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 § 103
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hou et al. (US 2022/0383078 A1), hereinafter “Hou”, and in view of Chen et al. (US 2024/0274286 A1), hereinafter “Chen”.
As per claim 1, Hou teaches a method for configuring a machine learning model comprising:
“selecting a head from a plurality of stored head, responsive to an input, to implement a layer in a transformer machine learning model” at [0492]-[0501] and Figs. 10-14;
(Hou teaches calculating importance of each attention head in the transformer layer of the first neural network model and selecting an attention head with higher importance as an attention head in the transformer layer of the second neural network model)
“copy the selected head from persistent storage to active memory” at [0492]-[0501], [0534]-[0544] and Figs. 10-14;
(Hou teaches copying the selected head from the first neural network to the second neural network and sending the second neural network model to the terminal device)
Hou does not explicitly teach “executing the layer in the transformer machine learning model on the input using the selected head to generate an output, performing an action responsive to the output” as claimed. However, Chen teaches a method for receiving patient’s data as input and predicting clinical outcome using transformer layers comprising multi-head attention layers comprising, including the steps of “executing the layer in the transformer machine learning model on the input using the selected head to generate an output, performing an action responsive to the output” at [0003]-[0004].
Thus, it would have been obvious to one of ordinary skill in the art to combine Chen with Hou’s teaching by using the transformer machine learning model to predict clinical outcome because “the outcomes predicted by clinical prediction models provide an ability to infer accurate treatment responses and disease progress forecasts based on clinical features and a molecular profiles of the patients. In turn, professionals can develop precises medications for eventual use in the medical decision-making process in order to provide personalized treatment decisions for treating a patient’s medical condition while considering outcome risks given the patient’s clinical features and molecular profile”, as suggested by Hou at [0026].
As per claim 2, Hou and Chen teach the method of claim 1 discussed above. Hou also teaches: wherein “selecting the head includes processing the input with a policy network that generates a score for each of the plurality of stored heads and selecting a head from the plurality of stored heads having highest scores” at [0492]-[0501] and Fig. 10;
As per claim 3, Hou and Chen teach the method of claim 2 discussed above. Hou also teaches: wherein “the generated scores indicate expected performance for the respective plurality of stored heads” at [0492]-[0501] and Fig. 10;
As per claim 4, Hou and Chen teach the method of claim 2 discussed above. Hou also teaches: wherein “the policy network is implemented as a linear neural network layer” at [0462]-[0465] and Fig. 9.
As per claim 5, Hou and Chen teach the method of claim 1 discussed above. Hou also teaches: wherein “selecting the head includes determining a weight attention matrix based on a dot product between embeddings of the plurality of stored heads and an embedding of the input” at [0424]-[0436], [0460]-[0465], [0495]-[0506].
As per claim 6 Hou and Chen teach the method of claim 5 discussed above. Hou also teaches: wherein “selecting the head further includes pooling the weight attention matrix to generate a strength of activation for each of the plurality of stored heads” at [0424]-[0436], [0460]-[0465], [0495]-[0506].
As per claim 7, Hou and Chen teach the method of claim 5 discussed above. Hou also teaches: wherein “selecting the head includes generating head weights for each of the plurality of stored heads by multiplying stored weights in a corresponding head group by attention weights and summing a result” at [0424]-[0436], [0460]-[0465], [0495]-[0506].
As per claim 8, Hou and Chen teach the method of claim 1 discussed above. Chen also teaches: wherein “the input includes patient medical information and wherein the output includes a prediction of a disease to add in medical decision making” [0031]-[0035].
As per claim 9, Hou and Chen teach the method of claim 8 discussed above. Chen also teaches: wherein “the patient medical information includes the patient’s medical history and an image of a tissue sample” at [0031]-[0035].
As per claim 10, Hou and Chen teach the method of claim 1 discussed above. Chen also teaches: wherein “the action includes automatically altering a patient’s treatment” at [0002].
Claims 11-20 recite similar limitations as in claims 1-10 and are therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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, Sanjiv Shah can be reached at (571)272-4098. 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.
/KHANH B PHAM/Primary Examiner, Art Unit 2166
July 21, 2026
Prosecution Insights