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 .
Abstract
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
It is noted that the Abstract is currently written as a claim.
Claim Objections
Claims 6 and 10 are objected to because of the following informalities:
Claim 6 contains the word “similiar.”
Claim 10 contains the word “similarily.”
These words are spelled incorrectly.
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.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Independent claim 1 recites:
“A computer-implemented method for adding a quantity fact to a knowledge base, the knowledge base being a knowledge graph, the method comprising the following steps:
providing the knowledge base;
providing a textual resource;
providing an entity from the knowledge base;
providing a relation from the knowledge base;
providing a set of different units;
determining a quantity including a unit within the set of different units that is within the textual resource depending on the entity, the relation, and the set of different units;
determining a quantity fact including the entity, the relation, the quantity, and the unit; and
adding the quantity fact to the knowledge base.”
Claims 11 and 12 recite similar subject matter.
The claims provide a series of data structures, “the knowledge base, textual resource, entity, relation, and set of different units,” then perform two determining steps to add a fact to the knowledge base. The claimed determining steps are merely data analysis steps and are thus mental processes.
The claims contain additional elements in the form of “providing” various data elements and structures, “a processor,” “at least one non-transitory memory,” and “a non-transitory computer-readable medium.” The various knowledge base, entity, relation, and units are merely data structures. Adding a determined fact to a set of data structures is not an additional element and something a human using a generic computer is capable of.
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
“Providing” various data elements and structures appear to be data gathering steps and are thus mere pre-solution insignificant activity (see MPEP 2106.05(g). The “processor,” “at least one non-transitory memory,” and “a non-transitory computer-readable medium” are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The additional element of “Providing” various data elements and structures is merely extra-solution activity data gathering and is well understood, routine, and conventional (see MPEP 2106.05(g)). The recitation of generic hardware of “processor,” “at least one non-transitory memory,” and “a non-transitory computer-readable medium” are little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claims 2-10 are merely directed towards additional data determinations (data analysis) steps and data definitions. It is noted that the claimed data definitions and data analysis steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
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.
Claims 1, 2, 7, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ho (“Qsearch: Answering Quantity Queries from Text”), in view of Chalabi et al. (US Pre-Grant Publication 2018/0039695).
As to claim 1, Ho teaches a computer-implemented method for adding a quantity fact to a knowledge base … the method comprising the following steps:
providing the knowledge base (see Ho, section 2.1. A corpus of text documents is analyzed to output a model. The model comprises a set of “quantity facts” extracted from the text corpus);
providing a textual resource (see Ho section 2.1 The model relies upon text sources);
providing an entity from the knowledge base (see Ho section 2.1. The model identifies entities);
providing a relation from the knowledge base (see Ho section 2.1. The model identifies relations between entities);
providing a set of different units (see Ho section 2.1 The model identifies different units, such as km or $);
determining a quantity including a unit within the set of different units that is within the textual resource depending on the entity, the relation, and the set of different units (see Ho section 2.1 The model identifies facts based on the entity, relation, and units, as shown in “Definition 1”);
determining a quantity fact including the entity, the relation, the quantity, and the unit (see Ho section 2.1); and
adding the quantity fact to the knowledge base (see Ho section 2.1. The fact tuples as shown in Example one are extracted and added to the model, as also described in section 2.2, “Extract”).
Ho does not explicitly teach the knowledge base being a knowledge graph.
Chalabi teaches a knowledge base being a knowledge graph (see paragraph [0045]. Facts may be represented using a graph data structure).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ho by the teachings of Chalabi because Chalabi provides to Ho the benefit of being able to add facts to the underlying knowledge based by ensuring that added facts are accurate and because the method of Chalabi may be applied to multiple different knowledge domains (see Chalabi paragraph [0005]).
As to claim 2, Ho as modified teaches the method according to claim 1, wherein the determining of the quantity includes finding a section of the textual resource that includes at least one quantity depending on the unit (see Ho section 2.1, Example 1. Ho shows an example of extracting quantities from a text snippet of “BMW i8 costs about 138k Euros in Germany and has a battery range between 50 and 60km”),
determining a context for the unit within the section (see Ho section 2.1, Example 1),
determining a plurality of tuples (see Ho section 2.1, Example 1),
wherein each tuple of the plurality of tuples includes the entity, one of the at least one quantity, the unit, and the context, and selecting the quantity from one tuple of the plurality of tuples depending on the context (see Ho section 2.1, Example 1. Ho shows a tuple comprising the entity “BMW i8,” a numerical value, a unit, and a context).
