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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/06/2026 has been entered.
Response to Amendment
3. In response to the office action mailed on 01/06/2026, applicant filed an amendment on 04/06/2026, amending claims 1-2, 8, 10-11, 15, and 17-18. The pending claims are 1-20.
Response to Arguments
4. Applicant’s arguments, see arguments, filed 04/06/2026, with respect to the pending claims have been fully considered and are persuasive. The corresponding rejection has been withdrawn.
Claim Rejections - 35 USC § 101
5. 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 non-statutory subject matter. 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: Is the claimed invention to a process, machine, manufacture or composition of matter?
The claimed invention, at independent claims 1, 10, and 17, is directed to a method (process), system (machine), and computer readable medium (manufacture) for determining, by a language model, a plurality of speech tags for a plurality of words associated with a body of text; processing, by a domain-agnostic context extraction (DCE) model, the plurality of words by determining whether each word is a noun, proper noun, or adposition to generate a set of n- grams corresponding to a domain-agnostic context of the body of text, wherein the processing comprises iteratively processing each word of the plurality of words in sequence by: discarding a current word when it is not a proper noun or noun and storing the current word when it is a proper noun or a noun in the set of n-grams; and iteratively processing each of one or more subsequent words following the current word in sequence by storing the subsequent word that is a proper noun, a noun, or an adposition in the set of n-grams and saving the set of n-grams when the subsequent word is not a proper noun, a noun, or an adposition; generating, based on the set of n-grams, a contextual summary of the body of text; generating an understanding of the body of text based on the set of n-grams; and updating one or more machine-learning models based on the set of n-grams, wherein the one or more machine-learning models comprise one or more of the language model, the DCE model, or a ranking model.
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Under the 35 U.S.C. 101 new guidelines, the broadest reasonable interpretation of the claims, the claimed steps fall within the “Mental Processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
The steps of determining, by a language model, a plurality of speech tags for a plurality of words associated with a body of text and processing the plurality of words by determining whether each word is a noun, proper noun, or adposition to generate a set of n-grams, encompass under its broadest reasonable interpretation a process which may be practically performed by in a human mind using observation, evaluation, judgment, and opinion. For example, a user can manually determine plurality of speech tags for a plurality of words associated with a body of text and determine whether each word is a noun, proper noun, or adposition without using a machine.
The steps of discarding a word when it is not a proper noun or noun and storing the word when it is a proper noun or a noun in the set of n-grams; and iteratively processing each of one or more subsequent words following the current word in sequence by storing the subsequent word that is a proper noun, a noun, or an adposition in the set of n-grams and saving the set of n-grams when the subsequent word is not a proper noun, a noun, or an adposition, encompass mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. A human can manually perform discarding a word when it is not a proper noun or noun and storing the word when it is a proper noun or a noun in the set of n-grams; and iteratively processing each of one or more subsequent words following the current word in sequence by storing the subsequent word that is a proper noun, a noun, or an adposition in the set of n-grams and saving the set of n-grams when the subsequent word is not a proper noun, a noun, or an adposition, using a pen and a paper.
The steps of generating, based on the set of n-grams, a contextual summary of the body of text and generating an understanding of the body of text based on the set of n-grams, a human can read through a text, generate an understanding of the text using observation, evaluation, judgment, and opinion, and manually generate a contextual summary.
The step of updating one or more machine-learning models based on the set of n-grams …, it encompasses a mental process practically performed in the human mind by observation, evaluation, judgment, and opinion. The limitation does not provide any details about how the machine learning model operates or how is updated, and the plain meaning of “updating” encompasses mental observations or evaluations. See MPEP 2106.04(a)(2), subsection III.
Therefore, the claimed steps fall within the mental process grouping of abstract ideas
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claims recite the additional elements of “one or more processor”, “one or more computing systems” to execute, to determine, to process…, are mere data gathering and manipulating recited at high level of generality, and thus are insignificant extra-solution activity. The one or more processor and one or more computing systems are recited at a high level of generality, and they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recitation of “machine learning” is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
As to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (Step 2B), as explained above in Step 2A, Prong 2, the use of “one or more computers” and “ one or more processors” is at high level of generality, and even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Accordingly, the claims are ineligible.
Dependent claims 2-9, 11-16, and 18-20 further refer and describe the process of processing each word of the plurality of words by determining whether a word is a noun, proper noun, or an adposition (claims 2-3, 11-12, 18), describing the DCE model as not trained on domain specific data (claims 4, 13, 20), determining whether a word comprises all uppercase letters or a length of the first word is not greater than one, then adding it to an abbreviation-and-acronym list and delete the first word from the set of n-grams (claims 5, 14), determining an intent of the text and updating the machine learning model based on the intent (claims 6-8, 15-16), and generating a set of n-grams, which encompass a mental process that is practically performed in the human mind, as explained above in Step 2A, Prong 1. Also, The recitation of “machine learning” is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping.
Accordingly, claims 1-20 are directed to an abstract idea, and are not patent eligible.
Prior Art
6. The prior art does not teach or suggest determining, by a language model, a plurality of speech tags for a plurality of words associated with a body of text; processing, by a domain-agnostic context extraction (DCE) model, the plurality of words by determining whether each word is a noun, proper noun, or adposition to generate a set of n- grams corresponding to a domain-agnostic context of the body of text, wherein the processing comprises iteratively processing each word of the plurality of words in sequence by: discarding a current word when it is not a proper noun or noun and storing the current word when it is a proper noun or a noun in the set of n-grams; and iteratively processing each of one or more subsequent words following the current word in sequence by storing the subsequent word that is a proper noun, a noun, or an adposition in the set of n-grams and saving the set of n-grams when the subsequent word is not a proper noun, a noun, or an adposition; generating, based on the set of n-grams, a contextual summary of the body of text; generating an understanding of the body of text based on the set of n-grams; and updating one or more machine-learning models based on the set of n-grams, wherein the one or more machine-learning models comprise one or more of the language model, the DCE model, or a ranking model, as claimed by independent claims 1, 10, and 17.
The prior art Finkelshtein (US 20210089620) relates to automatically applying a named-entity recognition (NER) algorithm to a digital text document, to detect named entities appearing in the digital text document and automatically detecting at least one relation between the named entities, by applying a parts-of-speech (POS) tagging algorithm and a dependency parsing algorithm to sentences of the digital text document which contain the detected named entities. Finkelshtein teaches traversing and analyzes text documents word after word and sentence after sentence and tags words in sentences with their grammatical part-of-speech, such as adjective, adposition, adverb, conjunction, article, noun, particle, pronoun, verb, etc. The results of the POS tagging and the dependency parsing is utilized in determining paths of dependencies connecting the traversed words and sentences to estimate whether each relation between a person-type entity and another person-type or non-person-type entity is indicative of some personal information.
The prior art Muraoka (US 20240135099) relates to determining a set of context sentence(s) for use in performing part of speech tagging on the target sentence with the context sentence(s) being determined based on the proximity of the context sentence(s) to the target sentence in the piece of natural language text of the corpus data set; and for each given word of the plurality of taggable words of the target sentence, performing natural language processing to determine a part of speech tag for the given word based, at least in part, on the set of context sentences.
The prior art Ohrn (US 7912849) relates to a method for determining contextual summary information across documents.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO_892.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659
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