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 .
DETAILED CORRESPONDENCE
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 14, 2025 has been entered.
Status of Claims
Claims 1, 10, 15, 16 have been amended. (NOTE: Claim 15 has been identified as “Currently Amended”. However, no amendments have been made and, accordingly, the identifier is incorrect.)
No claims have been cancelled.
No claims have been added.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
obtaining training data, the training data including a plurality of descriptions, each of the plurality of descriptions describing a corresponding property;
preprocessing the plurality of descriptions by tokenization and parsing out special characters;
preparing the training data by assigning a score to each description in the training data based on a number of days the property was on the market prior to selling and a number of users that have saved the description, wherein the assigned scores are normalized using a normalization formula;
extracting from the training data, for each of a plurality of features, a corresponding feature value of a first plurality of feature values, the plurality of features including a feature words count, number of adjectives, and grammatical mistake count;
a model using the first plurality of feature values corresponding to the plurality of features, the model including a plurality of coefficients, each coefficient corresponding to one of the plurality of features;
obtaining data including a listing description of an item;
extracting for the listing description, for each of the plurality of features, a corresponding one of a second plurality of feature values, wherein extracting for the listing description includes searching an adjective dictionary for words in the description to determine the number of adjectives, and searching a feature words dictionary for words in the description to determine the feature words count; and
applying the second plurality of feature values such that one or more scores are generated for the listing description; and
generating a new description using the one or more scores and the second plurality of feature values.
The invention is directed towards the abstract idea of rewriting written content, which is further based on the collection and comparison of information and, based on a rule(s), identify options, as well as, collecting and organizing information, which corresponds to “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., having a user review previous written work/description in order to assess the grammar of the content (e.g., word count, adjectives, and grammatical mistake count), assessing the description by assigning scores and normalizing the scores, referring to and applying extraction rules (word count, adjectives, grammatical mistakes, and dictionaries) to extract selected content from the description, assigning/determining scores, based the rules, to the extracted content, and rewriting/writing a new description based on the scores and rules, as well as performing mathematical calculations to assign scores and normalize the scores using a normalization formula. To put it another way, the claimed invention is directed towards rewriting a description or writing a new description based on the assessed prior description.
The limitations of:
obtaining training data, the training data including a plurality of descriptions, each of the plurality of descriptions describing a corresponding property;
preprocessing the plurality of descriptions by tokenization and parsing out special characters;
preparing the training data by assigning a score to each description in the training data based on a number of days the property was on the market prior to selling and a number of users that have saved the description, wherein the assigned scores are normalized using a normalization formula;
extracting from the training data, for each of a plurality of features, a corresponding feature value of a first plurality of feature values, the plurality of features including a feature words count, number of adjectives, and grammatical mistake count;
a model using the first plurality of feature values corresponding to the plurality of features, the model including a plurality of coefficients, each coefficient corresponding to one of the plurality of features;
obtaining data including a listing description of an item;
extracting for the listing description, for each of the plurality of features, a corresponding one of a second plurality of feature values, wherein extracting for the listing description includes searching an adjective dictionary for words in the description to determine the number of adjectives, and searching a feature words dictionary for words in the description to determine the feature words count; and
applying the second plurality of feature values such that one or more scores are generated for the listing description; and
generating a new description using the one or more scores and the second plurality of feature values
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor, generic machine learning model, and generic non-transitory computer readable medium. That is, other than reciting a generic processor machine learning model, generic processor, and generic non-transitory computer readable medium nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor machine learning model, generic processor, and generic non-transitory computer readable medium in the context of this claim encompasses a user collecting and review a description to score certain language from that was used and rewriting or writing a new description based on the user’s evaluation of the original description and describing the environment of use, in this case, a real estate description. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor machine learning model, generic processor, and generic non-transitory computer readable medium, then it falls within the “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor and generic non-transitory computer readable medium to communicate information, as well as performing operations that a human can perform in their mind and/or pen and paper, i.e. reviewing/assessing and scoring written content and writing/rewriting a new description based on the assessment of the description, as well as performing mathematical calculations to assign scores and normalize the scores using a normalization formula. The generic processor and generic non-transitory computer readable medium in the steps are recited at a high-level of generality (i.e., as a generic processor and generic non-transitory computer readable medium can perform the insignificant extra solution steps of communicating information (See MPEP 2106.05(g) while also reciting that the a generic processor and generic non-transitory computer readable medium are merely being applied to perform the steps that can be performed in the human mind and/or with the aid of pen and paper; "[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.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor and generic non-transitory computer readable medium.
