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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea in regards to claims 1, 14 and 20 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s stands on this subject matter because, under step 2A, the claims recite an abstract idea in the form of mental process and organizing human activity. A person can read text, replace a person’s name with a generic label, identify text related to task, determine whether additional context is needed, classify the task into a hierarchy of categories, select an appropriate template, combine the template with the text to form an instruction, use the instruction to extract task information, restore the person’s name, create a to-do-item, and communicate the task to the appropriate person. These steps describe collecting, analyzing, organizing and presenting information and managing tasks, which are activities that can be performed mentally or with the aid of generic tools. The claims do not integrate the abstract idea into a practical application because it merely uses a generic natural language processing tools as part of the information processing tool as part of the information processing workflow and do not recite a specific improvement to computer functionality, natural language processing technology or another technical field.
Under step 2B, the additional limitations do not provide an inventive concept. The hierarchical to do types, command text templates, extraction command, natural language processing tool, task creation, and task notification are recited only by the results they achieve, without specifying a particular technical implementation or algorithm that improves a computer performance. Likewise, the claims do not explain how the information quantity is determined, how the preset condition is technically evaluated, or how the natural language processing tool itself is improved. Considered individually or as an ordered combination, the limitations merely automate the longstanding practice of reviewing text, classifying tasks, extracting information using instructions or templates, and assigning work using conventional computer functions. Accordingly, the claims amounts to no more than applying a generic natural language processing tool to implement an abstract idea.
Applicant's arguments with respect to 35 U.S.C. 103 rejection of claims 1, 14 and 20 have been considered and found persuasive, and the rejection has been withdrawn. See detailed reason for indicating of allowance.
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 and 14-20 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. The claims 1, 14 and 20 are directed to the abstract idea of collecting, analyzing, and manipulating information, specifically, reading text, identifying portions related to a task, evaluating whether sufficient information is present, supplementing the text with additional context, and extracting a to-do item. These steps constitute mental processes and methods of organizing human activity, both of which are judicial exceptions under 35 U.S.C. 101. The claim recites only functional, result-oriented language (“obtaining,” “identifying,” “determining,” “supplementing,” “extracting”) without specifying any particular technological implementation, specialized data structure, or improvement to computer functionality. Nothing in the claim ties the method to a specific machine or transforms it into a practical application beyond the abstract idea itself.
Further, the claims lack an inventive concept. The steps described are well-understood, routine, and conventional text-processing activities, and the claim merely automates a human task—reviewing text and extracting action items—using generic computer operations. No unconventional algorithms, architectures, or technical features are recited that would amount to significantly more than the underlying abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 2-9 and 15-19 further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in text processing.
Claims 2 and 15; generic data-preparation and analysis that do not improve computer technology and therefore do not amount to significantly more than the abstract idea.
Claims 3 and 16, routine text-segmentation and classification operations.
Claims 4 and 17, a standard supervised-learning setup for classifying information.
Claims 5 and 18, another generic classifier/regressor applied to text.
Claims 6, merely recite routine supervised training of a machine-learning model.
Claims 7 and 19, still describes only how additional context is chosen within the same informational corpus.
Claim 8, is just an additional rule for when to gather more context.
Claim 9, is routine in text processing and does not improve computer functionality or add significantly more than the abstract idea of evaluating and refining text.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Mukherjee et al. (“Smart To-Do: Automatic Generation of To-Do Items from emails”; May 5, 2020) teaches that, given an email, its thread, and metadata such as sender and recipient names, the system generates “a short and succinct To-Do item for the task mentioned in the email.” It identifies “the task sentence,” “relevant sentences” providing context, and “entities (e.g., people) associated with the task.” (see [1.]) Mukherjee further states that helpful contextual information is combined to generate the to-do item using a sequence to sequence architecture (see [1.] [Fig. 2]).
Ray et al. (US 2020/0410989) teaches, for example, Ray replaces “Alice” with the general token <contact> while retaining the contact-slot annotation (see [0036-0037] [0074-0075]). Ray explains that an intent determines the action and slots determine the action parameters; its example identifies the intent send_message, the contact slot “Alice,” and the message slot “happy birthday.” (see [0035]) Ray also compares a “slot entropy score” with an “uncertainty threshold,” altering a candidate when the score exceeds the threshold. (see [0092-0094] [Fig. 8]) Once the intent and parameters are determined, the personal assistant service performs the task and presents the results to the user (see [0056-0057] [0118-0119]).
Liu et al. (“Meeting Action Item Detection with Regularized Context Modeling”; 2023) teaches action item detection as determining whether a sentence contains an actionable task and identifies task information such as the task description, timeframe, and owner (see [1.] [2.2]). Liu observes that “coreferences and omission of information are quite common in multi-party meetings” and selects the top-k sentences having the highest cosine-similarity scores as global context (see [3.1]). Its model processes both “the focus sentence” and “the sentence with its local/global context” (see [3.1] [Fig. 1]).
The difference between the prior art and the claimed invention is that Mukherjee, Ray nor Liu explicitly teach determining a to-do type of the piece of original text data, wherein the to-do type comprises a first-level type, a second-level type, and a third-level type, the second-level type is a subdivision type of the first-level type, and the third-level type is a subdivision type of the second-level type, each second-level type has a corresponding command text template, and each third-level type has a corresponding command text template; splicing a command text template corresponding to the to-do type and the piece of original text data based on the command text template corresponding to the to-do type and the piece of original text data, and generating an extraction command text for the piece of original text data; inputting the extraction command text to a natural language processing tool to obtain the to-do data output by the natural language processing tool.
Therefore, it would not have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Mukherjee, Ray and Liu to include determining a to-do type of the piece of original text data, wherein the to-do type comprises a first-level type, a second-level type, and a third-level type, the second-level type is a subdivision type of the first-level type, and the third-level type is a subdivision type of the second-level type, each second-level type has a corresponding command text template, and each third-level type has a corresponding command text template; splicing a command text template corresponding to the to-do type and the piece of original text data based on the command text template corresponding to the to-do type and the piece of original text data, and generating an extraction command text for the piece of original text data; inputting the extraction command text to a natural language processing tool to obtain the to-do data output by the natural language processing tool.
Conclusion
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
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