DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made non-final.
Claims 1-8 are pending in the case. Claims 1, and 8 are independent claims.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 05/17/2022. It is noted, however, that applicant has not filed a certified copy of the CN2022105338057 application as required by 37 CFR 1.55.
Claim Objections
Claim 1 is objected to because of the following informalities: Page 3, states "the positive feedback preference representation, the positive feedback preference representation," and appears to be a duplicate sentence. Appropriate correction is required.
Claim 2 is objected to because of the following informalities: Page 4, last line "then, drawing he..." appears to be the word 'the' and not 'he'. Appropriate correction is required.
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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a method (a process), Claim 8 is directed to a device comprising a memory, a processor, and a computer program (a machine). Therefore, Claims 1-8 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “computing system”, “predictive model”, and “supervised learning algorithm”) [see MPEP 2106.04(a)(2)(III)].
“extracting all positive feedback interaction items and negative feedback interaction items from user interaction history information, and constructing positive feedback interaction sequences and negative feedback interaction sequences respectively by arranging all positive feedback interaction items and negative feedback interaction items in the order of interaction time” (e.g., a human can compare and identify positive and negative feedback and order them based on date)
“encoding positive and negative feedback interaction sequences into a sequence of positive and negative feedback interaction vectors, respectively, and encoding user global preference information into a user global preference vector”(e.g., a human can sort the positive and negative feedback into respective positive and negative tables)
“calculating similarity between the comprehensive user preference representation and the corresponding vector of multiple candidate interaction items and the similarity is used as the recommendation score to rank the candidate interaction items, and recommending the user preference based on the ranking result” (e.g., a human can compare data from a model output, then rank it based on preferred outcomes)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “neural set operations model”, “set operation”, “multi-layer perceptron”, “union operation”, and “difference operation” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations, and is rejected thusly for each claim which recites the same language.
Regarding the “obtain user global preference information” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “learning comprehensive preference representation with the neural set operations model including set operation and multi-layer perceptron, comprising performing a union operation on a time-nearest plurality of positive feedback interaction vectors drawn from a sequence of positive feedback interaction vectors followed by a difference operation with a time-nearest plurality of negative feedback interaction vectors drawn from a sequence of negative feedback interaction vectors to obtain a positive feedback preference representation”, and “after performing a union operation on a time-nearest plurality of negative feedback interaction vectors extracted from the negative feedback interaction vector sequence, performing the difference set operation on a time-nearest plurality of positive feedback interaction vectors from the positive feedback interaction vector sequence to obtain negative feedback preference representation” limitations, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, these are merely inputting data into a model to learn positive and negative feedback to generate a positive and negative feedback score/representation.
Regarding the “the positive feedback preference representation, the positive feedback preference representation, the negative feedback preference representation and the global user preference embeddings are mapped using a multi-layer perceptron to obtain the comprehensive user preference representation” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, this is merely combining the results of a model to generate a combined dataset.
Regarding the “training, by a computing system, a plurality of predictive models to identify a set of object attributes from input data, the plurality of predictive models individually being trained based at least in part on a supervised learning algorithm and a training data set including one or more examples for which a corresponding set of object features is known” limitations, which are recited at a high-level of generality such that they amount to no more than generally linking the use of abstract idea to a particular technological environment or field of use using a generic computer component (See MPEP 2106.05(h)). In particular it is merely describing how the data is labeled for use in the claimed mental process.
Regarding the “based at least in part on providing the input data to a first predictive model of the subset of the plurality of predictive models”, “based at least in part on the first predicted attribute identified, obtaining, from a second predictive model of the subset of the plurality of predictive models,”, and “based at least in part on the first predicted attribute identified, obtaining, from a third predictive model of the subset of the plurality of predictive models,” limitations, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, they are merely inputting data into different models to identify desired data.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not 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 element of a “neural set operations model”, “set operation”, “multi-layer perceptron”, “union operation”, and “difference operation” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “obtain user global preference information” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering. 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").
Regarding the “learning comprehensive preference representation with the neural set operations model including set operation and multi-layer perceptron, comprising performing a union operation on a time-nearest plurality of positive feedback interaction vectors drawn from a sequence of positive feedback interaction vectors followed by a difference operation with a time-nearest plurality of negative feedback interaction vectors drawn from a sequence of negative feedback interaction vectors to obtain a positive feedback preference representation”, and “after performing a union operation on a time-nearest plurality of negative feedback interaction vectors extracted from the negative feedback interaction vector sequence, performing the difference set operation on a time-nearest plurality of positive feedback interaction vectors from the positive feedback interaction vector sequence to obtain negative feedback preference representation” limitations, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “the positive feedback preference representation, the positive feedback preference representation, the negative feedback preference representation and the global user preference embeddings are mapped using a multi-layer perceptron to obtain the comprehensive user preference representation” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 recites the following mathematical process, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “processor”, “ conditional generative model”, “graph neural network”) [see MPEP 2106.04(a)(2)(I)].
