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 .
DETAILED ACTION
This is Non-Final Office Action, in responses to Patent Application filed 05/10/2024. Claim(s) 1-23 are pending. Claim(s) 1, 12 and 23 is/are independent.
In addition, 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was filed 03/18/2026 is/are attached to this Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-23 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). … for generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model, the computing system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising: inputting a first segment of a sequence into a machine-learned sequence processing model, wherein the first segment comprises data associated with a sequence generation request; generating, in parallel, a plurality of candidate second segments; generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score; selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments; processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment; and returning the selected second segment and the third segment in response to the sequence generation request ... and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... a computing system configured for generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model, the computing system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising: inputting a first segment of a sequence into a machine-learned sequence processing model, wherein the first segment comprises data associated with a sequence generation request; generating, in parallel, a plurality of candidate second segments; generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score; selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments; processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment; and returning the selected second segment and the third segment in response to the sequence generation request … These limitation(s) recite mental processes and mathematical calculation...since... generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model…, in parallel.. and … generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score … [is a high level mathematical calculation(s) (see the current specifications USPGPUB 20250348728 A1, Para(s) 132-137 and Fig. 3 for this interpretations...] ... then [APPLY IT] “using a segment quality model to generate a first component score and a response quality model to generate a second component score”....Thus these limitation(s) recite mental processes and mathematical calculation(s). ..
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “A computer system” … for generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model….to generating, in parallel, a plurality of candidate second segments; generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score; selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments; processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment; and returning the selected second segment and the third segment in response to the sequence generation request ...it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application, …Also, these limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... “A computer system” .. for generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model….to generating, in parallel, a plurality of candidate second segments; generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score; selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments; processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment; and returning the selected second segment and the third segment in response to the sequence generation request ......These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-11 and 13-22 further recite, addition limitation(s) such as, (trained using segment-level feedback signals to generate scores for input segments, segment label pair, response-level label, reward or the response quality model was trained using reinforcement learning with the response-level feedback signals providing a reward, composite score, weighted combination is weighted based on an ordinal value, autoregressively generate an output segment that indicates a score, numerical digits of the score and attributes of the given input segment., etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-23 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
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(s) 1-23 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. As drafted, Claim(s) 1, 12 and 23 recite the limitation(s) said “… a machine-learned sequence processing model, … inputting a first segment of a sequence into a machine-learned sequence processing model…” (in the same claim) . There is insufficient antecedent basis for this limitation in the claim.
Dependent Claim(s) 2-11 and 13-22 were similar reject with same rationale.
Allowable Subject Matter
Claim(s) 1-23 would be allowable if rewritten and/or amending to remedy the 101 and 112 rejection(s).
Reason for Allowance
Under the broadest reasonable interpretation of the claimed limitation which is consistence with the Applicant's Specification, the prior arts of recorded when taken individually or in combination do not expressly teach or render obvious the limitations recited in claim(s) 1, 12 and 23 when taken in the context of the claims as a whole, especially the concept of, “... generation of multiple candidate segments of a multi-segment sequence using a machine-learned sequence processing model, the computing system comprising: … inputting a first segment of a sequence into a machine-learned sequence processing model, wherein the first segment comprises data associated with a sequence generation request; generating, in parallel, a plurality of candidate second segments; generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score; selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments; processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment; and returning the selected second segment and the third segment in response to the sequence generation request ...” As claimed and further supports in the specifications PGPUB 20250348728 A1- The Abstract and Para(s) [0003]-[0009], [0063]-[0072].
In addition, neither a reference uncovered that would have provided a basis of evidence for asserting a motivation, nor one of ordinary skilled in the art before the effective filing date of the claimed invention, would have combined them to arrive at the present invention as recited in the context of independent claim(s) 1 and 14 as a whole.
Thus, claim(s) 1, 12 and 23 is/are allowed over the prior arts of record. Dependent claims 2-11 and 13-22 are also allowable due to its dependency of independent claim(s) 1 and 12.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Park et al. (“US 12,664,478 B2” filed 07/07/2023, describing method for generating feedback signals for training a machine-learned agent model. The example method can include obtaining an output of a machine-learned agent model, the output including a next state feature generated by the machine-learned agent model based on a sequence of preceding states. The example method can include processing, using a machine-learned reward model, the output and the sequence of preceding states to generate a quality indicator indicating a quality of the next state feature in view of the preceding states. The machine-learned reward model could be trained by retrieving reference data from a reference data source and computing one or more quality indicators in view of a respective training input and output(s), and the reference data. The example method can include outputting the quality indicator to a model trainer for updating the machine-learned agent model … [The Abstract].
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/QUOC A TRAN/Primary Examiner, Art Unit 2145
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