DETAILED ACTION
Status of Claims
1. This office action is in response to RCE filed 12/4/2025.
2. Claims 1, 7, 9, 10, 16, 18, 19 are pending.
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 .
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, 7, 9, 10, 16, 18, 19
Claims 1, 7, 9, 10, 16, 18, 19 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.
Step 1: Claims 1-9 are directed to a method; claims 10-18 are directed to non-transitory computer readable medium, claims 19-20 to a system – each of which is one of the statutory categories of inventions.
Step 2A: A claim is eligible at revised Step 2A unless it recites a judicial exception and the exception is not integrated into a practical application of the application.
Prong 1: Prong One of Step 2A evaluates whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon).
Groupings of Abstract Ideas:
I. MATHEMATICAL CONCEPTS
A. Mathematical Relationships
B. Mathematical Formulas or Equations
C. Mathematical Calculations
II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY
A. Fundamental Economic Practices or Principles (including hedging, insurance, mitigating risk)
B. Commercial or Legal Interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
C. Managing Personal Behavior or Relationships or Interactions between People (including social activities, teaching, and following rules or instructions)
III. MENTAL PROCESSES.
Concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
See MPEP 2106.04 (a) (2) Abstract Idea Groupings [R-10.2019]
The limitations of the independent claims 1, 10 and 19 –
receiving an enhancement request comprising an original current earnings call transcript;
generating, using the original current earnings call transcript as an input to a text embedding generator, a plurality of original current earnings call transcript embeddings respectively based on a plurality of original sentiment-pertinent transcript sentences extracted from the original current earnings call transcript;
processing, using a supervised-learning language model, the plurality of original current earnings call transcript embeddings to generate a plurality of original current sentence sentiments;
processing, using a reinforced-learning language model configured with a policy neural network implementing a partially observable Markov decision process (POMDP), the plurality of original current earnings call transcript embeddings and input features to generate a plurality of new sentiment-pertinent transcript sentences and a plurality of new current sentence sentiments;
determining, using a supervised-learning reward model, a plurality of transcript sentence ranks by comparing the original current earnings call transcript embeddings with new current earnings call transcript embeddings generated from the plurality of new sentiment-pertinent transcript sentences;
identifying a subset of the plurality of transcript sentence ranks each indicating a sentiment improvement based on at least one of: (i) a positive shift in sentiment tone classification, or (ii) an increase in sentiment confidence score;
producing, from the original current earnings call transcript, a new current earnings call transcript comprising a set of enhanced sentiment-pertinent transcript sentences respectively mapped to the subset of the plurality of transcript sentence ranks;
training the supervised-learning reward model using the plurality of original current earnings call transcript embeddings, the new current earnings call transcript embeddings, and the plurality of transcript sentence ranks;
generating a plurality of new current earnings call transcript embeddings respectively based on the plurality of new sentiment-pertinent transcript sentences;
converting the plurality of transcript sentence ranks into a first reward score;
predicting, using a market dynamics simulator, a current earnings call reaction at least based on the plurality of original current earnings call transcript embeddings and the plurality of new current earnings call transcript embeddings;
converting the current earnings call reaction into a second reward score;
computing a sentiment shift penalty based on the plurality of original current sentence sentiments and the plurality of new current sentence sentiments;
deriving a final reward score from the first reward score, the second reward score, and the sentiment shift penalty;
updating the reinforced-learning language model based on the final reward score; and
providing the new current earnings call transcript in response to the enhancement request
– recite Mathematical Concepts and/or Mental Process and/or Certain Methods of Organizing Human Activity that are abstract ideas.