As to claim 7, Ho as modified by Chalabi teaches the method according to claim 3, the method further comprising:
determining a first score for at least one tuple of the plurality of tuples depending on the similarity to its reference, wherein the first score indicates a confidence for the at least one tuple being selectable for determining the quantity fact (see Chalabi paragraphs [0112]-[0116]. Chalabi measures the similarity of a candidate fact to a reference. It is noted that Ho teaches wherein such facts are tuples. See Ho section 2.1); and
adding the at least one tuple to a group of tuples when the first score indicates that the confidence for the at least one tuple being selectable for determining the quantity fact is higher than a first threshold (see Chalabi paragraphs [0116]. If a threshold is passed, then a candidate fact is determined to be similar to a reference fact and stored); wherein
the determining of the quantity fact includes selecting a tuple from the group of tuples (see Chalabi paragraph [0116] for selecting and adding a fact. Ho teaches wherein facts are tuples and added to a knowledge base, see Ho section 2.1 and 2.2).
As to claim 10, Ho as modified teaches the method according to claim 3, wherein the determining of the similarily includes determining the similarity depending on a normalization of the quantity in at least one of the tuples, wherein the normalization is determined depending on the unit in at least one of the tuples (see Ho page 9, Quantity Matching).
As to claims 11 and 12, see the rejection of claim 1.
Claims 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Ho (“Qsearch: Answering Quantity Queries from Text”), in view of Chalabi et al. (US Pre-Grant Publication 2018/0039695), and further in view of Overell et al. (US Pre-Grant Publication 2011/0307435).
As to claim 3, Ho as modified teaches the method according to claim 2.
Ho does not teach further comprising:
providing a reference for each tuple of the plurality of tuples;
determining a similarity of at least one tuple of the plurality of tuples to the reference for the tuple;
selecting the tuple from the plurality of tuples that includes a context that is more similar to its reference than a context in at least one other tuple of the plurality of tuples is to its reference.
Overell teaches further comprising:
providing a reference for each tuple of the plurality of tuples (see paragraphs [1208]-[1214]. A list of facts is input and a reliability score is generated for each fact. As noted in Ho section 2.1, facts are tuples. The reliability score is based on a comparison of the fact to buckets and other knowledge sources. These are references);
determining a similarity of at least one tuple of the plurality of tuples to the reference for the tuple (see paragraph [1208]-[1214]. The system of Overell determines whether facts are already contained in a relevant bucket, it is already believed, or if it has been extracted from sentences from different sources. These are all measures of “similarity” to a reference. As noted above, Ho section 2.1 shows wherein facts are represented as tuples);
selecting the tuple from the plurality of tuples that includes a context that is more similar to its reference than a context in at least one other tuple of the plurality of tuples is to its reference (see Overwell paragraphs [1208]-[1214]. Reliable facts are added to the knowledge base. Unreliable facts are not. Thus, facts that include a context judged reliable are more similar to their references than facts that contain contexts that are unreliable. As noted above, Ho section 2.1 shows wherein facts are represented as tuples).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ho by the teachings of Overell because Overell provides to Ho the benefit of being able to add facts to the underlying knowledge and ensure that the facts are reliable.
As to claim 4, Ho as modified by Overell teaches the method according to claim 3, wherein the providing of the reference for each tuple including providing a reference predicate domain for the knowledge base, providing a reference entity from the knowledge base, and providing a set of reference units from the set of units (see Overell paragraphs [1232]-[1245]. The buckets that are provided as a reference may match a variety of attributes of a candidate fact. Also noted that facts may have values for units, such as time points, see paragraph [0220]).
As to claim 6, Ho as modified by Overell teaches the method according to claim 3, wherein the providing of the reference for each tuple includes determining, for each tuple of the plurality of tuples, the reference that is more similiar to the context in the tuple than to a context in at least one other tuple of the plurality of tuples (see Overwell paragraphs [1208]-[1214]. Reliable facts are added to the knowledge based. Unreliable facts are not. Thus, tuples that include a context judged reliable are more similar to their references than facts that contain contexts that are unreliable. Thus, some references are more similar to a context of a fact than of other candidate facts).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ho (“Qsearch: Answering Quantity Queries from Text”), in view of Chalabi et al. (US Pre-Grant Publication 2018/0039695), in view of Overell et al. (US Pre-Grant Publication 2011/0307435), and further in view of Saha et al. (“Boostrapping for Numerical Open IE”).
As to claim 5, Ho as modified teaches the method according to claim 4.
