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 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 1-6 are 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.
Claim 1 recites the limitation the respective representation of the user selection (on the last two lines of the claim); however, the claim has previously only recited a respective representation of each user selection of a sequence of user selections, i.e. a plurality of respective representations. It is therefore unclear to which particular representation the limitation refers. For the purpose of examination, the claim will be interpreted as if it had read according to the respective representations of the user selections in the sequence of user selections, as does Claim 16.
Similarly, Claim 2 recites the respective representations of the user selection, but will be interpreted as if it had read the respective representations of the user selections, as no plural respective representations of any one selection have been previously recited.
Dependent claims are rejected for inheriting and failing to cure the indefiniteness of a parent claim.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 17-20 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Specifically, Claim 16 recites a non-transitory computer readable medium having stored therein instructions for causing a computing device to perform steps. Claims 17-20 recite only the computing device, which does not require the computer readable medium and thus fails to require all of the limitations of the parent claim upon which each depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites steps of determining a context embedding vector according to the sequence of user selections (a mental process of judgement); querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embedding vector, to obtain a knowledge-enhanced representation of the sequence of user actions (interpreted in light of the specification as examining values of neighboring nodes of a node to determine a value, which is a mental process of judgement); determining … based on the knowledge-enhanced representation, a respective representation of each user selection in the sequence of user selections (a mental process of judgement); and determining a predicted next user selection according to the respective representation[s] of the users selection[s] in the sequence of user selections (a mental process of judgement). Thus, the claim recites the abstract idea of predicting a user selection based on a sequence of transformations of representations of user selections.
The claim does not include any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
receiving a sequence of user selections through the electronic user interface, which is insignificant extra-solution activity of data gathering (MPEP 2106.05(g)); and
a graph neural network with which the claimed mental process of determining a respective representation is performed, which is merely using a computer or other machinery as a tool to perform the mental process step (MPEP 2106.05(f)(2)).
Thus, neither of the additional elements make use of or apply the mental processes steps to improve computer or other technology and do not integrate the abstract idea into a practical application and the claim is directed to the abstract idea.
Finally, the additional elements, taken alone and in combination, cannot provide significantly more than the abstract idea itself because the data gathering is well-understood, routine, and conventional (MPEP 2106.05(d), transmitting or receiving data over a network); the use of a computer or other machinery as a tool to perform the abstract idea cannot do so (MPEP 2106.05(f)(2)); and there is no nexus between the additional elements to provide significantly more. Therefore, the claim is subject-matter ineligible.
Claim 2, dependent upon Claim 1, only further recites that the determining a predicted next user selection mental process step is performed using a transformer-based encoder, which by MPEP 2106.05(f)(2) can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 3 and 5, dependent upon Claim 1, each recite additional mental process steps of the abstract idea (determining an embedding vector for each user selection; determining the context embedding vector according to the respective embeddings; building the knowledge graph) but no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 4 and 6 recite new additional elements which only specify the data used in the mental process steps (wherein user selections are not included in the knowledge graph; wherein session relationships comprise certain types of relationships), which only specifies a particular field of use, which by MPEP 3206.05(h) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 16-20 recite alternatively a non-transitory computer readable medium or a computing device to perform precisely the methods of Claims 1, 2, and 4-6, respectively. As performance on generic computing components cannot integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself, Claims 16-20 are rejected for reasons set forth in the rejections of Claims 1, 2, and 4-6, respectively.
Claim 7 recites a system comprising a processor, thus an article of manufacture, one of the four statutory categories of patentable subject matter. However, Claim 7 further recites steps of predicting the next user selection (a mental process); to generate an output based upon a sequence of user actions (a mental process); and to determine a predicted next user action (a mental process). Thus, the claim recites a mental process of determining a predicted next user action based upon a sequence of user actions.
The claim does not recite any additional elements which could integrate the abstract idea, because the additional elements consist only of:
a processor; and a memory comprising a non-transitory computer readable medium having stored therein instructions to perform the abstract idea; and performance of an abstract idea on generic computer components cannot integrate the abstract idea into a practical application (MPEP 2106.05(f)(2))
to train, deploy and use (input into) a model to perform the generate an output mental process step; and “using a computer or other machinery as a tool” to perform an abstract idea cannot integrate the abstract idea into a practical application (MPEP 2106.05(f)(2))
to receive a sequence of user actions through an electronics user interface and to output the predicted next user action in response to the sequence of user actions which are insignificant extra-solution activity of data gathering and data display (MPEP 2106.05(g)).
