Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
2. Claims 4, 15 and 22 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.
Response to Arguments
3. Applicant’s arguments with respect to claim 1, 12 and 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
4. 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 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.
5. Claims 1-3, 5, 12-14, 16, 19-21 and 23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Khandelwal et al. “Nearest Neighbor Machine Translation”, herein Khandelwal.
Regarding Claim 1:
Khandelwal discloses a method of information processing, comprising:
obtaining a first hidden state vector obtained by inputting information to be translated that is expressed in a source language into a pre-trained first translation model, and a first probability distribution that the first hidden state vector is predicted as respective words in a predetermined vocabulary of a target language (Khandelwal: Section 2, discloses the input is the source sentence + previous target tokens, the model is pre-trained, a decoder intermediate vector is output before the output layer, this is made clear under the Datastore creation heading when Khandelwal specifically states: “The key is a high-dimensional representation of the entire translation context computed by the MT decoder, f(s, t1:i−1);” further discloses “At test time, given a source x, the model outputs a distribution over the vocabulary pMT (yi |x, yˆ1:i−1) for the target yi at every step of generation,” i.e., obtains a probability distribution over a predetermined target vocabulary);
obtaining, from a vector index library of the target language, at least one target index term that satisfies a predetermined condition with the first hidden state vector, the target index term comprising a second hidden state vector retrieved based on similarity to the first hidden state vector (Khandelwal: Section 1, discloses datastore keys are decoder hidden representation, i.e., “corresponding target tokens for every example in the parallel data”);
determining a second probability distribution that the second hidden state vector is predicted as the respective words in the predetermined vocabulary using the same output layer as the first translation model (Khandelwal: Section 2, Generation; Section 5, SoftMax temperature, disclose each retrieved vector (second hidden state vector) has: an associated target vocabulary word, distances are converted to unnormalized probabilities and these are aggregated and normalized to produce a probability distribution over the nearest neighbor probability);
fusing the first and second probability distributions to obtain a fused probability distribution (Khandelwal: Section 2 discloses “interpolating with the base model distribution… the model and kNN distributions are interpolated with a tuned parameter;” linear interpolation is a fusing, both distributions are over the same predetermined vocabulary)
returning the fused probability distribution to the first translation model to enable the first translation model to determine a translation result according to the fusion probability distribution (Khandelwal: Section 2 discloses returning the final kNN-MT distribution).
Regarding Claim 2:
Khandelwal further discloses the method of claim 1, wherein the fusing the first and second probability distributions to obtain a fused probability distribution comprises:
determining, with a pre-trained fusion proportion determination model, a first and a second fusion proportions corresponding to the first and second probability distributions respectively (Khandelwal: Section 2, Generation, kNN-LM explicitly learns and tunes
PNG
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λ, the interpolation weight between the base model probability distribution and the nearest neighbor probability.);
and fusing the first and second probability distributions according to the first and second fusion proportions to obtain the fused probability distribution (Khandelwal: Section 2, teaches fusion proportions or respective probability distributions using a trained parameter λ and fusing the distributions according to the determined proportions).
Regarding Claim 3:
Khandelwal further discloses the method of claim 1, wherein the fusing the first and second probability distributions to obtain a fusion probability distribution comprises:
determining a sum of a product of the first probability distribution and the first fusion proportion and a product of the second probability and the second fusion proportion as the fused probability distribution (Khandelwal: Equation (3) within Section 2 is a product of distribution by proportion, Sum of the two products and results in a fused probability distribution: p(yi |x, yˆ1:i−1) = λ pkNN(yi |x, yˆ1:i−1) + (1 − λ) pMT(yi |x, yˆ1:i−1)).
Regarding Claim 5:
Khandelwal further discloses the method of claim 1, wherein the vector index library is established by:
inputting a predetermined parallel corpus into a pre-trained second translation model for decoding by the second translation model, to obtain a reference hidden state vector corresponding to a plurality of morphemes of the target language in the predetermined corpus, the predetermined parallel corpus comprising predetermined corpus in the source language and predetermined corpus in the target language of synonyms (Khandelwal: Section 2 constructs the datastore offline and consists of a set of key-value pairs. Obtains a high dimensional representation (hidden state) using a machine translation decoder with two parallel language text collections and uses sentencepiece which uses subword units)
and establishing the vector index library based on a plurality of the reference hidden state vectors, wherein the second translation model is a same translation model as the first translation model and is obtained by trained using a same training scheme (Khandelwal: Section 2 discloses the overall process of creating the hidden state vectors creates an entire datastore, the model used to do this is the same exact model).