The dependent claims further limit the abstract idea(s) to –
wherein the plurality of original current sentence sentiments and the plurality of new current sentence sentiments each comprises a sentiment tone and a sentiment confidence (claim 7);
prior to providing the new current earnings call transcript:
generating a plurality of new current earnings call transcript embeddings respectively based on the plurality of new sentiment-pertinent transcript sentences;
converting the plurality of transcript sentence ranks into a first reward score;
predicting, using a market dynamics simulator, a current earnings call reaction at least based on the plurality of original current earnings call transcript embeddings and the plurality of new current earnings call transcript embeddings;
converting the current earnings call reaction into a second reward score;
computing a sentiment shift penalty based on the plurality of original current sentence sentiments and the plurality of new current sentence sentiments;
deriving a final reward score from the first reward score, the second reward score, and the sentiment shift penalty; and
updating the reinforced-learning language model based on the final reward score (claims 9, 16);
processing, using the supervised-learning language model, the plurality of original current earnings call transcript embeddings further produces an original current transcript sentiment,
processing, using the reinforced-learning language model, the input features and the plurality of original current earnings call transcript embeddings further produces a new current transcript sentiment, and
computing the sentiment shift penalty is further based on the original current transcript sentiment and the new current transcript sentiment (claims 9, 18)
– that also recite Mathematical Concepts, and/or Mental Process and/or Certain Methods of Organizing Human Activity.
See MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]
B. Evaluating Claims Reciting Multiple Judicial Exceptions
A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
In other claims, multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record.
See also RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract.”).
Hence under Prong One of Step 2A, claims recite a combination of judicial exception(s).
Prong 2: Prong Two of Step 2A evaluates whether the claim recites additional elements that integrate the judicial exception into a practical application of the exception.
Limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer or to any other technology or technical field – see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception – see MPEP 2106.05(e)
Limitations that are not indicative of integration into a practical application include:
Adding the words “apply it” (or an equivalent) with 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 – see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Additional elements recited by the claims, beyond the abstract idea, include: text embedding generator; original current earnings call transcript embeddings; new current earnings call transcript embeddings supervised-learning language model; reinforced-learning language model; training the supervised-learning reward model using the plurality of original current earnings call transcript embeddings, the new current earnings call transcript embeddings, and the plurality of transcript sentence ranks; market dynamics simulator; and a non-transitory computer readable medium executed by a computer processor (claim 10). Examiner finds that any additional element(s), beyond the judicial exception, has been recited at a high level of generality such that the claim limitations amount to no more than mere instructions to apply the exception using generic components (MPEP 2106.05(f)) or insignificant data gathering activities (MPEP 2106.05(g)).
MPEP 2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described “the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’” 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it”. 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims “so result focused, so functional, as to effectively cover any solution to an identified problem”)).
Here, the limitations –
“generating, using the original current earnings call transcript as an input to a text embedding generator, a plurality of original current earnings call transcript embeddings respectively based on a plurality of original sentiment-pertinent transcript sentences extracted from the original current earnings call transcript;”
“processing, using a supervised-learning language model, the plurality of original current earnings call transcript embeddings to generate a plurality of original current sentence sentiments;”
“processing, using a reinforced-learning language model configured with a policy neural network implementing a partially observable Markov decision process (POMDP), the plurality of original current earnings call transcript embeddings and input features to generate a plurality of new sentiment-pertinent transcript sentences and a plurality of new current sentence sentiments;”
“determining, using a supervised-learning reward model, a plurality of transcript sentence ranks by comparing the original current earnings call transcript embeddings with new current earnings call transcript embeddings generated from the plurality of new sentiment-pertinent transcript sentences;”
“producing, from the original current earnings call transcript, a new current earnings call transcript comprising a set of enhanced sentiment-pertinent transcript sentences respectively mapped to the subset of the plurality of transcript sentence ranks;”
“training the supervised-learning reward model using the plurality of original current earnings call transcript embeddings, the new current earnings call transcript embeddings, and the plurality of transcript sentence ranks;”
“generating a plurality of new current earnings call transcript embeddings respectively based on the plurality of new sentiment-pertinent transcript sentences;”
“predicting, using a market dynamics simulator, a current earnings call reaction at least based on the plurality of original current earnings call transcript embeddings and the plurality of new current earnings call transcript embeddings;”
“updating the reinforced-learning language model based on the final reward score;”
– have been expressed purely in terms of results, devoid of implementation details.
