvement made to machine learning by the invention claimed therein rendered patent eligible subject matter under the Mayo test. However, Applicant’s claimed invention does not concern anything like (from Desjardins):
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Instead, Applicant’s trains and employs generic machine learning models in a fashion closely analogous to the subject matter held to be patent ineligible in Recentive Analytics. In terms of, Applicant claims training machine learning models with training data, and providing inputs to the trained models in order to generate outputs.
Applicant further argues that it has claimed a “practical application” because its claimed invention in regard to “machine processing of mixed speech data” is allegedly analogous to that of the Office’s 101 Example 48. While Applicant does not indicate as much, presumably Applicant meant claims 2 or 3 from Example 48, because the Office indicated that claim 1 was not patent eligible. Here are claims 2 and 3:
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Applicant, on the other hand, claims employing an “automatic speech recognition system”, but without claiming or disclosing any further detail as to how that system works. This is not analogous to Example 48 but, instead, must be well-understood, routine, and conventional given the limited disclosure in Applicant’s specification as to how to use this feature and, thereby, does not render “significantly more” than Applicant’s abstract idea.
Applicant further argues that it has claimed “significantly more” than its abstract idea. Applicant’s argument is not persuasive as none of Applicant’s claimed devices and/or software techniques are improved by their embodiment of Applicant’s invention. Namely, Applicant’s claimed processor and/or memory do not, e.g., run faster, use less power, and/or be able to be manufactured more cheaply than they would otherwise; Applicant’s claimed automatic speech recognition system does not function faster or more precisely; and Applicant’s claimed training and employment of machine learning models does not make these models provide better or quicker results as a result of embodying Applicant’s invention. At best, Applicant’s claimed invention could provide an improvement in terms of allowing human beings to form better study groups to facilitate their discussions of certain topics. That is, if the human beings, in fact, looked at the output of Applicant’s computer program and then actually acted on that output to form those groups, neither of which are currently claimed. Such a (potential) improvement to human functioning, however, resulting from the viewing of a computer display is not patent eligible under the Mayo test:
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Trading Technologies v. IBG LLC, slip. op. page 9.
For these reasons the rejections made under 35 USC 101 are maintained.
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 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mike Grant whose telephone number is 571-270-1545. The Examiner can normally be reached on Monday through Friday between 8:00 a.m. and 5:00 p.m., except on the first Friday of each bi-week.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's Supervisory Primary Examiner, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL C GRANT/Primary Examiner, Art Unit 3715
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