Thus, neither of the additional elements make use of or apply the mental processes steps to improve computer or other technology and do not integrate the abstract idea into a practical application and the claim is directed to the abstract idea.
Finally, the additional elements, taken alone and in combination, cannot provide significantly more than the abstract idea itself because the data gathering and display is well-understood, routine, and conventional (MPEP 2106.05(d), transmitting or receiving data over a network); the use of a computer or other machinery as a tool to perform the abstract idea cannot do so (MPEP 2106.05(f)(2)); and there is no nexus between the additional elements to provide significantly more. Therefore, the claim is subject-matter ineligible.
Claim 8, dependent upon Claim 7, merely specifies the particular data used in the training of the model to perform the abstract idea (the sequence of user actions and a knowledge graph), which is merely stating the particular field of use, and thus cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself (MPEP 2106.05(h)).
Claim 9, dependent upon Claim 7, further only recites using a computer or other machinery as a tool to perform the mental process (predicting a next user action in response to each new user action, using the trained model), which by MPEP 2106.05(f)(2) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 10, dependent upon Claim 7, only recites additional mental process steps (querying a knowledge graph, interpreted in light of the specification as examining values of neighboring nodes of a node to determine a value; determining a respective representation; determining a predicted next user action) performed by a computer or other machinery as a tool (i.e. with a graph neural network), which by MPEP 2106.05(f)(2) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 11, dependent upon Claim 10, only further recites that the determining a predicted next user selection mental process step is performed using a transformer-based encoder, which by MPEP 2106.05(f)(2) can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 12 and 14, dependent upon Claim 10, each recite additional mental process steps of the abstract idea (determining an embedding vector for each user selection; determining the context embedding vector according to the respective embeddings; building the knowledge graph) but no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 13 and 15 recite new additional elements which only specify the data used in the mental process steps (wherein user selections are not included in the knowledge graph; wherein session relationships comprise certain types of relationships), which only specifies a particular field of use, which by MPEP 3206.05(h) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 5-12, 14-17, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu et al., “Session-Based Recommendation with Graph Neural Networks.”
Regarding Claim 1, Wu teaches a method for predicting a next user selection in an electronic user interface (Wu, title, “Session-Based Recommendation” for pg. 350, 1st column, 4th paragraph, “a stream of user clicks on an e-commerce website”), the method comprising: receiving a sequence of user selections through the electronic user interface (Wu, pg. 347, Fig. 1, “we model all session sequences”); determining a context embedding vector according to the sequence of user selections (Wu, pg. 349, 1st column, 1st paragraph & Eq. (1), “
v
1
t
-
1
,
…
,
v
n
t
-
1
is the list of node vectors in session s” is a context embedding vector); querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embedding vector, to obtain a knowledge-enhanced representation of the sequence of user selections (Wu, pg. 349, 1st column, Eq. (1) where
A
s
,
i
represents a knowledge graph, see 2nd paragraph, “which represents weighted connections of outgoing and incoming edges in the session graph” and the operation of Eq. (1) is querying this graph, and
v
i
t
of Eq. (5) is a knowledge-enhanced representation of the sequence); determining, with a graph neural network respective of the knowledge graph, based on the knowledge-enhanced representation, a respective representation of each user selection in the sequence of user selections (Wu, pg. 347, Fig. 1, the embeddings
v
1
,
v
2
,
v
4
,
v
3
are respective representations of each user selection, also see pg. 349, 2nd column, 2nd paragraph, “After feeding all session graphs into the gated graph neural network, we obtain the vectors of all nodes”); determining a predicted next user selection according to the representative representations of the user selections in the sequences of user selections (Wu, pg. 347, Fig. 1, “Finally, we predict the probability of each item that will appear to be the next-click one for each session”).
Regarding Claim 2, Wu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wu further teaches wherein determining the predicted next user selection according to the respective representations of the user selections in the sequence of user selections comprises inputting the respective representations of the user selections into a transformer-based encoder (Wu, pg. 347, Fig. 1, where the “Attention Network” is a transformer-based encoder).
Regarding Claim 3, Wu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wu further teaches wherein determining a respective embeddings vector for each user selection in the sequence of user selections; wherein determining the context embedding vector is according to the respective embeddings vector for each user selection in the sequence of user selections (Wu, pg. 349, 1st column, 1st paragraph & Eq. (1), “
v
1
t
-
1
,
…
,
v
n
t
-
1
is the list of node vectors in session s” as a whole is a context embedding vector comprised of individual respective embeddings vector for each user selection in the sequence).
