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 Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 10, “an input sequence” should be changed to -- the input sequence --
Appropriate correction is required.
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.
Claims 1- 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1, the claim is considered indefinitely because it improperly mixes statutory classes by reciting both system limitations and method steps within the same claim. For example, the claim recites “a sequence-to-sequence neural network system configured to…” which is indicative of a machine claim while also reciting “the method comprising…” which is indicative of a process claim. Therefore, it is unclear whether the claim is satisfied using the recited system or upon performing the recited method steps. The inclusion of both system and method limitations in the same claim makes the scope of the claim ambiguous and it fails to provide clear boundaries of the claimed subject matter.
Claims 2 - 18 depend on claim 1 and are also rejected.
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.
Claims 1 – 15, 18, 24, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
Claims 1 - 15 and 18 are directed to a method and claim 25 is directed to a system with structural components.
Thus, claims 1 - 15, 18, and 25 falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 24 recites “one or more computer-readable storage media” that stores a software program performing a function. The specification fails to expressly limit the recited “media” to a statutory embodiment. Thus, the plain and ordinary meaning of the recited "media" includes signals, carrier waves, etc.
Accordingly, the recited “one or more computer-readable storage media” is not a process, a machine, a manufacture or a composition of matter, and claim 24 fails to recite statutory subject matter as defined in 35 U.S.C. 101.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
process the system input to generate a system output defining a next token probability distribution over possible output tokens for a next output token to extend the partial output sequence;
For example, a human reads a sentence and mentally predicts the next word by considering likely options and choosing the most probable one.
extending the initial partial output sequence by performing a look ahead tree search of possible continuations of the initial partial output sequence guided by the sequence-to-sequence neural network system, until one or more termination criteria are met.
For example, a human starts a sentence and mentally determines different ways by using a search tree that the sentence can continue by thinking a few steps ahead. And using their judgement to select the branch (continuation) of the search tree of the sentence and stopping once the sentence feels complete.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive, as a system input, i) an input sequence comprising a sequence of input tokens and ii) a partial output sequence comprising zero, one, or more output tokens;
obtaining i) an input sequence comprising the sequence of input tokens and ii) an initial partial output sequence; and
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive, as a system input, i) an input sequence comprising a sequence of input tokens and ii) a partial output sequence comprising zero, one, or more output tokens;
obtaining i) an input sequence comprising the sequence of input tokens and ii) an initial partial output sequence; and
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2,
Step 2A, Prong One
The claim recites in part:
guiding the look ahead tree search using a value neural network, wherein guiding the look ahead tree search using a value neural network comprises processing both the input sequence and a partial output sequence associated with a node of the look ahead tree search to evaluate the node by generating a value for a partial output sequence associated with the node; and guiding the look ahead tree search using the value for the partial output sequence associated with the node.
For example, a human starts a sentence and mentally determines different ways (root to the branches of a search tree) that the sentence can continue by thinking a few steps ahead. And using a ranking system to select the best continuation (branch of search tree) of the sentence based on the highest ranked continuation.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim is directed to the same abstract idea identified in claim 2 above.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the sequence-to-sequence neural network system and the value neural network have each been trained, using training data pairs comprising a training input sequence and a training output sequence, to optimize a respective sequence transduction metric, and wherein a sequence transduction metric for the sequence-to-sequence neural network system and a sequence transduction metric for the value neural network are different
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation sequence transduction metric amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the sequence-to-sequence neural network system and the value neural network have each been trained, using training data pairs comprising a training input sequence and a training output sequence, to optimize a respective sequence transduction metric, and wherein a sequence transduction metric for the sequence-to-sequence neural network system and a sequence transduction metric for the value neural network are different
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation sequence transduction metric amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 4,
Step 2A, Prong One
The claim recites in part:
wherein a root node of a search tree for the look ahead tree search represents the initial partial output sequence, wherein edges to child nodes on a path from the root node each represent a candidate continuation of the initial partial output sequence; and
wherein performing the look ahead tree search guided by the sequence-to-sequence neural network system comprises, for child nodes of the search tree:
processing the sequence of input tokens, the initial partial output sequence, and the candidate continuation of the initial partial output sequence, using the sequence-to-sequence neural network system, to define a next token probability distribution over possible output tokens for a next output token for extending the candidate continuation of the initial partial output sequence;
using the next token probability distribution to expand the search tree.
For example, a human starts a sentence and mentally predicts different ways by using a search tree that the sentence can continue, evaluate their likelihood, and continuing with the most likely branch (or option)..