The original disclosure does not teach and person of ordinary skills in the art –
how the text embedding generator generates current earnings call transcript embeddings from the current earning call transcript;
how the reinforced-learning language model generates original current call transcript embeddings and original current sentence sentiments;
how a new earnings call transcript is produced from the original earnings call transcript;
how the supervised-learning reward model us trained;
how new current earnings call transcript embeddings are generated;
how the market dynamics simulator predicts a current earnings call reaction based on original and current earnings call embeddings;
how the reinforced-learning language model is updated
All purported inventive concepts reside in how the above steps are technically accomplished and not in how the processing technologically achieves the result which neither the specification or the drawings shed any light on.
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All recitations are no more than an instruction to apply a black box – reinforced-learning model or supervised-learning reward model – to earnings call transcript embeddings. There are no recitations of the structure and operation of such supervised learning model. There is no recitation on how the model is constructed or trained; there is no narrowing of their manner of implementation and operation of the supervised learning model or reinforced-learning model. There is only the aspirational objective for the models with no recitation as to how that objective is achieved. This is therefore not an improvement in technology, but rather advice to use some black box to determine earnings call sentiment shift.
See also Two-Way Media Ltd. v. Comcast Cable Commc’n, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017) (“The claim [before the court] requires the functional results of ‘converting,’ ‘routing,’ ‘controlling,’ ‘monitoring,’ and ‘accumulating records,’ but does not sufficiently describe how to achieve these results in a non-abstract way.”); see Intellectual. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (“Indeed, the claim language here provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it. Our law demands more.”); see Affinity Labs of Texas, LLC v. Amazon.com Inc., 838 F.3d 1266, 1269 (Fed. Cir. 2016) (“At that level of generality, the claims do no more than describe a desired function or outcome, without providing any limiting detail that confines the claim to a particular solution to an identified problem. The purely functional nature of the claim confirms that it is directed to an abstract idea, not to a concrete embodiment of that idea.”); see Move, Inc. v. Real Estate Alliance Ltd., 721 F. App’x 950, 952-53, 954-56 (Fed. Cir. 2018) (non-precedential) (“Instead of focusing on the technical implementation details of the zooming functionality, for example, claim 1 recites nothing more than the result of the zoom.”).
The combination of additional elements – generating, processing, determining, identifying, producing, training, converting, predicting, computing, deriving, updating – does not purport to improve the functioning of a computer or effect an improvement in any other technology or technical field. Instead, the additional elements do no more than use computer as a tool and/or link the use of the judicial exception to a particular technological environment or field of use. The focus of the claims is not on improvement in computers, but on certain independently abstract ideas – generating new earnings call transcripts from an original earnings call transcripts – that merely uses machine learning models as tools. Steps that do no more than spell out what it means to “apply it on a computer” cannot confer patent eligibility. Indeed, nothing in claim 1 improves the functioning of the computer, makes it operate more efficiently, or solves any technological problem. See Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1378, 1384-85 (Fed. Cir. 2019).
Therefore, the additional elements, individually or in combination, do not integrate the judicial exception into a practical application.
Hence, the claims are ineligible under Step 2A.
Step 2B: In Step 2B, the evaluation consists of whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
As discussed in Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic components, which is insufficient to provide an inventive concept.
When considered individually or as an ordered combination, the additional elements fail to transform the abstract idea of – generating new earnings call transcripts from an original earnings call transcripts – into significantly more.
See MPEP 2106.05(f) Mere Instructions To Apply An Exception [R-10.2019].
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Hence, the claims are ineligible under Step 2B.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to a judicial exception without significantly more.
