DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 10 June 2022.
Claims 1-9 are pending. Claims 1, 8, and 9 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed on 17 July 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10 June 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The examiner accepts the drawings filed 10 June 2022.
Claim Objections
Claims 1-9 are objected to because of the following informalities:
With respect to independent claims 1, 8, and 9, the applicant recites “an embedding of the first training query in knowledge graph embedding model (claim 1, lines 7-9; claim 8, lines 7-9; claim 9, lines 10-12; emphasis added).” It appears that the claim should recite “the knowledge graph.” Appropriate correction is required.
With respect to claims 2-7, the claims fail to cure the informalities of independent claim 1. Claims 2-7 are similarly objected to for this informality.
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 2-5 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-7 are directed toward a method (process). Claim 8 is directed toward device (machine). Claim 9 is directed toward a non-transitory computer-readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 2:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 2, the claims recite:
determining a set of possible monadic consecutive queries that are consistent according to the ontology and a set of entries and a set of relations of the knowledge graph (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to determine a set of possible monadic consecutive queries that are consistent with the ontology and a set of entries and relations)
selecting the first training query from the set of monadic consecutive queries (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement/evaluation to select a first training query)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
training the knowledge graph embedding model with a first training query and its predetermined answer to minimize a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and the minimize a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model (claim 1, lines 4-11)
wherein the second training query is determined from the first training query depending on the ontology (claim 1, lines 12-13)
The training 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))
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:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
training the knowledge graph embedding model with a first training query and its predetermined answer to minimize a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and the minimize a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model (claim 1, lines 4-11)
wherein the second training query is determined from the first training query depending on the ontology (claim 1, lines 12-13)
The training 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))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 3:
With respect to dependent claim 3, the claim depends upon dependent claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 3, the claims recite:
determining the first training query according to a predetermined query shape (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a evaluation of a predetermined query shape by a user and a judgment to determine the first training query)
Claim 4:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 4, the claims recite:
randomly sampling a query (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing an observation of a plurality queries and selecting a random sample of queries)
determine a generalization of the query with the ontology (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses an evaluation to determine a generalization of the query with the ontology)
determining the second training query from a specialization of the generalization (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to determine a second query from a specialization of the generalization)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
training the knowledge graph embedding model with a first training query and its predetermined answer to minimize a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and the minimize a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model (claim 1, lines 4-11)
wherein the second training query is determined from the first training query depending on the ontology (claim 1, lines 12-13)
The training 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))
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:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
training the knowledge graph embedding model with a first training query and its predetermined answer to minimize a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and the minimize a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model (claim 1, lines 4-11)
wherein the second training query is determined from the first training query depending on the ontology (claim 1, lines 12-13)
The training 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))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 5:
With respect to dependent claim 5, the claim depends upon dependent claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 5, the claims recite:
determining generalizations of the query up to the generalization depth and/or (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a evaluation of generalizations depth and performing an observation to determine the specializations up to the generalizations depth)
determining specialization of the query up to the specialization depth (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a evaluation of specialization depth and performing an observation to determine the specializations up to the specialization depth)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
providing a generalization depth
providing a specialization depth
These limitations 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:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
providing a generalization depth
providing a specialization depth
These limitations 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.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
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 and 6-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ren et al. (Query2Box: Reasoning Over Knowledge Graphs In Vector Space Using Box Embeddings, 29 February 2020, provided in IDS filed 10 June 2022, hereafter Ren).
As per independent claim 1, Ren discloses a computer-implemented method for training a knowledge graph embedding model of a knowledge graph that is enhanced by an ontology, the method comprising:
training the knowledge graph embedding model with a first training query and its predetermined answer (Section 3; page 3, paragraph 5: Here, in order to train a system, a set of queries are generated with their answers at the training time) to minimize a distance between an embedding of the answer in the knowledge graph embedding model, and an embedding of the first training query in knowledge graph embedding model, and to minimize a distance between the embedding of the answer and an embedding of a second training query in the knowledge graph embedding model (Abstract: Here, answers as embedded as “close” to their corresponding queries. The examiner interprets this as minimizing the distance between the query and the answer within the graph. Further, answers are shown as having a minimized distance with respect to the query in the vector space (Figure 1D) in order to perform operations on the data set, such as intersections. Additionally, to train the system, a set of training queries with their answers are generated at training time (page 3; Section 3). The examiner interprets this set of training queries as including at least a first and a second training query)
wherein the second training query is determined from the first training query depending on the ontology (Section 3.2; page 4, paragraphs 5-6: Here, a complex query is decomposed into a sequence of logical operations and these operations are executed in the vector space. In this instance, a first query, such as “Where did Canadian citizens with Turing Awards graduate?”, is decomposed into a plurality of second queries. This may include “Turing Award” and “Canada” (Figure 1). With the operators “Win” and “Citizen” applied to the respective sets in the vector space)
As per dependent claim 2, Ren discloses the method further comprising:
determining a set of possible monadic consecutive queries that are consistent according to the ontology and a set of entities and a set of relations of the knowledge graph (Section 3.2; page 4, paragraphs 5-6: Here, a complex query is decomposed into a sequence of logical operations and these operations are executed in the vector space. In this instance, a first query, such as “Where did Canadian citizens with Turing Awards graduate?”, is decomposed into a plurality of second queries. This may include “Turing Award” and “Canada” (Figure 1). With the operators “Win” and “Citizen” applied to the respective sets in the vector space)
selecting the first training query from the set of monodic consecutive queries (Section 3; page 3, paragraph 5: Here, in order to train a system, a set of queries are generated with their answers at the training time)
As per dependent claim 3, Ren discloses the method further comprising determining the first training query according to a predetermined query shape (Section 3.2: Here, a query is modeled as an embedding box (page 2, paragraph 2) and the corresponding answers to the queries are determined as entities enclosed within the embedding box).