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 5,
Step 2A, Prong One
The claim recites in part:
performing the look ahead tree search of possible continuations of the partial output sequence guided by a value neural network,
wherein the value neural network is configured to process at least a partial output sequence to generate a value for the partial output sequence, and
wherein performing the look ahead tree search guided by a value neural network comprises:
evaluating candidate continuations of the initial partial output sequence, represented by nodes of the look ahead tree search, by processing the candidate continuation of the initial partial output sequence represented by a node using the value neural network to determine a value for the node.
For example, a human considers several possible ways to finish a sentence using a search tree, mentally scores each option based on how good it sounds, and choose the best branch (continuation).
For example, a human starts a sentence and mentally determines different ways by using a search tree that the sentence can continue by thinking a few steps ahead. And using their judgement to select the branch (continuation) of the search tree of the sentence and stopping once the sentence feels complete.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 6,
Step 2A, Prong One
The claim recites in part:
wherein the value neural network is configured to process a combination of the input sequence and the partial output sequence;
wherein determining the value for a node comprises a combination of the input sequence and the candidate continuation of the initial partial output sequence represented by the node.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds best based on context/experience.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 7,
Step 2A, Prong One
The claim recites in part:
wherein the value neural network is configured to process a combination of the input sequence and the partial output sequence;
wherein determining the value for a node comprises a combination of the input sequence and the candidate continuation of the initial partial output sequence represented by the node.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it, and scores then ranks which completion sounds best based on context/experience.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 8,
Step 2A, Prong One
The claim recites in part:
selecting one of the possible continuations of the initial partial output sequence using the look ahead tree search;
extending the initial partial output sequence using the selected possible continuation to generate an extended partial output sequence;
extending the extended partial output sequence by performing another look ahead tree search of possible continuations of the extended partial output sequence guided by the sequence-to-sequence neural network system.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds best.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 8,
Step 2A, Prong One
The claim recites in part:
selecting one of the possible continuations of the initial partial output sequence using the look ahead tree search;
extending the initial partial output sequence using the selected possible continuation to generate an extended partial output sequence;
extending the extended partial output sequence by performing another look ahead tree search of possible continuations of the extended partial output sequence guided by the sequence-to-sequence neural network system.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds best.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 9,
Step 2A, Prong One
The claim recites in part:
iteratively extending the extended partial output sequence by performing look ahead tree searches, until a complete version of the output sequence is generated.
For example, a human starts a sentence and mentally determines different ways that the sentence can continue by thinking a few steps ahead using a tree search, And using their judgement to select the best branch (continuation) of the sentence and stopping once the sentence feels complete.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 10,
Step 2A, Prong One
The claim recites in part:
wherein extending the initial partial output sequence by performing a look ahead tree search of possible continuations of the initial partial output sequence comprises:
generating a search tree probability distribution over the possible continuations of the initial partial output sequence using the look ahead tree search;
selecting a continuation of the initial partial output sequence from the possible continuations using the search tree probability distribution.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds best.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 11,
Step 2A, Prong One
The claim recites in part:
wherein extending the initial partial output sequence by performing a look ahead tree search of possible continuations of the initial partial output sequence comprises:
generating a search tree probability distribution over the possible continuations of the initial partial output sequence using the look ahead tree search;
selecting a continuation of the initial partial output sequence from the possible continuations using the search tree probability distribution.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) is the most probable.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 11,
Step 2A, Prong One
The claim recites in part:
wherein extending the initial partial output sequence by performing a look ahead tree search of possible continuations of the initial partial output sequence comprises:
generating a search tree probability distribution over the possible continuations of the initial partial output sequence using the look ahead tree search;
selecting a continuation of the initial partial output sequence from the possible continuations using the search tree probability distribution.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) is the most probable.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 12,
Step 2A, Prong One
The claim recites in part:
wherein extending the initial partial output sequence by performing a look ahead tree search of possible continuations of the initial partial output sequence comprises:
generating a search tree probability distribution over the possible continuations of the initial partial output sequence using the look ahead tree search;
selecting a continuation of the initial partial output sequence from the possible continuations using the search tree probability distribution.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) is the most probable.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 12,
Step 2A, Prong One
The claim recites in part:
performing the look ahead tree search of possible continuations of the partial output sequence guided by a value neural network, and wherein using the next token probability distribution to expand the search tree comprises:
traversing the search tree from the root node until a leaf node is encountered;
expanding the leaf node by creating at least one new child node for the leaf node, wherein the new child node represents a candidate extension of the candidate continuation of the initial partial output sequence;
determining edge data for a new edge between the leaf node and the new child node by using the next token probability distribution to determine an action score for the new edge;
evaluating the leaf node by processing the candidate continuation of the initial partial
output sequence using the value neural network to determine a leaf node value.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) is the most probable.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 13,
Step 2A, Prong One
The claim recites in part:
further comprising updating the action scores for edges between the leaf node and the root node traversed during the search, using the leaf node value
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds the best and updates the scores.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 14,
Step 2A, Prong One
The claim recites in part:
updating the action score for an edge comprise setting the action score to a value determined by a maximum value amongst tree searches involving the edge performed during the look ahead tree search.
