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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Galitsky (US 2020/0356605 A1).
As per claim 1, Galitsky teaches a computer-implemented method, comprising:
receiving, via one or more processors, a first set of textual data (0050, - accessing input text, 0051, 0055); and
generating, via the one or more processors and using a trained machine-learning model that is applied to the first set of textual data, a classification of the first set of textual data (0053-0054, 0128-0129), wherein:
the trained machine-learning model has been trained based on a subset of textual data that resulted from filtering a set of training textual data (0095, 0090, 0128-0129); and
the filtering of the set of training textual data to generate the subset of textual data is based on a comparison between the training textual data and a second set of textual data (0127-0128, 0168-0169, 0213).
As per claim 2, Galitsky teaches the computer-implemented method of claim 1, wherein the filtering includes: extracting one or more keywords from the second set of textual data(0213); and extracting, as the subset of textual data, one or more portions of the set of training textual data corresponding to the one or more keywords (0213).
As per claim 3, Galitsky teaches the computer-implemented method of claim 1, wherein the filtering includes applying a similarity-based matching technique to the set of training textual data and the second set of textual data (0126-0128, 0156-0159).
As per claim 4, Galitsky teaches the computer-implemented method of claim 3, wherein the similarity-based matching technique includes a cosine similarity (0126-0128, 0156-0159).
As per claim 5, Galitsky teaches the computer-implemented method of claim 1, wherein the set of training textual data includes one or more prior classifications applied to the training textual data (0053-0054, 0128-0129).
As per claim 6, Galitsky teaches the computer-implemented method of claim 1, wherein: the first set of textual data is received via an interactive chat with a chat bot (0095); and the method further comprises causing the chat bot to output the generated classification of the first set of textual data (0095, 0090, 0128-0129).
As per claim 7, Galitsky teaches the computer-implemented method of claim 1, wherein the set of training textual data includes one or more coded entries that are each associated with or categorized by a respective code (0088-0089, 0095).
As per claim 8, Galitsky teaches the computer-implemented method of claim 1, wherein: the trained machine-learning model was further trained using one or more sub-classification metrics determined based on the set of training textual data, such that the trained machine-learning model is configured to generate a respective sub-classification of the first set of textual data for each of the one or more sub-classification metrics; and the trained machine-learning model is further configured to generate the classification of the first set of textual data based on the one or more respective sub-classifications (0052-0054, 0095, 0090, 0127-0129, 0135, 0156, Fig.14).
As per claim 9, Galitsky teaches a system, comprising:
at least one memory storing instructions (0303-0304); and
at least one processor operatively connected to the at least one memory and configured to execute the instructions to perform operations (0303-0304), including:
receiving a first set of textual data (0050, - accessing input text, 0051, 0055); and
generating, using a trained machine-learning model that is applied to the first set of textual data, a classification of the first set of textual data (0053-0054, 0128-0129), wherein:
the trained machine-learning model has been trained based on a subset of textual data that resulted from filtering a set of training textual data (0095, 0090, 0128-0129); and
the filtering of the set of training textual data to generate the subset of textual data is based on a comparison between the training textual data and a second set of textual data (0127-0128, 0168-0169, 0213)..
As per claim 10, Galitsky teaches the system of claim 9, wherein the filtering includes: extracting one or more keywords from the second set of textual data (0213); and extracting, as the subset of textual data, one or more portions of the set of training textual data corresponding to the one or more keywords (0213).
As per claim 11, Galitsky teaches the system of claim 9, wherein the filtering includes applying a similarity-based matching technique to the set of training textual data and the second set of textual data (0126-0128, 0156-0159).
As per claim 12, Galitsky teaches the system of claim 9, wherein the set of training textual data includes at least one of: one or more prior classifications applied to the training textual data; or one or more coded entries that are each associated with or categorized by a respective code (0088-0089, 0095).
As per claim 13. Galitsky teaches the system of claim 9, wherein: the classification is a domain-specific classification; and the second set of textual data includes domain-specific information associated with the domain-specific classification (0052-0054, 0095, 0090, 0127-0129, 0135, 0156, Fig.14).
As per claim, 14, Galitsky teaches the system of claim 9, wherein: the trained machine-learning model was further trained using one or more sub-classification metrics determined based on the set of training textual data, such that the trained machine-learning model is configured to generate a respective sub-classification of the first set of textual data for each of the one or more sub-classification metrics; and the trained machine-learning model is further configured to generate the classification of the first set of textual data based on the one or more respective sub-classifications (0052-0054, 0095, 0090, 0127-0129, 0135, 0156, Fig.14).
