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 § 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, 4, 7, 9-13, 16, 18, 20, are rejected under 35 U.S.C. 103 as being unpatentable over Fauqueur (US 20230351111 A1), in view of Mertens (US 10366151 B1).
Regarding claim 1, Fauqueur teaches method for operating one or more computing devices, comprising (Fauqueur [49, 50, 61, 103] method executing by software stored on a medium by processor(s), method may be performed by machine learning):
analyzing, by the computing device, an electronic resource to identify Entities in textual content, wherein each said Entity comprises one or more words (Fauqueur [110] text portions or sections are analyzed to find entities );
performing machine learning operations by a first classifier to assign an entity type classification of a plurality of entity type classifications to at least one of the Entities (Fauqueur [114, 119, 123, 125] entity type(s) (subject, object, verb, noun types )may be assigned);
performing machine learning operations by a second classifier to assign each said Entity to one or more segments of the textual content ... (Fauqueur [87, 119, 127] text portions may have facts, entities may be associated with specific text portions);
performing machine learning operations by a third classifier to recognize relationships of the Entities to each person or business entity identified in the textual content and assign a relationship classification of a plurality of relationship classifications to at least one of the Entities associated with one of the recognized relationships (Fauqueur [110, 120-122] relationships between entities are determined and triples formed accordingly);
converting, by the computing device, the electronic resource into relationship vectors based on outputs of the first, second and third classifiers (Fauqueur [117, 126, 127, 138, 139] entities, text and relationship information may be converted to triples (vectors) with subject, verb, object and direction).
Fauqueur does not specifically teach textual content that respectively comprise facts about people or business entities; controlling, by the computing device, operations of a software application using the relationship vectors.
However Mertens teaches textual content that respectively comprise facts about people or business entities (Mertens Col 2, line 37- Col 3, line 13, textual content may have facts and be associated with entities, entities may include people and businesses);
controlling, by the computing device, operations of a software application using the relationship ...information... (Mertens Col 3, lines 14-29, Col 11, lines 44- 64, Fig. 3, information related to entities of interest (including relationships regarding who, where, when and other facts for entities) may be displayed (operation within an application).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Mertens of textual content that respectively comprise facts about people or business entities, controlling, by the computing device, operations of a software application using the relationship ...information..., into the invention suggested by Fauqueur; since both inventions are directed towards associating textual content facts with entities, and incorporating the teaching of Mertens into the invention suggested by Fauqueur would provide the added advantage of associating content with real-life entities such as people and businesses and using the information to display facts of interest to a user within an application being used, and the combination would perform with a reasonable expectation of success (Mertens Col 2, line 37- Col 3, line 29, Fig. 3).
Regarding claim 4, Fauqueur and Mertens teach the invention as claimed in claim 1 above.
Claim 1 further teaches that an entitiy may be a person or a business.
Fauqueur further teaches wherein the electronic resource comprises a multi-...entity... sentence, and an assignment of the Entity to one or more segments of textual content indicates that the Entity has relationships with at least ...other entities... mentioned in the multi-...entity... sentence (Fauqueur [101, 114, 155] sentences may have entities that are nouns and subjects and objects, which may be related to another entity in the sentence).
Regarding claim 7 Fauqueur and Mertens teach the invention as claimed in claim 1 above. Fauqueur does not specifically teach wherein the controlling operations of the software application comprising performing autonomous operations to provide facts contained in one or more of the relationship vectors, based on content of a first window of the software application or another software application that is currently being displayed on a display screen
However Mertens teaches wherein the controlling operations of the software application comprising performing autonomous operations to provide facts contained in one or more of the relationship ...information.., based on content of a first window of the software application ... (Mertens Col 3, lines 14-29, Fig. 3, information related to entities of interest in current display, may be displayed within an application).
Regarding claim 9, Fauqueur and Mertens teach the invention as claimed in claim 7 above. Fauqueur does not specifically teach wherein the autonomous operations are triggered by navigation to a particular type of website, creation of an electronic message, start of an online phone call, or start of an online meeting
However Mertens teaches wherein the autonomous operations are triggered by ...creation of an electronic message...or start of an online meeting (Mertens Col 3, line 18, Col 12, lines 8-13, document may be email or in the context of a meeting, Mertens Col 7, line 58- Col 8, line 25, starting a new document in the appropriate application may trigger fact checking and display).