As per dependent claim 6, Ren discloses the method further comprising providing an answer to a conjunctive query with the knowledge graph embedding model (Figure 1: Here, an answer to the conjunctive query is generated by identifying the intersection of the decomposed query).
As per dependent claim 7, Ren discloses the method further comprising:
training the knowledge graph embedding to: (i) maximize a distance between the embedding of the first training query and at least one embedding of a predetermined entity that is not an answer to the first training query and/or (ii) maximize a distance between the embedding of the second training query and at least one embedding of a predetermined entity that is not an answer to the second query (Section 3.2; pages 4-5: Here, an entity to box distance is used. Entities that are within the box (answers) are regarded as “close enough” and the value is down weighted to 0 < value < 1. Entities outside the box (non-answers) are not down weighted, thereby maximizing their distance values).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Ren and further in view of Beller et al. (US 2018/0053098, published 22 February 2018, hereafter Beller).
As per dependent claim 4, Ren discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Ren further discloses:
determining a generalization of the query with the ontology (pages 2-3: Here, Query2Box provides strong generalizations to answer complex queries and can generalize logical query structures it has never seen during training)
determining a second training query from a specialization of the generalization (Section 3.2; page 4, paragraphs 5-6: Here, a complex query is decomposed into a sequence of logical operations and these operations are executed in the vector space. In this instance, a first query, such as “Where did Canadian citizens with Turing Awards graduate?”, is decomposed into a plurality of second queries. This may include “Turing Award” and “Canada” (Figure 1). With the operators “Win” and “Citizen” applied to the respective sets in the vector space)
Ren fails to specifically disclose random sampling a query. However, Beller, which is analogous to the claimed invention because it is directed toward evaluating knowledge graphs, discloses random sampling a query (paragraph 0045: Here, sample user queries are randomly selected from the knowledge graph). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Beller with Ram, with a reasonable expectation of success, as it would have allowed for selecting a benchmark set (Beller: paragraph 0045) in order to evaluation the training of the system (Beller: paragraph 0003).
With respect to independent claim 8, the claim recites limitation substantially similar to those in claim 1. The rejection of claim 1 under Ram is incorporated herein by reference.
Ram fails to specifically disclose a device. However, Beller, which is analogous to the claimed invention because it is directed toward a training and testing training data, discloses a device (Figure 7; paragraph 0055). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Beller’s device with Ram’s method, with a reasonable expectation of success, as it would have allowed for implementing the method on a device to execute the method by a processor (Beller: paragraphs 0063-0064).
With respect to independent claim 9, the claim recites limitation substantially similar to those in claim 1. The rejection of claim 1 under Ram is incorporated herein by reference.
Ram fails to specifically disclose a non-transitory computer-readable medium on which is stored a computer program including computer readable instructions. However, Beller, which is analogous to the claimed invention because it is directed toward a training and testing training data, discloses a non-transitory computer-readable medium on which is stored a computer program including computer readable instructions (Figure 7; paragraphs 0063-0064). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Beller’s computer readable medium with Ram’s method, with a reasonable expectation of success, as it would have allowed for implementing the method on a device in order to leverage the computer readable medium to execute the method by a processor (Beller: paragraphs 0063-0064).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ren and Beller and further in view of Colgrave et al. (US 2009/0055367, published 26 February 2009, hereafter Colgrave).
As per dependent claim 5, Ren and Baller disclose the limitations similar to those in claim 5, and the same rejection is incorporated herein. Ren further discloses:
providing a generalization and determining generalization of the query and/or
providing a specialization and determining specializations of the query (pages 2-3: Here, Query2Box provides strong generalizations to answer complex queries and can generalize logical query structures it has never seen during training)
However, Ren fails to specifically disclose providing a depth and querying data up to the depth. However, Colgrave, which is analogous to the claimed invention because it is directed toward querying objects in a graph (tree), discloses providing a depth and querying data up to the depth (paragraph 0084: Here, a depth limit is specified. The query traverses the graph (tree) until a depth counter is reached. Objects after the depth counter are cut while those before the depth counter are returned). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Colgrave with Ren-Baller, with a reasonable expectation of success, as it would have allowed for traversing graphs, with constraints, and return a subset of objects (Colgrave: paragraphs 0021 and 0084).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Meijer et al. (US 2013/0159969, published 20 June 2013): Discloses the use of monadic query operators (paragraph 0041)
Cheng et al. (US 11809480): Discloses generating a personalized knowledge graph when interacting with media content (column 2, line 57-column 3, line 25) and using the knowledge graph and associated metadata for training a machine learning model (column 3, lines 43-62)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128