As per claim 15, Galitsky teaches a computer-implemented method, comprising:
receiving, via one or more processors, a set of training textual data that includes respective textual data for each of a plurality of entities (0050, - accessing input text, 0051, 0055);
receiving, via the one or more processors, criteria data separate from the set of training textual data that defines one or more criteria for at least one classification (0053-0054, 0128-0129);
extracting a subset of textual data from the set of training textual data by filtering the set of training textual data based on a comparison between the set of training textual data and the criteria data (0213); and
training a machine-learning model, via the one or more processors and using the subset of textual data, to generate the at least one classification of input textual data of an entity (0095, 0090, 0128-0129).
As per claim 16, Galitsky teaches the computer-implemented method of claim 15, further comprising: extracting, via the one or more processors, the one or more criteria from the criteria data (0213).
As per claim 17, Galitsky teaches the computer-implemented method of claim 15, further comprising: monitoring, via the one or more processors, a data source for updated criteria data (0271); and upon detecting the updated criteria data via the monitoring, training a further machine-learning model based on the updated criteria data (0271).
As per claim 18, Galitsky teaches the computer-implemented method of claim 15, wherein the criteria data is labeled based on respective prior classifications of the respective textual data for each of a plurality of entities (0127-0130, .
As per claim 19, Galitsky teaches the computer-implemented method of claim 15, wherein: training the machine-learning model includes causing the machine-learning model to develop a respective sub-classification for each of the one or more criteria defined by the criteria data; and the classification that the machine-learning model is trained to generate includes the respective sub-classification for each of the one or more criteria (0052-0054, 0095, 0090, 0127-0129, 0135, 0156, Fig.14).
As per claim 20, Galitsky teaches the computer-implemented method of claim 15, further comprising: after the training, providing the machine-learning model to a library of models indexed based on classification, the library configured to provide access to a particular model in response to identification of a desired classification (0201, 0272, 0280-0283).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
The following is applicable prior art.
Galitsky (US 2020/0184155 A1) teaches systems, devices, and methods that use discourse analysis and other techniques to form questions and answers from text. The questions and answers can be used for different applications, including providing a virtual dialogue or generating training data for machine-learning models. For example, a dialogue application generates a discourse tree that represents text and identifies a question from a satellite elementary discourse unit of the discourse tree. The dialogue application annotates the text by inserting the generated question and labeling the satellite elementary discourse unit as an answer.
Wen et al., (US 2025/0190458 A1) teach a dialogue system, comprising: a first input configured to obtain first input data relating to speech or text provided by a user through a first interface; a first output configured to provide first output data relating to speech or text information specified by a determined dialogue act through the first interface; one or more processors, configured to: receive second input data through a second interface; store information specifying one or more configuration settings based on the second input data; and perform a dialogue method using a dialogue platform, the dialogue method comprising: determining dialogue information from the first input data; determining a dialogue act based on the determined dialogue information using a dialogue management module, wherein determining the dialogue act comprises selecting a next state from a plurality of states stored in the dialogue management module, wherein at least some of the plurality of states comprise information specifying a dialogue act and at least some of the plurality of states comprise transition information specifying a transition to another state, the transitions defining one or more dialogue pathways, wherein selecting the next state comprises selecting a state which is specified by the transition information in a current state or selecting a state which is specified by a rule in a first set of one or more rules, wherein at least one of the plurality of states comprises stored information specifying a configuration setting based on the second input data; updating a configuration of the dialogue platform for the next user input when the selected next state comprises stored information specifying a configuration setting.
Hwang et al., (US 2002/0078090 A1) teach a method and system for constructing a text summarization. At least one domain ontology that includes a set of concepts is selected. A user profile indicative of a user's interests is defined in terms of the ontology concepts. A document's relevance to the user is determined based upon the user profile. If the document is relevant, at least a portion of the ontology is used to extract concepts from the document. The degree of match between the extracted concepts and the user profile concepts is determined and the document text summary is generated if the degree of match exceeds a predetermined threshold. Generating the summary may include selecting sentences based on the concepts in the user profile, ranking the selected sentences by relevance to the user profile, selecting sentences for inclusion in the document text summary based upon the ranking, and merging the selected sentences into the document text summary.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
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, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
Prosecution Insights