DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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 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.
Response to Amendment
3. In the amendments filed on 4/16/2025, claims 35 and 40 are amended. Claims 1-30 are cancelled. Claims 47-50 have been added. Claims 31-50 are pending in this office action.
Priority
4. Applicant's claim for the benefit of prior-filed US application 18/441,797, now US Patent 12,292,910, filed 2/14/2024, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Information Disclosure Statement
5. Initialed and dated copy of Applicant's IDS form 1449, filed 4/29/2025, is attached to the instant Office action.
Specification
6. The abstract of the disclosure is objected to because the abstract appears to be over 150 words and contain the legal word "embodiments", which should be removed. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Double Patenting
7. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321I or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
8. Claims 47 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No. 12,292,910. Although the claims at issue are not identical, they are not patentably distinct from each other as shown below, since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
The subject matter claimed in the instant application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
U.S. Patent No. 12,292,910
Instant Application
Claim 13,
A command user interface tool to assist labeling an initial dataset of unlabeled datapoints with a plurality of different categories comprises:
a user input device for receiving user commands;
a display;
and a processor framework programmed and operable to:
upon receiving a user search command, generate a filtered set of unlabeled datapoints by searching and filtering the initial data set of unlabeled datapoints based on a keyword associated with each category;
generate a verified set of labeled datapoints based on a user manually reviewing and labeling the filtered set of unlabeled datapoints;
prompt the user to select a first data-labeling model;
upon receiving a user train command, train, during a first phase, the first data-labeling model based on the verified set of labeled datapoints;
upon receiving a user compute command, compute a category and confidence score for each datapoint of the initial dataset based on the first data-labeling model; prompt the user to select a second data-labeling model different than the first data-labeling model, and train the second labeling model, based on the verified set of labeled datapoints;
compute a category and confidence score for each datapoint of the initial dataset based on the second data-labeling model; compare, for each datapoint, the category and confidence score of the first data-labeling model and second data-labeling model;
and show datapoints in which the category computed by the first data-labeling model disagrees with the category computed by the second data-labeling model.
Claim 47,
A command user interface system to assist labeling an initial dataset of unlabeled datapoints comprises:
a production classifier programmed and operable to execute a machine learning model trained on a dataset to automatically determine a confidence score for a new datapoint supplied by a sub-user computing device, and to assign a label to the new datapoint based on the confidence score;
a main server programmed and operable to electronically transfer data and communications between the sub-user computing device and the production classifier, and between the production classifier and a command line interface tool; a command line interface tool comprising:
a user input device for receiving user commands;
a display;
and a processor framework programmed and operable to:
upon receiving a user search command, generate a filtered set of unlabeled datapoints by searching and filtering the initial data set of unlabeled datapoints based on a keyword associated with each category;
generate a verified set of labeled datapoints based on a user manually reviewing and labeling the filtered set of unlabeled datapoints;
upon receiving a user train command, train, during a first phase, a first data-labeling model based on the verified set of labeled datapoints;
and upon receiving a user compute command, compute a category and confidence score for each datapoint of the initial dataset based on the first data-labeling model.
9. Claim(s) 31 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S Patent No. 12,292,910 in view of Wang et al (US Publication 2024/0112014 A1).
Claim 1 of US Patent 12,292,910 teach all the limitations of claim 31 of the instant application, except for detecting entity behaviors from input computing devices; generating, on a server, unlabeled datapoints based on the entity behaviors, as recited in claim 31.
Wang teaches detecting entity behaviors from input computing devices; generating, on a server, unlabeled datapoints based on the entity behaviors. (paragraph 0021, 0031, 0043, 0069, a named entity recognizer model is utilized for labeling rules, entity recognition interpreted as detecting entity behaviors)
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine the instant application’s method to classify new data points into categories with Wang’s ability to perform entity recognition for labeling. The motivation for doing so would be to better perform data annotation and training (paragraph 0003).
