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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per Claim 1 (and similarly claims 9 and 17):
The original Specification (i.e. the original Specification of Parent Application 17/903,798 and Provisional Application 63/352,110, hereafter original Specification, where this application is a continuation, and not a continuation-in-part) does not have written description for training a general synthetic data generation model using the general training data set; generating general synthetic data using the trained general synthetic data generation model; and training a machine learning model directed to the particular task using the general synthetic data and the task-specific training data set (col. 3, lines 2-25 of Parent Patent 1 [US 12,242,568] describes where general data points and task-specific data points are obtained/”extracted” from different data sources [such that the “extracting” steps of claim 1 are supported]; col. 3, lines 26-34 of Parent Patent 1 describe data generation model training embodiments that solely use general data points or use little or none of the task-specific data points, but the original Specification does not specifically describe where the data generation model is general, and it is not implied/inherent/obvious for a model generated using general data to also be general because, in the original Specification [particularly claim 7 incorporating the limitations of parent claims 1-4 and 6] describe where a classifier directed to a task is trained using synthetic data generated by a synthetic data generation model that is trained using general first training data, where, if the synthetic data is assumed to be general because it is generated by a model that is assumed to be general because the model is trained using general data, then general synthetic data is used to train a task-specific classifier [meaning that general data is not exclusively used to train general models/classifiers and therefore the data generation model cannot be assumed to be general just because it is trained using general data]; col. 3, lines 35-49 also seem to describe where generated synthetic data is tasks-specific [since the generated synthetic example data points resemble task-specific data] which at least suggests that the synthetic data in the original Specification is not general synthetic data)
It is also not clear where the original Specification supports claims 6, 13, and 19.
The dependent claims include the issues of their respective parent claims.
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.
Claim 19 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 19 seems like it is intended to be a system version of a combination of claims 5-6 but is a “run-on sentence” since there is no semicolon followed by “and” at the end of line 3 of claim 19.
Amending “training data set” in line 3 of claim 19 to recite –training data set; and—would suffice to resolve this issue, but Applicant can also, at Applicant’s discretion, amend lines 2-3 of claim 19 to match the phrasing of claim 5 (amend “excludes any content of the” to just –excludes the--) and amend line 4 of claim 19 to match the phrasing of claim 6 (amend “excludes the” to “excludes any content of the”) for language consistency.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
As per Claim(s) 1 (and similarly claim[s] 9 and 17, and consequently claim[s] 2-8, 10-16, and 18-20 which depend on claim[s] 1, 9, and 17), the prior art of record does not teach or suggest the combination of all limitations in claim(s) 1, including (i.e. in combination with the remaining limitations in claim[s] 1) One or more non-transitory computer-readable media storing program instructions that, when executed by one or more hardware processors, cause performance of operations comprising: extracting a task-specific training data set from a first data source comprising data that is specific to a particular task; extracting a general training data set from a second data source comprising data that is not specific to the particular task; training a general synthetic data generation model using the general training data set; generating general synthetic data using the trained general synthetic data generation model; and training a machine learning model directed to the particular task using the general synthetic data and the task-specific training data set.
2022/0084510 teaches “In many cases, insufficient human-labeled training examples are readily available for training task-adapted language understanding models. In other words, a model trained using only the available examples will likely exhibit poor performance when employed for the corresponding task. The disclosed implementations offer an approach for using generative models to create synthetic task-specific training examples that can be used in place of, or in addition to, training examples created by actual users. For the purposes of this document, the term “synthetic” means at least partly machine-generated. Using a generative model to generate training data for a natural language understanding model, as described herein, can provide large quantities of suitable training data at relatively low cost, because the synthetic training examples do not need to be labeled by human users” (paragraph 24). Paragraph 86 describes using model-generated synthetic examples to train an NLU model. The synthetic training examples in this reference appear to be task-specific and not general.
2023/0342670 teaches “training data for a task specific model may be generated by a general purpose model, such as a zero-shot model” (paragraph 15) and “In operation, an administrator associated with a user or provider may interact with the administration API 210 in order to provide various parameters 208 for development and deployment of one or more new machine learning systems, such as conversational AI systems, among other options. The parameters 208 may, in at least one embodiment, include user defined classes that are associated with one or more zero shot models, such as the model 212. The model 212 may then be associated with a machine learning platform 202 that may be receive queries 206 through an inference API 204. The model 212, which as noted above may be a general purpose model, may provide responses to the queries 206, while those responses and their associated queries are saved. For example, inference requests (e.g., queries 206) are received and labels are looked up for the model, where the labels may form a part of the parameters 208. In at least one embodiment, zero shot classification is performed in order to generate responses to the requests. As will be described herein, once a threshold number of responses and queries are stored within the query and result store 214 or a threshold period of time has elapsed, the information within the query and result store 214 may be used, at least in part, to train a smaller, lighter task specific model for use with the machine learning platform 202” (paragraph 30). This reference does not appear to describe where the zero shot/general models are trained using a general training data set extracted from a data source that is not a data source from which task-specific training data is extracted (the zero-shot/general-purpose model is only described as being trained on a “large data set” without specifying where the large data set came from), and does not appear to describe where the task-specific model is also trained based on the task-specific training data.
Double Patenting
For clarity of the record, NO Double Patenting rejections are required between the claims of this application and the claims of Parent Patent 1 (US 12,242,568) because the claims of Parent Patent 1 do not teach or suggest at least extracting a task-specific training data set from a first data source comprising data that is specific to a particular task (claim 3 of Parent Patent 1 does not teach or suggest where the second task-specific training data is extracted from anything or where anything is extracted from the second task-specific training data) training a general synthetic data generation model using the general training data set; generating general synthetic data using the trained general synthetic data generation model; (Claims 1, 2, and 4 of Parent Patent 1 do not specifically teach or suggest where either of the synthetic data generation model or the synthetic data generated by the synthetic generation are general, and it is not natural/inherent/obvious for the synthetic data generation model or the synthetic data that the synthetic data generation model generates to be general just because it is trained based on general data because claim 7 [interpreted as incorporating the limitations of claims 1-4 and 6] describes where a task-specific classifier is trained using the synthetic data [where, if the synthetic data is assumed to be general because it was generated by a model trained on general data, means that general data is used to train a task-specific classifier, such that models/classifiers trained using general data are not necessarily general]) and training a machine learning model directed to the particular task using the general synthetic data and the task-specific training data set (the task-specific classifier is claimed as being trained based on the synthetic data [which may be suggested to be general] but not also based on the task-specific training data set)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC YEN whose telephone number is (571)272-4249. The examiner can normally be reached M-F 12:00PM -8:30PM EST.
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.
EY 10/14/2026
/ERIC YEN/Primary Examiner, Art Unit 2658
Prosecution Insights