Regarding claim 10 Fauqueur and Mertens teach the invention as claimed in claim 7 above. Fauqueur does not specifically teach wherein the first window comprises an electronic message window, a social networking website window, or a web conferencing window
However Mertens teaches wherein the first window comprises an electronic message window (Mertens Col 3, line 18, document may be email, Mertens Col 7, line 58- Col 8, line 25, starting a new document in the appropriate application may trigger fact checking and display).
Regarding claim 11 Fauqueur and Mertens teach the invention as claimed in claim 7 above. Fauqueur does not specifically teach wherein the autonomous operations are performed without requiring a user to launch a software application configured to search stored contact information
However Mertens teaches wherein the autonomous operations are performed without requiring a user to launch a software application configured to search stored contact information
(Mertens Col 1, lines 63-65, Mertens Col 7, line 58- Col 8, line 25, starting a new document in the appropriate application may trigger fact checking and display, displayed information may be displayed within the document application context, information may be contact information).
Regarding claim 12 Fauqueur and Mertens teach the invention as claimed in claim 1 above. Fauqueur does not specifically teach wherein the values of at least one of the relationship vectors are automatically presented on the computing device or another computing device during an online meeting based on identities of participants of the online meeting
However Mertens teaches wherein the values of at least one of the relationship ...information... are automatically presented on the computing device ... during an online meeting based on identities of participants of the online meeting (Mertens Col 12, lines 8-13, document may be the context of a meeting and based on meeting participants, Mertens Col 7, line 58- Col 8, line 25, starting a new document in the appropriate application may trigger fact checking and display).
Claim 13 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1 and is rejected under the same rationale.
Fauqueur further teaches a system, comprising: at least one processor; a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for selectively providing facts about people (Fauqueur [49, 50, 61, 103] method executing by software stored on a medium by processor(s), method may be performed by machine learning).
Claim(s) 16, 18, is/are dependent on claim 13 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 4, 7 respectively, and is/are rejected under the same rationale.
Claim 20 is directed towards a medium storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale.
Fauqueur further teaches a non-transitory computer-readable medium that stores instructions that are configured to, when executed by at least one computing device, cause the at least one computing device to perform operations (Fauqueur [49, 50, 61, 103] method executing by software stored on a medium by processor(s), method may be performed by machine learning).
Claims 2, 3, 14, 15, are rejected under 35 U.S.C. 103 as being unpatentable over Fauqueur (US 20230351111 A1) in view of Mertens (US 10366151 B1), and further in view of Williams (US 20190325022 A1).
Regarding claim 2, Fauqueur and Mertens teach the invention as claimed in claim 1 above. Fauqueur does not specifically teach wherein the converting comprises inserting at least some of the Entities as values in a plurality of data statements
However Williams teaches wherein the converting comprises inserting at least some of the Entities as values in a plurality of data statements (Williams [195-200, 202-207, 310] based on determined relationships, entities are populated into knowledge base).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Williams of wherein the converting comprises inserting at least some of the Entities as values in a plurality of data statements, into the invention suggested by Fauqueur and Mertens; since both inventions are directed towards determining entities, facts and relationships in textual content, and incorporating the teaching of Williams into the invention suggested by Fauqueur and Mertens would provide the added advantage of allowing a knowledge base to be enhanced by populating it with information gleaned from textual content, and the combination would perform with a reasonable expectation of success (Williams [195-200, 202-207, 310]).
Regarding claim 3, Fauqueur, Mertens and Williams teach the invention as claimed in claim 2 above.
Claim 7 further teaches that an entitiy may be a person or a business, and
converting the electronic resource into relationship vectors based on outputs of the first, second and third classifiers, comprises inserting at least some of the Entities as values in a plurality of data statements.
Fauqueur further teaches wherein at least one of the Entities is ...present... as a value in a first one of the plurality of the ... relationship vectors... that contains a first fact about a first ...entity... and is ...present... as a value in a second one of the plurality of ... relationship vectors... that contains a second fact about a second ...entity... (Fauqueur [101, 114, 155] sentences may have entities that are nouns and subjects and objects, which may be related to other entities and facts in the sentence).
Claim(s) 14, 15, is/are dependent on claim 13 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2, 3, respectively, and is/are rejected under the same rationale.
