DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 24 December 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “learning unit” and “combination unit” in claims 1 and 8-13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1 and 16-20 recite the limitation "the learning model" and “the another learning model”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3, 7-9, 13-15, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200090035, hereinafter referred to as Thathachar et al., in view of US 12444408, hereinafter referred to as Sainath et al.
Regarding claim 1, Thathachar et al. discloses a model generation device comprising:
a learning unit that
learns a transferable learning model (“In various embodiments, the MANN architecture is referred to as an Encoder-Decoder NTM (ED-NTM). As set out below, different types of encoders are studied in systematic manner, showing an advantage of multi-task learning in obtaining the best possible encoder. This encoder enables transfer learning to solve a suite of working memory tasks. In various embodiments, transfer learning for MANNs is provided (as opposed to tasks learned in separation). The trained models can also be applied to related ED-NTMs that are capable of handling much larger sequential inputs with appropriately large memory modules,” Thathachar et al., para [0028].), transfers a part of the learning model to another transferable learning model (“Referring to FIG. 17, a general architecture of a multi-task memory-augmented encoder-decoder according to embodiments of the present disclosure is illustrated. Given a set of tasks T ={T.sub.1, T.sub.2, . . . , T.sub.n} a multi-task memory-augmented encoder-decoder is provided for the tasks in T, which learns the neural network parameters embedded in the controllers. In various embodiments, a multi-task learning paradigm is applied,” Thathachar et al., para [0088]. The multitask memory-augmented encoder/decoder system including combinations of the encoder ANN and the decoder ANNs of the subsets of the task set tau corresponds to the “learning model”. And, Thathcachar et al., fig. 16, showing a single-task encoder/decoder built for each task T corresponds to “another learning model”.), and
learns a non-transfer portion other than a transfer portion of the another learning model (“FIG. 8 illustrates an exemplary ED-NTM model used for a reverse recall task. In this example, the encoder portion of the model is frozen. The encoder E.sup.S was pretrained on the serial recall task (D.sup.R stands for Decoder-Reverse),” Thathachar et al., para [0063]. And, “The weights are instantiated and frozen (set as non-trainable) for each encoder in all the encoder-decoders. Each of the encoder-decoders are now trained separately to obtain the parameters of the individual decoders,” Thathachar et al., para [0097]. The parameters of the individual decoder ANNs correspond to “non-transfer portion”. The encoder ANN parameters which are frozen and for which weights are instantiated in relation to each encoder ANN of all of the encoder/decoders correspond to the "transfer portion".).
However, Thathachar does not disclose a combination unit that generates a combined model in which the non-transfer portion of the another transferable learning model is combined with the learning model.
Sainath et al. is cited to disclose a combination unit that generates a combined model in which the non-transfer portion of the another transferable learning model is combined with the learning model (Sainath et al., col. 4, lines 44-56 and fig. 2.). Sainath et al. benefits Thathachar et al. by performing encoding processing of a plurality of tasks using a shared encoder so that a memory and/or calculation resources can be used more efficiently than when the processing is performed using a dedicated encoder for each task. Therefore, a person skilled in the art could easily provide the invention disclosed in Thathachar et al. with a function for coupling the decoder ANN s having the weight parameters trained to the memory of the multitask memory-augmented encoder/decoder system (in other words, to provide a function corresponding to the "coupling unit" of the present application) so that the encoder ANN is shared by the plurality of tasks.
As to claim 16, claim 16 is rejected on the same grounds as claim 1. And, Thathachar et al., fig. 19, teaches a computer system.
As to claim 17, claim 17 is rejected on the same grounds as claim 1. And, Thathachar et al., fig. 19, teaches a computer system.
As to claim 18, claim 18 is rejected on the same grounds as claim 1. And, Thathachar et al., fig. 19, teaches a computer system.
As to claim 19, claim 19 is rejected on the same grounds as claim 1. And, Thathachar et al., fig. 19, teaches a computer system.
As to claim 20, claim 20 is rejected on the same grounds as claim 1. And, Thathachar et al., fig. 19, teaches a computer system.