Ho does not clearly teach wherein the determining of the similarity includes:
determining if a numerical representation of the entity of the at least one tuple is mapped by a numerical representation of the reference predicate to a numerical representation that is within a predetermined distance to a numerical representation of the reference entity or not,
determining if the unit of the at least one tuple is within the set of reference units or not, and
determining the similarity between the context from the at least on tuple of the plurality of tuples to the reference for at least one tuple of the plurality of tuples so that the numerical representation of the entity of the at least one tuple is mapped by the numerical representation of the reference predicate to a numeric representation that is within the predetermined distance to the numerical representation of the reference entity and so that the unit of the at least one tuple is within the set of reference units.
Saha teaches wherein the determining of the similarity includes:
determining if a numerical representation of the entity of the at least one tuple is mapped by a numerical representation of the reference predicate to a numerical representation that is within a predetermined distance to a numerical representation of the reference entity or not (see Saha pages 317-318, section 1. Saha shows that when identifying seed facts, numbers are matched when they are within a percentage threshold. Also see Section 3),
determining if the unit of the at least one tuple is within the set of reference units or not (see Saha pages 317-318, section 1. Saha shows that sentences are boostrapped only when the units match. Also see Section 3), and
determining the similarity between the context from the at least on tuple of the plurality of tuples to the reference for at least one tuple of the plurality of tuples so that the numerical representation of the entity of the at least one tuple is mapped by the numerical representation of the reference predicate to a numeric representation that is within the predetermined distance to the numerical representation of the reference entity and so that the unit of the at least one tuple is within the set of reference units (see Saha pages 317-318, section 1 and Section 3.1. Numbers are only matched when they are within a percentage threshold. Additionally, section 3.1 discusses “successful matching,” which indicates that similarity is determined).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ho by the teachings of Saha because Saha provides to Ho the benefit of being able to add facts to the underlying knowledge that match certain identified seed patterns with increased precision (see Saha Abstract).
Claim 8-9 is rejected under 35 U.S.C. 103 as being unpatentable over Ho (“Qsearch: Answering Quantity Queries from Text”), in view of Chalabi et al. (US Pre-Grant Publication 2018/0039695), and further in view of Roy et al. (“Reasoning about Quantities in Natural Language”).
As to claim 8, Ho as modified by Chalabi teaches the method according to claim 7, further comprising:
determining for a tuple in the group of tuples a second score depending on [a value] in the tuple, wherein the second score is indicative of a likelihood for that tuple being selectable for determining the … fact (see Chalabi paragraph [0112]-[0116]. Multiple analyses of the data within fact tuples is considered, including scoring values within the tuples), and
either adding the tuple to a set of candidate facts if the second score indicates that the likelihood of that tuple being selectable for determining the … fact is higher than a third threshold (see Chalabi paragraph [0112]-[0116]. Facts that are higher than a threshold may be stored), or
not adding that tuple to the set of candidate facts otherwise, wherein the determining the fact includes selecting a tuple from the set of candidate facts (see Chalabi paragraph [0112]-[0116]).
Ho as modified does not teach:
determining for a tuple in the group of tuples an assessment depending on the quantity in the tuple …
Roy teaches:
determining for a tuple in the group of tuples [a determination] depending on the quantity in the tuple … (see section 5.1.1 and Algorithm 2, which compares facts based on a quantity to determine whether the extract fact and a reference fact match or contradict one another).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ho by the teachings of Roy because Roy provides to Ho the benefit of performing quantity fact based analysis based on additional standardization and natural language reasoning analyses, which will improve the assessment of quantity based analysis.
As to claim 9, Ho as modified by Chalabi teaches the method according to claim 8,
wherein when the first score indicates a confidence of the at least one tuple being selectable as the fact that is below a second threshold (see Chalabi paragraph [0116]. Multiple environment-specific thresholds may be used for passage or failure. The comparison needs to pass only “with respect to any environment-specific threshold value,” so it would be obvious that it may fail one of them), performing:
determining a tuple in the plurality of tuples that is not in the set of candidate facts and has the same entity as a tuple of the set of candidate facts (see Chalabi paragraph [0111] and [0112]-[0116]. The comparison may be executed for all possible entity pairs),
determining a similarity depending on a quantity in the tuple of the plurality of tuples and the quantity in the tuple of the set of candidate facts (see Chalabi paragraph [0111] and [0112]-[0116]),
selecting the context in the tuple of the plurality of tuples as a candidate for another reference when the similarity is larger than a fourth threshold (see Chalabi paragraph [0111] and [0112]-[0116]).
Conclusion
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/CHARLES D ADAMS/Primary Examiner, Art Unit 2152
February 8, 2026