Regarding Claim 5, Wu teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wu further teaches building the knowledge graph according to session relationships of possible user selections (Wu, pg. 347, Fig. 1, the directed knowledge graph is built according to the session order sequences/relationships & “We model all session sequences as session graphs”).
Regarding Claim 6, Wu teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). Wu further teaches wherein the session relationships comprise one or more of: co-views (Wu, pg. 350, 1st column, 4th paragraph, “a stream of user clicks on an e-commerce website”).
Claims 16, 17, 19, and 20 recite alternatively a non-transitory computer readable medium or a computing device to perform precisely the methods of Claims 1, 2, 5, and 6, respectively. As Wu performs their method on a computer (Wu, pg. 348, 2nd column, footnote, “To make our results fully reproducible, all source codes have been made public”) in which a non-transitory medium and a computing device comprising a processor are inherent, Claims 16, 17, 19, and 20 are rejected for reasons set forth in the rejections of Claims 1, 2, 5, and 6, respectively.
Regarding Claim 7, Wu teaches a system for predicting a next user selection (Wu, title, “Session-Based Recommendation” for pg. 350, 1st column, 4th paragraph, “a stream of user clicks on an e-commerce website”), the system comprising: a processor; and a memory comprising a non-transitory computer readable medium having stored therein one or more instructions executable by the processor (Wu, pg. 348, 2nd column, footnote, “To make our results fully reproducible, all source codes have been made public” denotes that they perform their method on a computer, in which a processor and memory are inherent) to perform operations comprising: train a model for predicting the next user selection; deploy the trained model (Wu, pg. 349, 2nd column, “Making Recommendation and Model Training” including training the graph neural network, 1st column, 2nd-to-last paragraph, “updating all nodes in session graphs until convergence” also see pg. 347, Fig. 1 & pg. 350, 1st column, 2nd paragraph, “we use the Back-Propagation Through Time (BPTT) algorithm to train the proposed SR-GNN model”); receive a sequence of user actions through an electronics user interface (Wu, pg. 347, Fig. 1, “we model all session sequences” & pg. 350, 1st column, 4th paragraph, “a stream of user clicks on an e-commerce website”); input the sequence of user actions into the trained model to generate an output; determine a predicted next user action based on the output of the trained model (Wu, pg. 347, Fig. 1 & “Finally, we predict the probability of each item that will appear to be the next-click one for each session”); and output the predicted next user action in response to the sequence of user actions (Wu, pg. 347, Fig. 1, the presented probabilities & Abstract, “to make recommendations” denotes that a user must receive the prediction/recommendation).
Regarding Claim 8, Wu teaches the system of Claim 7 (and thus the rejection of Claim 7 is incorporated). Wu further teaches wherein training the model further comprises: receive, as input, the sequence of user actions and a knowledge graph respective of possible user actions; wherein the predicted next user action is determined based on the knowledge graph respective of the possible user actions (Wu, pg. 347, Fig. 1 & pg. 349, 2nd column, “Making Recommendation and Model Training” including training the graph neural network, 1st column, 2nd-to-last paragraph, “updating all nodes in session graphs until convergence”).
Regarding Claim 9, Wu teaches the system of Claim 7 (and thus the rejection of Claim 7 is incorporated). Wu further teaches wherein inputting the sequence of user actions into the trained model further comprises: inputting each new user action into the trained model, such that the trained model is predicting a next user action in response to each new user action, based on a sequence of prior user actions (Wu, pg. 347, Fig. 1, where the prediction is in response to the latest new user action in the sequence).