Although the claim recites “training a machine learning model” and “applying a machine learning model”, the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses 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.
Even training and applying a trained machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a trained machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards reviewing written content to determine how well it has been written based on grammar rules. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic machine learning model, generic processor, and generic non-transitory computer readable medium to perform the steps of:
obtaining training data, the training data including a plurality of descriptions, each of the plurality of descriptions describing a corresponding property;
preprocessing the plurality of descriptions by tokenization and parsing out special characters;
preparing the training data by assigning a score to each description in the training data based on a number of days the property was on the market prior to selling and a number of users that have saved the description, wherein the assigned scores are normalized using a normalization formula;
extracting from the training data, for each of a plurality of features, a corresponding feature value of a first plurality of feature values, the plurality of features including a feature words count, number of adjectives, and grammatical mistake count;
a model using the first plurality of feature values corresponding to the plurality of features, the model including a plurality of coefficients, each coefficient corresponding to one of the plurality of features;
obtaining data including a listing description of an item;
extracting for the listing description, for each of the plurality of features, a corresponding one of a second plurality of feature values, wherein extracting for the listing description includes searching an adjective dictionary for words in the description to determine the number of adjectives, and searching a feature words dictionary for words in the description to determine the feature words count; and
applying the second plurality of feature values such that one or more scores are generated for the listing description; and
generating a new description using the one or more scores and the second plurality of feature values
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 2 is directed towards human activities and mental processes, in this case, reviewing/reading written content and assigning a score
Claim 3 is directed towards descriptive subject matter, in this case, describing the training data.
Claim 4 is directed towards human activities, mental processes, and collecting and organizing information, in this case, organizing information in groups.
Claim 5 is directed towards human activities, mental processes, and collecting and organizing information, in this case, organizing information in groups and describing the information and/or rule that the groups are based on.
Claim 6 is directed towards the recitation of generic technology/technique and applying it to the abstract idea, in this case, generically reciting that the machine learning models include supervised and semi-supervised learning, which can further include human activities as the claim is broad enough to encompass a human providing their input in response to the model’s output.
Claim 7 is directed towards descriptive subject matter, in this case, describing what the content is intended to be about, i.e. real estate property, commercial real estate, or vehicle.
Claim 8 is directed towards human activities, mental processes, and descriptive subject matter, in this case, describing the information that is used to generate a new description, i.e. based on the rule and feature values, generating (writing) a new description.
Claim 9 is directed towards human activities, mental processes, and descriptive subject matter, in this case, describing the information that is used to generate a new description, i.e. based on the rule and feature values, generating (writing) a new description, as well as the recitation of generic technology and applying it to the abstract idea to perform the extra-solution activity of displaying information via a generic graphical user interface (GUI).
The remaining claims are directed towards subject matter that has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for scoring written content to convey how well it reads or how well it was written. Accordingly, the claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 11/14/2025 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The applicant argues, “‘preprocessing the plurality of descriptions by tokenization and parsing out special characters,’ which are computer-specific operations that cannot be meaningfully performed mentally or with pen and paper.”
However, the Examiner respectfully disagrees.
The Examiner refers to the provided NPL documents cited in the PTO-892 to demonstrate that tokenization can, indeed, be performed by a human using pen and paper. NPL reference by Gunjal discloses, “Tokenization is one of the first step in any NLP pipeline. Tokenization is nothing but splitting the raw text into small chunks of words or sentences, called tokens. If the text is split into words, then its called as 'Word Tokenization' and if it's split into sentences then its called as 'Sentence Tokenization'. Generally 'space' is used to perform the word tokenization and characters like 'periods, exclamation point and newline char are used for Sentence Tokenization. We have to choose the appropriate method as per the task in hand. While performing the tokenization few characters like spaces, punctuations are ignored and will not be the part of final list of tokens.” (Page 3 – 4) Gunjal then provides examples that have been typed/written out and presented to a user to read to better understand the process, i.e. Gunjal explicitly provides an example demonstrating that tokenization and parsing out special characters can, indeed, be performed by a human in their mind and/or with the aid of pen and paper.