Claim Rejections - 35 USC § 112
10. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
11. Claim 31 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 31 recites the limitation “the new datapoint” in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
12. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
13. Claim(s) 47-50 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ardila et al. (US Patent 12,271,443 B1)
As per claim 47, Ardila teaches A command user interface system to assist labeling an initial dataset of unlabeled datapoints comprises: (see Abstract)
a production classifier programmed and operable to execute a machine learning model trained on a dataset to automatically determine a confidence score for a new datapoint supplied by a sub-user computing device, and to assign a label to the new datapoint based on the confidence score; (column 4 lines 25 – column 5 line 8, column 7 line 18-31, a machine learning trainer trains a machine learning model based on labels selected by users and updates to labels, column 9 lines 30-58, column 10 lines 61 – column 11 lines 19, confidence scores for predicted labels are calculated)
a main server programmed and operable to electronically transfer data and communications between the sub-user computing device and the production classifier, and between the production classifier and a command line interface tool; (column 8 lines 35-57, servers)
a command line interface tool comprising: a user input device for receiving user commands; (Figure 5, user interface)
a display; (column 15 lines 35-50, display to user)
and a processor framework programmed and operable to: (Figure 1 reference 102, processor)
upon receiving a user search command, generate a filtered set of unlabeled datapoints by searching and filtering the initial data set of unlabeled datapoints based on a keyword associated with each category; (column 14 line 57 – column 15 line 20, a user query specifying a data sampling criterion comprising a term is received)
generate a verified set of labeled datapoints based on a user manually reviewing and labeling the filtered set of unlabeled datapoints; (column 15 lines 21-60, the user reviewed the selected data samples)
upon receiving a user train command, train, during a first phase, a first data-labeling model based on the verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
and upon receiving a user compute command, compute a category and confidence score for each datapoint of the initial dataset based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 48, Ardila teaches the processor framework is programmed and operable to assist a user to: update the verified set of labeled datapoints by reviewing and manually labeling low-confidence datapoints having a confidence score below a minimum value; (column 11 lines 37-61, change data sampling criteria due to low score)
update the verified set of labeled datapoints by reviewing and manually labeling high-confidence datapoints having a confidence score above a threshold value; (column 15 lines 21-60, review labels)
and train, during a second phase, the first data-labeling model based on the updated verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
recompute a category and confidence score for each datapoint of the initial dataset based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 49, Ardila teaches the processor framework is programmed and operable to assist a user to: update the verified set of labeled datapoints by reviewing and manually labeling low-confidence datapoints having a confidence score below a minimum value; (column 11 lines 37-61, change data sampling criteria due to low score)
update the verified set of labeled datapoints by reviewing and manually labeling high-confidence datapoints having a confidence score above a threshold value; (column 15 lines 21-60, review labels)
train, during a third phase, the first data-labeling model based on the updated verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
and recompute a category and confidence score for each datapoint of the initial dataset of datapoints based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 50, Ardila teaches the processor framework is programmed and operable to assist a user to: train a second label-assist model based on the verified set of labeled datapoints; (column 5 line 50 – column 6 line 10, second label)
compute a category and confidence score for each datapoint of the initial dataset based on the second data-labeling model; (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
and compare, for each datapoint, the category and confidence score of the first label-assist model and second label-assist model; (column 4 lines 26-53, matches)
and update the verified set of labeled datapoints based on a user manually reviewing and labeling datapoints in which the category computed by the first label-assist model conflicts with the category computed by the second label-assist model. (column 15 lines 21-60, the user reviewed the selected data samples)
Claim Rejections - 35 USC § 103
14. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
15. Claim(s) 31-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ardila et al. (US Patent 12,271,443 B1) in view of Wang et al (US Publication 2024/0112014 A1).
As per claim 31, Ardila teaches A computer-implemented method for classifying a new datapoint into a category comprising: (see Abstract)
generating, on a server, unlabeled datapoints (column 5 lines 23-50, column 8 line 58 – column 9 line 6, unlabeled data samples are generated and retrieved)
aggregating, on the server, the unlabeled datapoints from the generating step into an initial dataset of unlabeled datapoints; (column 7 lines 8-17, column 10 lines 10-60, at least one unlabeled data samples from a set of unlabeled data samples are selected for labeling)
providing a data labeling-assist module programmed and operable to interface with a user to transform the initial dataset of unlabeled datapoints into a preprocessed dataset of labeled datapoints; (column 7 lines 18-31, column 15 line 61 – column 16 line 4, a labeling engine determines labels to be associated with selected data samples to be presented to the user for evaluation)
training a classifier production model based on the pre-processed set of labeled datapoints; (column 4 lines 25 – column 5 line 8, column 7 line 18-31, a machine learning trainer trains a machine learning model based on labels selected by users and updates to labels)
receiving the new datapoint; (column 5 lines 23-50, column 7 lines 32-46, a supplementing engine selects additional unlabeled data samples)
computing a confidence score for the new datapoint based on the classifier production model; (column 9 lines 30-58, column 10 lines 61 – column 11 lines 19, confidence scores for predicted labels are calculated)
and assigning a label to the new datapoint based on the confidence score. (column 13 lines 45 – column 14 line 10, confidence levels of labels are utilized for labeling)
Ardila does not explicitly indicate detecting entity behaviors from input computing devices; generating, on a server, unlabeled datapoints based on the entity behaviors.
