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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 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, 14, 17, 19 and 20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Wang et al. (U.S. Patent Application Publication No. US 2020/0293828 A1) (hereafter referred to as “Wang”).
With regard to claim 1, Wang describes setting a parameter of a first model and a parameter of a second model based on a pre-trained model (refer for example to paragraphs [0114] and [0531]); learning the second model by performing a predetermined task on a source domain model (refer for example to paragraph [0078]); estimating an unobservable gradient for model updates on an unseen domain (refer for example to paragraph [0089]) based on the parameter of the first model and the parameter of the second model (refer for example to paragraphs [0061], [0065], [0114] and [0531]); and updating the first model based on the estimated unobservable gradient (refer for example to paragraph [0531]).
As to claim 14, Wang describes a non-transitory computer-readable medium including instructions, that when executed by a processor, perform a process (see Figure 18 and refer for example to paragraphs [0288] and [0293]) for domain generalization of a machine learning model, wherein the process comprises setting a parameter of a first model and a parameter of a second model based on a pre-trained model (refer for example to paragraphs [0114] and [0531]); learning the second model by performing a predetermined task on a source domain (refer for example to paragraph [0078]); estimating an unobservable gradient for model updates on an unseen domain refer for example to paragraph [0089]) based on the parameter of the first model and the parameter of the second model (refer for example to paragraphs [0061], [0065], [0114] and [0531]); and updating the first model based on the estimated unobservable gradient (refer for example to paragraph [0531]).
As to claim 17, Wang describes a processor, memory accessible by the processor and instructions stored in the memory that when read by the processor direct the processor (see Figure 18 and refer for example to paragraphs [0288] and [0293]) to set a parameter of a first model and a parameter of a second model based on a pre-trained model (refer for example to paragraphs [0114] and [0531]); learn the second model by performing a predetermined task on a source domain (refer for example to paragraph [0078]); estimate an unobservable gradient for model updates on an unseen domain refer for example to paragraph [0089]) based on the parameter of the first model and the parameter of the second model (refer for example to paragraphs [0061], [0065], [0114] and [0531]); and update the first model based on the estimated unobservable gradient (refer for example to paragraph [0531]).
With regard to claim 19, Wang describes a processor, memory accessible by the processor, and instructions stored in the memory that when read by the processor direct the processor (see Figure 18 and refer for example to paragraphs [0288] and [0293]) to retrieve a first model and a second model (refer for example to paragraphs [0114] and [0531]); learn the second model by classifying data of a first domain related to a first service (refer for example to paragraph [0078]); estimate an unobservable gradient (refer for example to paragraph [0089]) based on a parameter of the first model and a parameter of the second model (refer for example to paragraphs [0061], [0065], [0114] and [0531]); update the first model based on the estimated unobservable gradient (refer for example to paragraphs [0061], [0065], [0114] and [0531]); and classify data of a second domain related to a second service by using the updated first model (refer for example to paragraphs [0063] and [0086]).
As to claim 20, Wang describes retrieving a first model and a second model (refer for example to paragraphs [0114] and [0531]); learning the second model by classifying data of a first domain related to a first service (refer for example to paragraph [0078]); estimating an unobservable gradient (refer for example to paragraph [0089]) based on a parameter of the first model and a parameter of the second model (refer for example to paragraphs [0061], [0065], [0114] and [0531]); updating the first model based on the estimated unobservable gradient (refer for example to paragraph [0531]); and classifying data of a second domain related to a second service by using the updated first model (refer for example to paragraphs [0063] and [0086]).
Allowable Subject Matter
Claims 2-13, 15-16 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu, Sawada, Han, Amma, Tsai, Segu, Karlinsky, Arpit, Mangla, Sultana, Chiu, Ansari, Liu, Wu and Ran all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
November 19, 2025