The applicant continues on to argue, “‘assigning a score to each description in the training data based on a number of days the property was on the market prior to selling and a number of users that have saved the description, wherein the assigned scores are normalized using a normalization formula,’ … fundamentally ties the claims to specific, measurable real-world data that can only be collected through computerized real estate platforms.”
However, the Examiner respectfully disagrees.
First, there is nothing in the limitation to demonstrate that this is deeply rooted in technology or that it cannot be practically performed by a human using pen and paper. The Examiner asserts that the limitation is directed towards collecting and organizing information that can be performed by a human using pen and paper as it simply entails a human collecting known information, i.e. number of days the property was on the market prior to selling and a number of users that have saved the description, and writing down the information.
Second, the limitation is directed to “Mathematical Concepts”, which is admitted by the applicant. Specifically, the limitation recites that scores are assigned using an algorithm; however, the applicant further admits, “Furthermore, applying ‘a normalization formula’ represents a mathematical transformation requiring computational application of scaling algorithms to ensure comparability across properties with different characteristics. This combination of collecting platform analytics, applying mathematical formulas, and creating training labels based on measurable outcomes represents a concrete, technologically grounded approach fundamentally different from abstract mental evaluation.”
With that said, nothing in the claim is there any recitation that this is being performed by any technology, but, even if it were, it would not be a demonstration that the process is deeply rooted in technology, improving technology, resolving an issue in technology, or that it cannot be performed by a human using pen and paper. The Examiner asserts that a human can collect information, apply a formula, and calculate and assign scores.
Third, the limitation is directed towards descriptive subject matter describing what the applicant believes, in their mind, is the “best” data to use as part of the training data. This is subjective and not objective as another human may believe, in their mind, that neighborhood information, proximity to points of interests, landmarks, and/or geographical features, and etc. are better than the “number of days the property was on the market prior to selling and a number of users that have saved the description, and writing down the information.”
The applicant further argues, “wherein extracting for the listing description includes searching an adjective dictionary for words in the description to determine the number of adjectives, and searching a feature words dictionary for words in the description to determine the feature words count,” “represent specialized data structures specifically curated for this application, not generic text processing. The searching operations involve algorithmic comparison of words against entries in these specialized databases, requiring computational string matching, database queries, and aggregation of results. This dictionary-based approach provides a deterministic, reproducible method for extracting specific linguistic features that goes beyond generic natural language processing and represents a targeted technical solution requiring specialized data structures and search algorithms.”
However, the Examiner respectfully disagrees.
The Examiner asserts that describing data is not equivalent to a “specialized data structure”, but, in fact, descriptive subject matter. The limitation is doing nothing more than describing stored information and a rule to perform the abstract idea of collecting and comparing information and, based on a rule(s), identify options. The limitation is not technical, improving upon technology, resolving an issue that arose in technology, or deeply rooted in technology. A human, in their mind and/or with the aid of pen and paper, is fully capable of reading a description, extracting certain terms from the description, referring to a dictionary, thesaurus, or the like to compare the extracted terms and determine if the term is an adjective, counting the number of adjectives that were used in the description, and perform this process for feature words.
The applicant continues to argue, “’generating a new description using the one or more scores and the second plurality of feature values.’ This is not merely analyzing or scoring text but creating new textual content through computational processing. As the specification discloses, ‘To generate the new description, a sub-score and/or associated feature value(s) may be applied to generate a portion of the new description.’ This generative capability represents a transformative.”
However, the Examiner respectfully disagrees.