For example, a human reads the beginning of a sentence, then mentally tries possible ways to finish it using a search tree, and scores then ranks which branch (completion) sounds the best and updates the scores.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 15,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
Processing the system input to generate a system output using the sequence-to-sequence neural network system comprises:
processing the system input using an encoder neural network system including a transformer neural network subsystem to generate a latent representation of the system input, and
processing a combination of the latent representation of the system input and the partial output sequence using a decoder neural network system including a transformer neural network subsystem to generate the system output.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
Processing the system input to generate a system output using the sequence-to-sequence neural network system comprises:
processing the system input using an encoder neural network system including a transformer neural network subsystem to generate a latent representation of the system input, and
processing a combination of the latent representation of the system input and the partial output sequence using a decoder neural network system including a transformer neural network subsystem to generate the system output.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 18,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
In addition, the recitation of “the input tokens and the output tokens each represent words or wordpieces in a natural language” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In addition, the recitation of the “input tokens and the output tokens each represent words or wordpieces in a natural language” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 24 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites one or more computer-readable storage media and one or more computers which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 25 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites one or more computers and one or more storage devices which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
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.
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.
Claim(s) 1, 4, 7, 18, 24, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Celikyilmaz et al (US 2019/0287012) in view of Niu et al (US 2020/0364299).
As to claim 1, Celikyilmaz et al teaches a computer-implemented method of generating an output sequence from an input sequence using a sequence-to-sequence neural network system (paragraph [0005]…systems, methods, and computer-program products for training the encoder-decoder neural network, and using the trained network, for a variety of sequence-to-sequence mapping tasks, including, without limitation, abstractive summarization),
wherein the sequence-to-sequence neural network system is configured to:
receive, as a system input, i) an input sequence comprising a sequence of input tokens and ii) a partial output sequence comprising zero, one, or more output tokens (paragraph [0026]… The encoder-decoder neural network 100 includes, in its encoder layer 102, a plurality of multi-layer encoder agents 104, 105, 106, each taking a portion of the input as an input sequence ; paragraph [0027]… The encoder agents 104, 105, 106 exchange messages 108, depicted in FIG. 1 by dashed arrows, with one another. These messages may take the form of vectors computed from hidden-state output of one or more layers of the respective sending encoder agents and fed as input into one or more layers of the respective receiving encoder agents. For a given pair of a sending agent and a receiving agent, multiple messages (e.g., as explained below with reference to FIG. 2), e.g., corresponding to the outputs at multiple of the layers within the sending encoder agent, may be transmitted. Alternatively, the outputs from multiple layers within the sending encoder agent may be combined into a single message. Further, the message computed from a given layer need not be based on the entirety of the hidden-state output of the layer, which is generally a sequence of hidden-state output vectors corresponding to the tokens in the input sequence, but may generally be computed from any combination of these hidden-state output vectors (such as, e.g., from a single hidden-state output vector), and this combination may be fixed for a given neural network or may be learned during network training)(Examiner’s Note: “encoder agents 104, 105, 106, each taking a portion of the input as an input sequence” reads on “receive, as a system input, i) an input sequence” ; “which is generally a sequence of hidden-state output vectors corresponding to the tokens in the input sequence” reads on “a sequence of input tokens” ; “exchange messages 108, depicted in FIG. 1 by dashed arrows, with one another. These messages may take the form of vectors computed from hidden-state output of one or more layers of the respective sending encoder agents and fed as input into one or more layers of the respective receiving encoder agents” reads on “a partial output sequence comprising zero, one, or more output tokens”); and
process the system input to generate a system output defining a next token probability distribution over possible output tokens for a next output token to extend the partial output sequence (paragraph [0050]… the vocabulary distributions output by the neural-network decoder 112, the decoder component 506 determines the output sequence. For this purpose, the decoder component 506 may employ a greedy decoding algorithm, which selects, for each token of the output sequence, the most probable label from the probability distribution over the vocabulary (e.g., in embodiments utilizing pointer networks, the extended vocabulary). More generally, the decoder component 506 may employ a beam search algorithm, which iteratively generates a tree structure of possible partial output sequences)(Examiner’s Note: “the decoder component 112 may employ a greedy decoding algorithm, which selects, for each token of the output sequence, the most probable label from the probability distribution over the vocabulary” reads on “process the system input to generate a system output defining a next token probability distribution over possible output tokens for a next output token” ; “the decoder component 112 may employ a beam search algorithm, which iteratively generates a tree structure of possible partial output sequences. In each iteration, the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token” reads on “to extend the partial output sequence”);