Prior Art
Relevant Prior Art not relied upon but made of record:
US20210043211 Automatic summarization of financial earnings call transcripts (US Application No.17/070,500)
US20220358594 Counterfactual e-net learning for contextual enhanced earnings call analysis (US Application No.17/315,764)
US20240185270 Unsupervised Cross-Domain Data Augmentation for Long-Document Based Prediction and Explanation (US Application No.17/972,167)
NPL An intuitive introduction to text embeddings, stackoverflow.blog, Nov. 9, 2023.
NPL Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications by Zakir et al., Cornell University, Feb 27, 2025
NPL Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction by Cao et al., Cornell University, Apr 2024
Response to Arguments
Applicant's arguments filed 12/4/2025 have been fully considered but they are not persuasive.
101
Applicant argues that the claims are directed to a judicial exception.
Examiner respectfully disagrees.
A close look at the specification confirms that the claims recite a combination of Mathematical Concepts, Mental Process and Certain Methods of Organizing Human Activity.
--Mathematical Concepts
As per para [0044], the text embedding generator (130) may at least be configured to perform text vectorization entailing the translation of certain text (e.g., a sentence) to a numerical representation (or text embedding) thereof. Any text embedding may be expressed as a vector or array reflecting an ordered sequence of numbers, where the vector/array may be of any arbitrary size (i.e., have any number of vector/array elements). Further, each numerical value forming said text embedding may reference a dimension (i.e., often depicted as a word) within a vocabulary (i.e., any number of unique words) chosen from a corpus (i.e., collection of texts in the finance domain). The numerical values themselves may each, for example, indicate: whether the corresponding dimension/word appears in a given sentence (where the vector/array is described as sparse); or a frequency of said dimension/word that appears in the given sentence (where the vector/array is described as dense).
As per para [0057], a text embedding (e.g., an original current earnings call transcript embedding) may generally refer to a numerical representation of a sentence or line of text (e.g., an original sentiment-pertinent transcript sentence), which may be suited for computer-based text semantics analysis. Each said text embedding may be expressed as a vector or array reflecting an ordered sequence of numbers, where the vector/array may be of any arbitrary size (i.e., have any number of vector/array elements). Further, each numerical value forming said text embedding may reference a dimension (i.e., often depicted as a word) within a vocabulary (i.e., any number of unique words) chosen from a corpus (i.e., collection of texts). The numerical values themselves may each, for example, indicate: whether the corresponding dimension/word appears in a given sentence/line of text (where the vector/array is described as sparse); or a frequency of said dimension/word that appears in the given sentence/line of text (where the vector/array is described as dense). Moreover, any original current earnings call transcript embedding(s) may be generated using any existing text vectorization technique.
As per para [0067], in Step 226, following the alternate determination (made in Step 220) that a model performance of the supervised-learning reward model is acceptable (i.e., indicating that the supervised-learning reward model has undergone sufficient training by way of labeled samples), one or more new current earnings call transcript embeddings is/are generated. In one or many embodiment(s) described herein, each new current earnings call transcript embedding may be obtained through text vectorization of a corresponding new sentiment-pertinent transcript sentence (produced in Step 210).
Hence, Examiner notes that based on the above noted disclosure from the specification, the limitations “generating a plurality of new current earnings call transcript embeddings respectively based on the plurality of new sentiment-pertinent transcript sentences” falls under the Mathematical Concepts category of abstract ideas.
As per para [0059], the document sentiment tone may be represented as the statistical mode of the collection of sentence sentiment tones – otherwise represented by the sentence sentiment tone that appears the most often across the collection of sentence sentiment tones. The document sentiment tone may be derived using other algorithms without departing from the scope of the embodiments described herein. Meanwhile, the sentiment score/confidence may refer to a numerical value expressing a likelihood or probability that the sentiment tone is correct.