The Examiner asserts that the applicant appears to be arguing that they have developed a new generative machine learning model or improving generative machine learning model technology, however, the claimed invention has been recited at a high level of generality and applying it to the abstract idea. As was previously discussed in the Final Rejection mailed on 9/24/2025, “For example, claim 1 is generic with regards to how the training of the model is performed and applied and claim 6 is supports this position because claim 6 simply recites that the machine learning model is trained using supervised and semi-supervised learning and nothing more. Upon review of the applicant’s specification, the Examiner asserts that the specification discloses similar support, i.e. reciting generic technology at a high level of generality and applying it to the abstract idea. The applicant argues that the invention is unlike conventional technology, but, again, when reviewing the claimed invention and specification, the manner in which the applicant is attempting to improve the technology has been recited at a high level of generality that is relying on generic techniques, e.g., labeling, supervised and semi-supervised learning, and extraction of information, without providing the specific or critical techniques to demonstrate an improvement as was done in Enfish.”
Although the claimed invention has been amended to recite additional subject matter, the Examiner asserts that they are descriptive, as has been discussed above. Moreover, the limitations, as discussed above, are not deeply rooted in technology or resolving an issue that arose in technology, but have been recited at a high level of generality and being applied to the abstract idea and, at best, have done nothing more than describe processes that can be performed by a human in their mind and/or with the aid of pen and paper and provide descriptive subject matter to describe the environment of use, the data to be used, mathematical concepts, and data sources to compare a description against.
Finally, generating a new description is not deeply rooted in technology nor is it improving technology or resolving an issue that arose in technology, but describing what a human can perform in their mind and/or with the aid of pen and paper, while also reciting the process at a high level of generality that does not demonstrate how technology is being improved upon or resolving an issue that arose in technology. The Examiner asserts that the invention amounts to what, for example, elementary school English teachers have done long before the effective filing date of the invention, e.g., an elementary school English teacher reviewing and editing a student’s work and commenting, suggesting, or rewriting/writing a new, different, or better description than what was handed in by the student, but still based on what the student provided. The applicant’s claimed invention has done nothing more than describe this general process to be performed in a particular environment of use, describe content that corresponds to the environment of use, and reciting generic technology at a high level of generality and applying it to the abstract idea.
With regards to claim 3, the claim is simply describing the two data groups that are included in the training data and then describes the generic technique of labeling each group and nothing more. There is a lack of specificity to demonstrate that the claimed invention is performing a process that is not generic and, therefore, is simply reciting generic technology at a high level of generality and applying it to the abstract idea. The applicant argues and compares the claimed invention against the state of the art and expresses how the claimed invention improves or is intended to improve over the state of the art, however, the claim lacks such levels of specificity to demonstrate an improvement. Again, claim 3 provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f).
With regards to claims 4, 5, the Examiner asserts that these claims also suffer from similar deficiencies and, accordingly, refers to and incorporates the analysis provided above. Simply assigning scores that the applicant believes, in their mind, are the “best” scores or descriptions based on some data to assign a group is not an improvement to technology or resolving an issue that arose in technology, but descriptive in nature, as claimed, which is further supported by claim 7, where the claim describes various possible intended information that could represent a description.
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards reviewing written content to determine how well it has been written based on grammar rules. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Kant (Tokenization A complete guide); Tekgul (Tokenization and Tokenizers for Machine Learning); Otten (Top 5 Ways to Implement NLP Text Tokenization Techniques in Python With NLTK, SpaCy, TextBlob, Genism & Keras); Gunjal (Tokenization in NLP) – which are NPL documents supporting statements made in the Response to Arguments section of the instant office action
Smith et al. (US PGPub 2024/0273291 A1); Gaidylo et al. (US Patent 12,333,247 B1); Mackey et al. (US Patent 11,960,822 B2); Olsen et al. (US Patent 11,727,198 B2); Chiba et al. (US Patent 10,963,626 B2); Hoover et al. (US PGPub 2011/0313757 A1); Shevchenko et al. (US Patent 10,771,529 B1); Nagvenkar et al. (US PGPub 2021/0397787 A1); Guiliak et al. (US Patent 10,915,697 B1) – which are directed towards utilizing machine learning to assist with reviewing written work and providing suggestions to improve it
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 1/12/2026