Regarding Claim 12:
Claim 12 has been analyzed with regard to claims 1 (see rejection above) and
is rejected for the same reasons of anticipation as used above
Regarding Claim 13:
Claim 13 has been analyzed with regard to claims 2 (see rejection above) and
is rejected for the same reasons of anticipation as used above
Regarding Claim 14:
Claim 14 has been analyzed with regard to claims 3 (see rejection above) and
is rejected for the same reasons of anticipation as used above.
Regarding Claim 16:
Claim 16 has been analyzed with regard to claims 5 (see rejection above) and
is rejected for the same reasons of anticipation as used above
Regarding Claim 19:
Claim 19 has been analyzed with regard to claims 1 (see rejection above) and
is rejected for the same reasons of anticipation as used above.
Regarding Claim 20
Claim 20 has been analyzed with regard to claims 2 (see rejection above) and
is rejected for the same reasons of anticipation as used above.
Regarding Claim 21:
Claim 21 has been analyzed with regard to claims 3 (see rejection above) and
is rejected for the same reasons of anticipation as used above.
Regarding Claim 23
Claim 23 has been analyzed with regard to claims 5 (see rejection above) and
is rejected for the same reasons of anticipation as used above.
Claim Rejections - 35 USC § 103
6. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 6-7, 17-18 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Khandelwal in view of Denkowski (US 10,268,684).
Regarding Claim 6:
The combination of Khandelwal further discloses the method of claim 1, wherein the obtaining a
first hidden state vector obtained by inputting information to be translated that is expressed in a source
language into a pre-trained first translation model, and the first hidden state vector being predicted as a
first probability distribution of respective words in a predetermined vocabulary comprises:
obtaining the first hidden state vector and the first probability distribution (Khandelwal: Section 2 discloses at test time, given source x, the model outputs a distribution over the vocabulary, the model also outputs a high level (hidden) representation)
Khandelwal does not explicitly disclose this process is don't with a first predetermined remote call interface.
However, Denkowski discloses this limitation: (Denkowski: discloses that its machine translation system can be deployed in a distributed computing environment, including service provider networks where tasks are executed by remote computing devices linked via communication networks. It further states that the translation module can be invoked by users, it reasonably contemplates performing translation tasks or accessing translation model functionality through remote network calls).
It would have been obvious to one of ordinary skill in the art to modify the local machine-translation method of Khandelwal so that its model outputs are obtained through a remote call interface as taught in Denkowski. Both references are within the field of machine translation and address model deployment efficiency. It is routine to expose translation or inference engines as remote services to reduce on device computation and enable distributable cloud services. One of ordinary skill in the art would recognize that using a known remote interface to access Khandelwal’s pre-trained model yields the predictable benefit of centralized model management while still returning the same hidden-state and probability outputs. The motivation for doing so would be to increase scalability by using distributed system and thereby increasing efficiency.
Regarding Claim 7:
The combination of Khandelwal further discloses the method of claim 1, wherein the obtaining, from a vector index library of a target language, at least one of target index term that satisfies a predetermined condition with the first hidden state vector comprises:
obtaining the at least one of target index term satisfies the predetermined condition with the first hidden state vector from the vector index library of the target language (Khandelwal: Section 2, the model outputs the representation which is used to query the datastore for the k nearest neighbors according to squared L2 distance)
Khandelwal does not explicitly disclose this process is don’t with a second predetermined remote call interface. However, Denkowski discloses this limitation: (Denkowski: Page 7 lines 16-19 discloses that its machine translation system can be deployed in a distributed computing environment, including service provider networks where tasks are executed by remote computing devices linked via communication networks. It further states that the translation module can be invoked by users, it reasonably contemplates performing translation tasks or accessing translation model functionality through remote network calls).
It would have been obvious to one of ordinary skill in the art to modify the local machine-translation method of Khandelwal so that its model outputs are obtained through a remote call interface as taught in Denkowski. Both references are within the field of machine translation and address model deployment efficiency. It is routine to expose translation or inference engines as remote services to reduce on device computation and enable distributable cloud services. One of ordinary skill in the art would recognize that using a known remote interface to access Khandelwal’s pre-trained model yields the predictable benefit of centralized model management while still returning the same hidden-state and probability outputs. The motivation for doing so would be to increase scalability by using distributed system and thereby increasing efficiency.
Regarding Claim 17:
Claim 17 has been analyzed with regard to claims 6 (see rejection above) and
is rejected for the same reasons of obviousness as used above.
Regarding Claim 18:
Claim 18 has been analyzed with regard to claims 7 (see rejection above) and
is rejected for the same reasons of obviousness as used above.
Regarding Claim 24:
Claim 24 has been analyzed with regard to claims 6 (see rejection above) and
is rejected for the same reasons of obviousness as used above
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IAN SCOTT MCLEAN/
Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654