As per para [0073], computation of first reward score may entail any algorithm(s) through which a scalar, numerical value may be derived from the manipulation of the transcript sentence rank(s). As per para [0075], computation of second reward score may entail any algorithm(s) through which a scalar, numerical value may be derived from the manipulation of the current earnings call reaction.
As per para [0077], computation of said sentiment shift penalty may entail any algorithm(s) through which a scalar, numerical value may be derived from the manipulation of at least one statistical distance value.
As per para [0078], derivation of the final reward score may entail any algorithm(s) through which a scalar, numerical value may be produced from the manipulation of the first reward score, the second reward score, and the sentiment shift penalty. By way of a non-limiting example, the final reward score may be calculated using the mathematical formula: final reward score = [first reward score + second reward score] – [weight value x sentiment shift penalty].
Hence, Examiner notes that based on the above quoted disclosure from the specification, the claim limitations – “converting the plurality of transcript sentence ranks into a first reward score,” “converting the current earnings call reaction into a second reward score,” “computing a sentiment shift penalty based on the plurality of original current sentence sentiments and the plurality of new current sentence sentiments; deriving a final reward score from the first reward score, the second reward score, and the sentiment shift penalty; updating the reinforced-learning language model based on the final reward score;” – fall under the Mathematical Concepts category of abstract ideas.
--Mental Process
As per para [0065], any transcript sentence rank may indicate an opinion of the consulted investor relations expert concerning a comparison performed thereby between an original sentiment-pertinent transcript sentence and a corresponding new sentiment-pertinent transcript sentence. Said opinion, more specifically, may be expressed as a simplified indicator (e.g., −1, 0, or 1) reflecting whether any new sentiment-pertinent transcript sentence has degraded (e.g., −1), remained the same (e.g., 0), or improved (e.g., 1) sentimentally in comparison with the corresponding original sentiment-pertinent transcript sentence. Any new sentiment-pertinent transcript sentence that indicate sentimental improvement over their respective original sentiment-pertinent transcript sentence may be referred to herein as an enhanced sentiment-pertinent transcript sentence. Examiner notes that based on the above disclosure, generating enhanced sentiment-pertinent transcript sentence involves observation, evaluation, judgement or opinion and hence constitutes Mental Process.
As per [0068], the supervised-learning reward model, thereafter and based on said inputted original current earnings call transcript embedding(s) and said inputted new current earnings call transcript embedding(s), may output or produce a transcript sentence rank (for each new sentiment-pertinent transcript sentence (produced in Step 210) corresponding to a given new current earnings call transcript embedding). Each produced transcript sentence rank, similarly to human feedback (received in Step 224), may be expressed as a simplified indicator (e.g., −1, 0, or 1) predicting whether any new sentiment-pertinent transcript sentence has degraded (e.g., −1), remained the same (e.g., 0), or improved (e.g., 1) sentimentally in comparison with the corresponding original sentiment-pertinent transcript sentence.
Examiner thus notes, that the supervised-learning reward model merely automates the Mental Process of producing enhanced sentiment-pertinent transcript sentence and therefore the limitation “identifying a subset of the plurality of sentence ranks each indicating a sentiment improvement …” falls under the Mental Process grouping of abstract ideas.
--Certain Methods of Organizing Human Activity
As per para [0049], the market dynamics simulator (140) may at least be configured to model an environment in which the financial performance of an organization's stock may be simulated based, at least in part, on the set of new sentences predicted/produced by the reinforced-learning language model (134). Particularly, the market dynamics simulator (140) may employ ensemble Gaussian processes and historical data (e.g., past earnings call transcript(s), past earnings statement(s), and/or past earnings call reaction(s)) to predict a prospective earnings call reaction (116) directed to a prospective earnings conference call scripted by a new (i.e., post-enhanced) earnings call transcript.
Hence, based on the above disclosure, the limitation “predicting, using a market dynamics simulator, a current earnings call reaction at least based on the plurality of original current earnings call transcript embeddings and the plurality of new current earnings call transcript embeddings” also falls under Certain Methods of Organizing Human Activity.