first, drawing the k time-nearest positive feedback interaction vectors from the sequence of positive feedback interaction vectors, and the union operation is performed on these k positive feedback interaction vectors in turn to obtain the joint embedding vector of the k positive feedback interaction vectors
h
U
i
˙
+
, denoted as,
h
U
i
˙
+
=UNION
x
u
i
j
+
,
x
u
i
j
+
∈
X
u
i
+
(1)
where UNION( ) denotes the concatenation operation, and
x
u
i
t
j
denotes the j-th positive feedback interaction vector, and
X
u
i
+
denotes the i-th user ui corresponding sequence of positive feedback interaction vectors;
wherein the equation (1) is understood as follows: for the j-th positive feedback interaction vector
x
u
i
j
+
with the j+1-th positive feedback
x
u
i
j
+
1
+
interaction vector
x
u
i
j
+
1
+
, the result of the union operation
x
u
i
j
+
∪
x
u
i
j
+
1
+
is obtained, then using the result of the union operation
x
u
i
j
+
∪
x
u
i
j
+
1
+
with the j+2th positive feedback interaction vector
x
u
i
j
+
2
+
to obtain the result of the concatenation operation
x
u
i
j
+
∪
x
u
i
j
+
1
+
∪
x
u
i
j
+
2
+
, until the k positive feedback interaction vectors are all concatenated to obtain the joint embedding vector of the k positive feedback interaction vectors
h
U
i
˙
+
” (e.g., a concatenation of the vectors, linear algebra operation )
“then, drawing he k time-nearest negative feedback interaction vectors drawn from the sequence of negative feedback interaction vectors, the joint embedding vector
h
U
i
˙
+
with the k negative feedback interaction vectors in order to perform the difference set operation to obtain the positive feedback preference representation
e
U
i
˙
+
, denoted as:
e
U
i
˙
+
=DIFFERENCE(
h
U
i
˙
+
,
x
u
i
j
-
),
x
u
i
j
-
∈
X
u
i
-
(2)
where DIFFERENCE ( ) denotes the difference set operation, and
x
u
i
j
-
denotes the first j′ negative feedback interaction vector, and
X
u
i
-
denotes the i-th user's corresponding sequence of negative feedback interaction vectors;
wherein the equation (2) is understood as follows: for the joint embedding vector
h
U
i
˙
+
with the first j′ negative feedback interaction vector
x
u
i
j
-
, and the difference set operation is performed to obtain
h
U
i
˙
+
\
x
u
i
j
-
−, and the difference operation is performed to obtain the result of the difference operation
h
U
i
˙
+
\
x
u
i
j
-
,\
x
u
i
j
'
+
1
-
with
h
U
i
˙
+
\
x
u
i
j
'
-
and the j′+1 negative feedback interaction vector
x
u
i
j
'
+
1
-
, until the k negative feedback interaction vectors are differenced to obtain a positive feedback preference representation
e
U
i
˙
+
” (e.g., a set subtraction over index sets, linear algebra of vector subtraction)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
In accordance with Step 2A, Prong 2, the claim does not include any additional elements and the judicial exception is not integrated 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 than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 recites the following mathematical process, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “processor”, “ conditional generative model”, “graph neural network”) [see MPEP 2106.04(a)(2)(I)].
“drawing the k time-nearest negative feedback interaction vectors drawn from the sequence of negative feedback interaction vectors, and the union operation is performed on these k negative feedback interaction vectors in turn to obtain the joint embedding vector of the k negative feedback interaction vectors
h
U
i
˙
-
, denoted as.
h
U
i
˙
-
=UNION
x
u
i
j
'
-
,
x
u
i
j
'
-
∈
X
u
i
-
(3)
wherein UNION( ) denotes the union operation, and
x
u
i
j
'
-
denotes the first j′ negative feedback interaction vector, and
X
u
i
-
denotes the i-th user ui corresponding sequence of negative feedback interaction vectors;
wherein the equation (3) is understood as follows: for the first j′ negative feedback interaction vector
x
u
i
j
'
-
with the first j′+1 negative feedback interaction vector
x
u
i
j
'
+
1
-
, the result of the union operation
x
u
i
j
'
-
∪
x
u
i
j
'
+
1
-
is obtained, and using the result of the union operation
x
u
i
j
'
-
∪
x
u
i
j
'
+
1
-
with the j′+2 negative feedback interaction vector
x
u
i
j
'
+
2
-
to obtain the result of the concatenation operation
x
u
i
j
'
-
∪
x
u
i
j
'
+
1
-