the method comprising:
obtaining i) an input sequence comprising the sequence of input tokens and ii) an initial partial output sequence(paragraph [0050]… In each iteration, the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token, and retains only the b most probable extended partial output sequences, where b is known as the beam width. For a beam width of b=1, the beam search algorithm reduces to greedy decoding.)(Examiner’s Note: “previously generated partial output sequences” reads on ”an initial output sequence”); and
extending the initial partial output sequence by performing a tree search of possible continuations of the initial partial output sequence guided by the sequence-to-sequence neural network system, until one or more termination criteria are met (paragraph [0050]…the decoder component 506 may employ a beam search algorithm, which iteratively generates a tree structure of possible partial output sequences. In each iteration, the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token, and retains only the b most probable extended partial output sequences, where b is known as the beam width. For a beam width of b=1, the beam search algorithm reduces to greedy decoding. Beam search algorithms are well-known to those of ordinary skill in the art, as are several alternative decoding methods)(Examiner’s Note: “the decoder component 506 may employ a beam search algorithm, which iteratively generates a tree structure of possible partial output sequences. In each iteration, the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token, and retains only the b most probable extended partial output sequences, where b is known as the beam width” reads on ” extending the initial partial output sequence by performing a tree search of possible continuations of the initial partial output sequence guided by the sequence-to-sequence neural network system, until one or more termination criteria are met”).
Celikyilmaz et al teaches a tree search but fails to explicitly show/teach a look ahead tree search.
However, Niu et al teaches a look ahead tree search (paragraph [0012]…In view of the need for an efficient text compression mechanism, embodiments described herein provide an unsupervised model for text compression. Specifically, the unsupervised model is configured to identify an optimal deletion path for each input sequence of texts (e.g., a sentence) and words from the input sequence are gradually deleted along the deletion path. For example, the optimal deletion path includes an ordered collection of sentences, of which each intermediate sentence along the path is itself a shorter and coherent version of the original input sentence. To identify the optimal deletion path, the unsupervised model may adopt a pretrained bidirectional language model (e.g., BERT) to score each candidate deletion based on the average perplexity of the resulting sentence and performs a simple greedy look-ahead tree search to select the best deletion for each step).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Celikyilmaz et al teaches a tree search but fails to be a look ahead tree search, as in Niu et al, for the purpose of select the best deletion for each step.
As to claim 4, Celikyilmaz et al teaches processing the sequence of input tokens, the initial partial output sequence, and the candidate continuation of the initial partial output sequence, using the sequence-to-sequence neural network system, to define a next token probability distribution over possible output tokens for a next output token for extending the candidate continuation of the initial partial output sequence (paragraph [0061]…it is determined (for each of the branches, if applicable), whether the selected label is the end-of-sequence symbol. If not, the selected label of the output token is fed back into the decoder 112 in act 616, and the decoder 112 then proceeds to compute the output probability distribution for the next token. The generation of output probability distributions and selection of labels therefrom repeats in a loop until the end of the sequence is reached. The output sequence and/or associated probability (or multiple output sequences and probabilities), collectively 620, are then returned. In the case of a beam search (with width b>1), the most probable of all computed output sequences can be selected as the final output.)(Examiner’s Note: “he selected label of the output token is fed back into the decoder 112 in act 616, and the decoder 112 then proceeds to compute the output probability distribution for the next token. The generation of output probability distributions and selection of labels therefrom repeats in a loop until the end of the sequence is reached” reads on “using the sequence-to-sequence neural network system, to define a next token probability distribution over possible output tokens for a next output token for extending the candidate continuation of the initial partial output sequence”) ; and
using the next token probability distribution to expand the search tree (paragraph [0050]…the decoder component 506 may employ a beam search algorithm, which iteratively generates a tree structure of possible partial output sequences. In each iteration, the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token, and retains only the b most probable extended partial output sequences, where b is known as the beam width. For a beam width of b=1, the beam search algorithm reduces to greedy decoding. Beam search algorithms are well-known to those of ordinary skill in the art, as are several alternative decoding methods)(Examiner’s Note: “the beam search algorithm extends each of a number of previously generated partial output sequences with one additional token” reads on “using the next token probability distribution to expand the search tree”).