Claims 5, 6, 8, 17, 19, are rejected under 35 U.S.C. 103 as being unpatentable over Fauqueur (US 20230351111 A1) in view of Mertens (US 10366151 B1), and further in view of Estes (US 11640494 B1).
Regarding claim 5, Fauqueur and Mertens teach the invention as claimed in claim 1 above. Fauqueur does not specifically teach wherein said performing machine learning operations by the second classifier further comprises assigning a first Entity of the Entities to both a first segment of textual content that is associated with a first person and a second segment of the textual content that is associated with a second person
However Estes teaches wherein said performing machine learning operations by the second classifier further comprises assigning a first Entity of the Entities to both a first segment of textual content that is associated with a first person and a second segment of the textual content that is associated with a second person (Estes Col 14, line 45- Col 15, line 25, entity resolution may be used to resolved multiple entities to a single representative entity to indicate that the multiple entities all refer to a single entity).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Estes of wherein said performing machine learning operations by the second classifier further comprises assigning a first Entity of the Entities to both a first segment of textual content that is associated with a first person and a second segment of the textual content that is associated with a second person, into the invention suggested by Fauqueur and Mertens; since both inventions are directed towards determining entities associated with text, and incorporating the teaching of Estes into the invention suggested by Fauqueur and Mertens would provide the added advantage of allowing a person to be identified as the same entity even if they are referred to in different ways, and the combination would perform with a reasonable expectation of success
(Estes Col 14, line 45- Col 15, line 25).
Regarding claim 6, Fauqueur and Mertens teach the invention as claimed in claim 1 above. Fauqueur does not specifically teach wherein the relationships of the Entities that are recognized by the third classifier comprise at least one of an educational relationship, a work relationship, a family relationship, or an interest relationship
However Estes teaches wherein the relationships of the Entities that are recognized by the third classifier comprise at least one of an educational relationship, a work relationship... (Estes Col 10, lines 50-64, entity relationships may be based on work or education).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Estes of wherein the relationships of the Entities that are recognized by the third classifier comprise at least one of an educational relationship, a work relationship..., into the invention suggested by Fauqueur and Mertens; since both inventions are directed towards determining entities associated with text
, and incorporating the teaching of Estes into the invention suggested by Fauqueur and Mertens would provide the added advantage of using text information to determine work and education information about an entity, and the combination would perform with a reasonable expectation of success (Estes Col 10, lines 50-64).
Regarding claim 8, Fauqueur and Mertens teach the invention as claimed in claim 7 above. Fauqueur does not specifically teach wherein the autonomous operations comprise: scanning content of the first window for an identifier of a person or business entity; searching a datastore for at least one said relationship vector which is associated with a person or business entity identified by the identifier; and presenting at least one said fact which was retrieved from the datastore in the first window or a second window displayed concurrently with the first window
However Mertens teaches wherein the autonomous operations comprise: scanning content of the first window for an identifier of a person or business entity; ... and presenting at least one said fact which was retrieved ... in the first window or a second window displayed concurrently with the first window (Mertens Col 7, line 450 Col 8, line 28, Mertens Col 3, lines 14-29, Fig. 3, based in user typing content within application- entities within types content are determined, information related to entities of interest are determined and presented in current display, may be displayed within an application window, Mertens Col 2, line 37- Col 3, line 13, textual content may have facts and be associated with entities, entities may include people and businesses).
Fauqueur and Mertens does not specifically teach searching a datastore for at least one said relationship vector which is associated with a person or business entity identified by the identifier
However Estes teaches searching a datastore for at least one said relationship vector which is associated with a person or business entity identified by the identifier (Estes Col 12, line 63- Col 13, line 35, Fig. 3, datastore may be searched for features vector of entities and/or relationship of interest may be search in datastore to determine information of interest).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Estes of searching a datastore for at least one said relationship vector which is associated with a person or business entity identified by the identifier, into the invention suggested by Fauqueur and Mertens; since both inventions are directed towards determining entities associated with text, and incorporating the teaching of Estes into the invention suggested by Fauqueur and Mertens would provide the added advantage of allowing using stored information to determine information of interest, and the combination would perform with a reasonable expectation of success (Estes Col 12, line 63- Col 13, line 35, Fig. 3).
Claim(s) 17, 19, is/are dependent on claim 13 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 5 and 8 respectively, and is/are rejected under the same rationale.
Conclusion
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146