Applicant argues that the amended claims is analogous to Example 39 of the Subject Matter Eligibility Examples issues January 7, 2019.
Examiner respectfully disagrees.
Despite Example 39’s hypothetical claim involving training a neural network, it nevertheless differs from the present claims in significant respects. Among other things, the claim in Example 39 transforms image data into modified image data that is then used to train the neural network. Applicant's claims are readily distinguishable from Example 39 both in terms of its data inputs and how the analysis is performed. In Example 39, the data used to train the model are “digital facial images” to which transformations are applied including “mirroring, rotating, smoothing, or contrast reduction to create a modified set of facial images”. In contrast, the inputs and output of the models – original earnings call transcript and new current earnings call transcript – are text data while the transformations include large language model primarily used for processing textual information. Thus, while Example 39 involves machine learning for image recognition, the pending claims in contrast, use machine learning for text information processing. For the above reasons, the analogy of the present claims to Example 39 do not hold up.
Applicant argues that like the patents in Enfish, the concepts in the claims are integrated into a practical application.
Examiner respectfully disagrees.
In Enfish, the invention at issue was directed at a wholly new type of logical model for a computer database – a self-referential table that allowed the computer to store many different types of data in a single table and index that data by column and row information. The disclosed technique in Enfish enabled faster searching and more effective storage of data than previous methods.
Applicant’s invention, on the other hand, is not directed to creating a wholly new type of data structure comparable to the self-referential table of Enfish. Rather, applicant’s invention is directed to applying language learning models to an earnings call transcript to generate a new earnings call transcript comprising enhanced sentiment-pertinent transcript sentences mapped to transcript sentence ranks. Generating a new earnings call transcript is, at most, an improvement to information in an earnings call as opposed to an improvement in the functioning of a computer.
As per MPEP 2106.05(a) (“To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.”).
Here, Examiner notes that the pending claims are directed to using language model and reward model to generate a new earnings call transcript (containing enhanced sentiment-pertinent sentences mapped to ranks) from original earnings call using generic computer components.
To the extent the claimed invention purports to provide an improvement, that improvement does not concern an improvement to computer capabilities but instead relates to achieving the aim of enhancing an earnings call transcript – a process in which machine learning models are merely used as tools. Merely employing machine learning models to enhance earnings call statement does not change the fact that the claims do not improve computer messaging technology, but instead invokes such technologies merely as tools. At most, the claimed invention improves the information in the original earnings call transcript but it does not improve computers or machine learning models. See, e.g., BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1288 (Fed. Cir. 2018) (“[A]n improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality.”). Similarly, a new earnings call statement with enhanced sentiment-pertinent transcript sentences mapped to transcript sentence ranks – may help investors understand financial results of a publicly traded organization but it does not achieve an improved technological result.
With respect to the applicant’s argument that a human mind is not equipped to perform the claimed steps in the claims, Examiner notes that the Federal Circuit has held that using machine learning methods to former human activities is not patent eligible. See Recentive Analytics Inc. v. Fox Corp., 134 F.4th 1205, 1213 (Fed. Cir. 2025) (“[T]he claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity.”).
The combination of limitations does not bring about (i) an improvement to the functionality of a computer or other technology or technical field; (ii) a “particular machine” to apply or use the judicial exception; (iii) a particular transformation of an article to a different thing or state; or (iv) any other meaningful limitation. See MPEP 2106.05(a)-(c), (e)-(h). Hence, the additional elements fail to integrate the recited combination of abstract idea(s) into a practical application or provide significantly more. See MPEP 2106.05(f).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARUNAVA CHAKRAVARTI whose telephone number is (571)270-1646. The examiner can normally be reached 9 AM - 5 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached at 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARUNAVA CHAKRAVARTI/Primary Examiner, Art Unit 3692