Wang teaches detecting entity behaviors from input computing devices; generating, on a server, unlabeled datapoints based on the entity behaviors. (paragraph 0021, 0031, 0043, 0069, a named entity recognizer model is utilized for labeling rules, entity recognition interpreted as detecting entity behaviors)
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Ardila’s method of labeling unlabeled data sets based on a machine learning model with Wang’s ability to perform entity recognition for labeling. This gives the user the ability to utilize entity recognition of received data samples that can be utilized in labeling data samples. The motivation for doing so would be to better perform data annotation and training (paragraph 0003).
As per claim 32, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to: retrieve the initial dataset; (column 5 lines 23-50, column 8 line 58 – column 9 line 6, retrieve unlabeled data samples)
generate a filtered set of unlabeled datapoints by searching and filtering the initial dataset of unlabeled datapoints based on a keyword associated with each category; (column 14 line 57 – column 15 line 20, a user query specifying a data sampling criterion comprising a term is received)
generate a verified set of labeled datapoints by manually reviewing and labeling the filtered set of unlabeled datapoints; (column 15 lines 21-60, the user reviewed the selected data samples)
train, during a first phase, a first data-labeling model based on the verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
compute a category and confidence score for each datapoint of the initial dataset of datapoints based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 33, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to: update the verified set of labeled datapoints by reviewing and manually labeling low-confidence datapoints having a confidence score below a minimum value; (column 11 lines 37-61, change data sampling criteria due to low score)
update the verified set of labeled datapoints by reviewing and manually labeling high-confidence datapoints having a confidence score above a threshold value; (column 15 lines 21-60, review labels)
and train, during a second phase, the first data-labeling model based on the updated verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
recompute a category and confidence score for each datapoint of the initial dataset of datapoints based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 34, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to: update the verified set of labeled datapoints by reviewing and manually labeling low-confidence datapoints having a confidence score below a minimum value; (column 11 lines 37-61, change data sampling criteria due to low score)
update the verified set of labeled datapoints by reviewing and manually labeling high-confidence datapoints having a confidence score above a threshold value; (column 15 lines 21-60, review labels)
train, during a third phase, the first data-labeling model based on the updated verified set of labeled datapoints; (column 14 lines 42-54, training and retraining of the machine learning model is performed)
and recompute a category and confidence score for each datapoint of the initial dataset of datapoints based on the first data-labeling model. (column 6 lines 26-50, classification, column 13 line 45 – column 14 line 10, confidence scores)
As per claim 35, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to: repeat the updating, training, and computing steps. (column 4 lines 26-54, repeat process)
As per claim 36, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to save to a database storage the labeled datapoints as the preprocessed dataset of labeled datapoints. (column 9 lines 30-58, storing predictions and labels)
As per claim 37, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to export the preprocessed dataset of labeled datapoints with confidence scores. (column 13 line 45 – column 14 line 10, confidence scores)
As per claim 38, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to compute the number of the number of datapoints for each label. (column 5 lines 51 – column 6 lien 10, number of data samples associated with labels)
As per claim 39, Ardila teaches the data labeling-assist module is programmed and operable to assist a user to identify datapoints in which the predicted label does not match the verified label. (column 4 lines 26-53, do not match)
As per claim 40, Ardila teaches the data labeling-assist module is programmed and operable to prompt the user for selecting a second data-labeling model, and assist the user to train the second data-labeling model, and wherein the preprocessed dataset of labeled datapoints is based on the first and second data-labeling models. (column 5 line 50 – column 6 line 10, second label)
As per claim 41, Ardila teaches the label is one selected from SHAFT and cannabis-related. (column 6 lines 26-50, label consistency)
As per claim 42, Ardila teaches the detecting step comprises detecting an act of registration or purchase.(column 6 line 51 – column 7 line 8, sampling objective)
As per claim 43, Ardila teaches the generating step comprises scraping a website for text. (column 8 line 58 – column 9 line 6, samples include text)
As per claim 44, Ardila teaches the aggregating step comprises arranging the unlabeled datapoints into a multirow and column csv file. (column 8 line 58 – column 9 line 6, samples)
As per claim 45, Ardila teaches each of the retrieve, generate a filtered set of unlabeled datapoints, train, and compute functions are command line interface commands. (column 4 lines 26-53, trainer)
As per claim 46, Ardila teaches the providing step is implemented on at least one server. (column 8 lines 35-57, servers)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhattacharya (US Publication 2024/0289354 A1)
Ramakrshna (US Patent 11,978,438 B1)
Winstead (US Patent 11,977,964 B1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANGELINO N GORTAYO whose telephone number is (571)272-7204. The examiner can normally be reached Monday-Friday 7:00am - 3:30pm.
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/DANGELINO N GORTAYO/Primary Examiner, Art Unit 2168