Regarding claim 3, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, wherein
the learning model and the another learning model include learning models that perform signal processing of generating target information as a target from an acoustic signal (Sainath et al., col. 4, lines 44-56.).
Regarding claim 7, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, wherein
each of the learning model and the another learning model includes a neural network (Thathachar et al., para [0088].).
Regarding claim 8, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 7, wherein
the learning unit transfers a part of an input layer side of the neural network (Thathachar et al., para [0063], [0088], and [0097].).
Regarding claim 9, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 8, wherein
the learning model includes an encoder block that projects an input to the learning model onto a predetermined space on the input layer side (Thathachar et al., para [0063], [0088], and [0097].), and
the learning unit transfers the encoder block (Thathachar et al., para [0063], [0088], and [0097].).
Regarding claim 13, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, wherein
the learning unit transfers a part of the learning model to another transferable learning model and learns a non-transfer portion other than a transfer portion of the another learning model (Thathachar et al., para [0063], [0088], and [0097].), and
the combination unit generates a new combined model obtained by combining the non-transfer portion of the another learning model with the combined model (Thathachar et al., para [0063], [0088], and [0097].).
Regarding claim 14, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, wherein
the learning model includes a learning model that performs one or more pieces of signal processing (Sainath et al., col. 4, lines 44-56 and fig. 2.).
Regarding claim 15, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, wherein
the another learning model includes a learning model that performs one or more pieces of signal processing (Sainath et al., col. 4, lines 44-56 and fig. 2.).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200090035, hereinafter referred to as Thathachar et al., in view of US 12444408, hereinafter referred to as Sainath et al., and further in view of CN 112527383, hereinafter referred to as Huang et al.
Regarding claim 10, Thathachar et al., as modified by Sainath et al., discloses the model generation device according to claim 1, but not wherein the learning unit adjusts the non-transfer portion of the combined model.
Huang et al. is cited to disclose the learning unit adjusts the non-transfer portion of the combined model (Huang et al., p. 7, highlighted section.). The inventions disclosed in Thathachar et al. and Huang et al. both relate to training of a multitask model, and therefore a person skilled in the art could easily arrive at the limitation stipulated by claim 10 by taking the above features described in Huang et al. into consideration in the invention disclosed in Thathachar et al.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200090035, hereinafter referred to as Thathachar et al., in view of US 12444408, hereinafter referred to as Sainath et al., further in view of CN 112527383, hereinafter referred to as Huang et al., and further in view of US 20200327884, hereinafter referred to as Bui et al.
Regarding claim 11, Thathachar et al., as modified by Sainath et al. and Huang et al., discloses the model generation device according to claim 10, but not wherein the learning unit adjusts a new non-transfer portion obtained by further adding another learning model to the non-transfer portion.
Bui et al. is cited to disclose the learning unit adjusts a new non-transfer portion obtained by further adding another learning model to the non-transfer portion (Bui et al., para [0142]-[0150] and figs. 7 and 8.). The inventions disclosed in Thathachar et al. and Bui et al. both relate to training of an encoder-decoder model, and therefore a person skilled in the art could easily arrive at the limitation stipulated by claim 11 by taking the above features described in Bui et al. into consideration in the invention disclosed in Thathachar et al.
Allowable Subject Matter
Claims 2, 4-6, and 12 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. None of the prior art alone or in combination suggests the limiting features of claims 2 and 4-6, a person skilled in the art would not easily conceive of these features even after considering common technical knowledge.
With respect to claims 4-6 specifically, WO 2020250797 (abstract, and paragraphs [0050]-[0053] and [0113]-[0127]) describes estimating a sound source direction and additional information using a learning model that outputs a vector representing the direction of arrival (the sound source direction) and additional information (the sound volume, etc.) of a sound in response to input of an acoustic signal. However, none of the prior art discloses or suggests the limiting features of claims 4-5 relating to the learning model and the other learning model, and a person skilled in the art would not easily conceive of these features even after considering common technical knowledge on the reference date of the present application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE L THOMAS-HOMESCU whose telephone number is (571)272-0899. The examiner can normally be reached Mon-Fri 8-6.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2656
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