Celikyilmaz et al fails to explicitly show and teach a root node of a search tree for the look ahead tree search represents the initial partial output sequence, wherein edges to child nodes on a path from the root node each represent a candidate continuation of the initial partial output sequence; and wherein performing the look ahead tree search guided by the sequence-to-sequence neural network system comprises, for child nodes of the search tree:
However, Niu et al teaches a root node of a search tree for the look ahead tree search represents the initial partial output sequence, wherein edges to child nodes on a path from the root node each represent a candidate continuation of the initial partial output sequence; and wherein performing the look ahead tree search guided by the sequence-to-sequence neural network system comprises, for child nodes of the search tree (paragraph [0022]…each node in this graph 200 is either the original sentence itself (i.e., the root node) or a subsequence of it at the token level. Each node has outgoing edges pointing to sentences resulting from different ways of deletions from the current sentence at the respective node. Each edge is associated with a cost, which is the average perplexity score of each token in the sentence assigned by BERT. An optimal deletion path on the directed acyclic graph is a path of nodes that have the minimum average scores of all nodes along the path)(Examiner’s Note: “the original sentence itself (i.e., the root node)” reads on “a root node of a search tree for the look ahead tree search represents the initial partial output sequence” ; “Each node has outgoing edges pointing to sentences resulting from different ways of deletions from the current sentence at the respective node” reads on “edges to child nodes on a path from the root node each represent a candidate continuation of the initial partial output sequence”).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Celikyilmaz et al to have a root node of a search tree for the look ahead tree search represents the initial partial output sequence, wherein edges to child nodes on a path from the root node each represent a candidate continuation of the initial partial output sequence; and wherein performing the look ahead tree search guided by the sequence-to-sequence neural network system comprises, for child nodes of the search tree, as in Niu et al, for the purpose of select the best deletion for each step.
As to claim 7, Niu et al teaches performing the look ahead tree search of possible continuations of the initial partial output sequence to determine a plurality of complete candidate output sequences, wherein each complete candidate output sequence represents the complete sequence of input tokens; scoring each of the complete candidate output sequences; and selecting a candidate output sequence as the output sequence based on the scores (paragraph [0023]…The scoring module 132 is configured, in some embodiments, to employ a pretrained BERT to score each intermediate sentence along a deletion path in the directed acyclic graph. In some embodiments, a pretrained BERT model is used to assign a negative log likelihood for each token so that a score for each intermediate sentence at a respective node can be obtained. The scoring module 132 is configured to score each intermediate sentence according to several rules: (1) when having the same parent node, a grammatical sentence is assigned a higher score than an ungrammatical one; and (2) the score is indicative of the time point when the deletion process is to be terminated (i.e., when the simplest possible subsequence is obtained))(Examiner’s Note: “each intermediate sentence” reads on “each complete candidate output sequence represents the complete sequence of input tokens” ; “score each intermediate sentence along a deletion path in the directed acyclic graph” reads on “scoring each of the complete candidate output sequences” ; “The scoring module 132 is configured to score each intermediate sentence according to several rules: (1) when having the same parent node, a grammatical sentence is assigned a higher score than an ungrammatical one” reads on “selecting a candidate output sequence as the output sequence based on the scores”).
It would have been obvious for performing the look ahead tree search of possible continuations of the initial partial output sequence to determine a plurality of complete candidate output sequences, wherein each complete candidate output sequence represents the complete sequence of input tokens; scoring each of the complete candidate output sequences; and selecting a candidate output sequence as the output sequence based on the scores, for the same reasons as above.
As to claim 18, Celikyilmaz et al teaches the method, wherein the input tokens and the output tokens each represent words or wordpieces in a natural language (paragraph [0026]…the input to the multiple encoder agents may be raw input, such as sequences of words in a natural language (or a sequence of vectors trivially mapping on the natural words, such as one-hot vectors whose dimensionality equals the size of the input vocabulary and which each have a single component equal to 1 corresponding to the word they encode, all other components being zero). The first layer of each encoder agent may create an initial embedded representation of such raw input (e.g., a representation with lower-dimensional real-valued vectors) ; paragraph [0045]… or natural-language-generation task, this basic vocabulary may correspond to the n most common words in a given language. The decoder 112 includes, at its output layer, an output node for each of these n words. To limit the computational cost associated with the prediction of each token in the output sequence, n may be limited, e.g., to on the order of thousands or ten-thousands of words) (Examiner’s Note:” sequences of words in a natural language…each token in the output sequence” reads on “the input tokens and the output tokens each represent words or wordpieces in a natural language”).
Claim 24 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 25 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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