Regarding Claim 10, Wu teaches the system of Claim 7 (and thus the rejection of Claim 7 is incorporated). Wu further teaches wherein receiving the sequence of user actions further comprises: determining a context embedding vector according to the sequence of user selections (Wu, pg. 349, 1st column, 1st paragraph & Eq. (1), “
v
1
t
-
1
,
…
,
v
n
t
-
1
is the list of node vectors in session s” is a context embedding vector); querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embedding vector, to obtain a knowledge-enhanced representation of the sequence of user selections (Wu, pg. 349, 1st column, Eq. (1) where
A
s
,
i
represents a knowledge graph, see 2nd paragraph, “which represents weighted connections of outgoing and incoming edges in the session graph” and the operation of Eq. (1) is querying this graph, and
v
i
t
of Eq. (5) is a knowledge-enhanced representation of the sequence); determining, with a graph neural network respective of the knowledge graph, based on the knowledge-enhanced representation, a respective representation of each user selection in the sequence of user selections (Wu, pg. 347, Fig. 1, the embeddings
v
1
,
v
2
,
v
4
,
v
3
are respective representations of each user selection, also see pg. 349, 2nd column, 2nd paragraph, “After feeding all session graphs into the gated graph neural network, we obtain the vectors of all nodes”); determining a predicted next user selection according to the representative representations of the user selections in the sequences of user selections (Wu, pg. 347, Fig. 1, “Finally, we predict the probability of each item that will appear to be the next-click one for each session”).
Regarding Claim 11, Wu teaches the system of Claim 10 (and thus the rejection of Claim 10 is incorporated). Wu further teaches wherein determining the predicted next user selection according to the respective representations of the user selections in the sequence of user selections comprises inputting the respective representations of the user selections into a transformer-based encoder (Wu, pg. 347, Fig. 1, where the “Attention Network” is a transformer-based encoder).
Regarding Claim 12, Wu teaches the system of Claim 10 (and thus the rejection of Claim 10 is incorporated). Wu further teaches wherein determining a respective embeddings vector for each user selection in the sequence of user selections; wherein determining the context embedding vector is according to the respective embeddings vector for each user selection in the sequence of user selections (Wu, pg. 349, 1st column, 1st paragraph & Eq. (1), “
v
1
t
-
1
,
…
,
v
n
t
-
1
is the list of node vectors in session s” as a whole is a context embedding vector comprised of individual respective embeddings vector for each user selection in the sequence).
Regarding Claim 14, Wu teaches the system of Claim 10 (and thus the rejection of Claim 10 is incorporated). Wu further teaches building the knowledge graph according to session relationships of possible user selections (Wu, pg. 347, Fig. 1, the directed knowledge graph is built according to the session order sequences/relationships & “We model all session sequences as session graphs”).
Regarding Claim 15, Wu teaches the system of Claim 14 (and thus the rejection of Claim 14 is incorporated). Wu further teaches wherein the session relationships comprise one or more of: co-views (Wu, pg. 350, 1st column, 4th paragraph, “a stream of user clicks on an e-commerce website”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al., “Session-Based Recommendation with Graph Neural Networks,” in view of Hamilton et al., “Inductive Representation Learning on Large Graphs.”
Regarding Claim 4, Wu teaches the method of Claim 3 (and thus the rejection of Claim 3 is incorporated). Wu appears to assume that all potential actions are covered in the knowledge graph, and does not address what might happen if one or more user selections in the sequence of user selections are not included in the knowledge graph. However, Hamilton, also in the field of content recommendation using embeddings of nodes in large graphs (see Hamilton, Abstract) teaches an framework that allows the graph embedding network to “generalize to unseen nodes” (Hamilton, Abstract), i.e. to give the recommendation system a way to deal with user selections that are not included in the knowledge graph. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inductive learning algorithm of Hamilton in the graph neural network embedding learning of Wu. The motivation to do so is to allow the recommendation algorithm of Wu to recommend when previously unseen actions/nodes occur.
Claim 18 recites a computing device to perform precisely the method of Claim 4. As Wu performs their method on a computer (Wu, pg. 348, 2nd column, footnote, “To make our results fully reproducible, all source codes have been made public”) in which a computing device comprising a processor is inherent, Claim18 is rejected for reasons set forth in the rejection of Claim 4.
Regarding Claim 13, Wu teaches the system of Claim 12 (and thus the rejection of Claim 12 is incorporated). Wu appears to assume that all potential actions are covered in the knowledge graph, and does not address what might happen if one or more user selections in the sequence of user selections are not included in the knowledge graph. However, Hamilton, also in the field of content recommendation using embeddings of nodes in large graphs (see Hamilton, Abstract) teaches an framework that allows the graph embedding network to “generalize to unseen nodes” (Hamilton, Abstract), i.e. to give the recommendation system a way to deal with user selections that are not included in the knowledge graph. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inductive learning algorithm of Hamilton in the graph neural network embedding learning of Wu. The motivation to do so is to allow the recommendation algorithm of Wu to recommend when previously unseen actions/nodes occur.
Conclusion
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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