Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. Claims 40-48 are presented for examination.
3. This office action is in response to the claims filed 10/01/2025.
4. Claims 40 and 43 are independent claims.
5. In view of the REM on 10/01/2025, PROSECUTION IS HEREBY REOPENED.
6. The office action is made non-final.
Claim Rejections - 35 USC § 112
7. 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.
8. Claims 40-48 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claims 40 and 43 recite “such as, but not limited to”. One skilled in the art could not interpret the scope of this claim because the “such as” is an ambiguous phrase which don't sufficiently specify the scope, leaving it open to interpretation for someone skilled in the art. As such, this renders the claims unclear, vague and indefinite, failing to precisely define the invention's boundaries as required by 35 U.S.C. § 112(b).
The dependent claims also are rejected because they inherit the deficiencies of claims 40 and 43 and therefore are rejected under the same rationale.
Examiner Note
9. The Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 102
10. 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 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.
11. 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;
12. Claims 40-48 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Mark et al (US 20130066921 A1) hereinafter as Mark.
Per definition: Ontology pruning is the process of systematically reducing the size and complexity of an existing, often large and general, ontology to create a smaller, more focused sub-ontology that is relevant to a specific application or domain.
13. Regarding claim 40, Mark teaches an Apparatus for pruning an ontology without loss of relevant function ([0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]), the apparatus including an electronic processing device (Fig 1) that generates a pruned ontology by:
a. Accepting a list of desired ontological concepts, object properties and data properties from a user (Fig 2, [0033], Fig 11, step 81, [0051], “gathers all observable data, such as query history, click stream data, data from personal sources such as email, contacts, accounts, leads, etc. At 84, this data is then analyzed and conceptualized. This may include identifying recursive patterns, meaning and intent based upon corrections and click stream data, preferences through explicit or implicit indicators, and identifying lingo, usage and concept synonyms.”);
b. identifying those ontological properties such as, but not limited to, concepts, object properties and data properties in the ontology to be pruned ([0003], “Common components of an ontology include objects, instances, classes, attributes, relations, restrictions, rules, axioms and events.”, Fig 2, [0033], Fig 11, step 81, [0051]);
c. identifying all relationships from the identified properties to other properties in the ontology to be pruned ([0022], “Adaptive Ontology typically consist of five phases: concept identification; relationship identification; concept inclusion; concept exclusion; and concept and relationship personalization.”, [0025], “To perform relationship identification, the controller uses indexing, clustering, classification and frequency counts to identify relationships between newly discovered concepts and existing concepts. Using this information, the controller determines possible relationships between the newly discovered concept and the current ontology.”, Figs 5 & 6, [0038], “The controller moves on to Relationship Identification to identify relationships between this new node and already existing concepts in the Ontology.” Figs 7 & 8, [0041], “FIG. 8 shows the relationships chosen from among the possibilities for the ontology map 40”, Fig 11, step 86, [0052], “Changes are identified at 86 by identifying new objects, identifying relationships between subsystem objects and existing objects. Once the change has been identified, such as updating the relationship on the ontological map, the change is executed at 88”);
d. identifying all relationships to the identified properties to the identified properties in the ontology to be pruned ([0022], “Adaptive Ontology typically consist of five phases: concept identification; relationship identification; concept inclusion; concept exclusion; and concept and relationship personalization.”, [0025], “To perform relationship identification, the controller uses indexing, clustering, classification and frequency counts to identify relationships between newly discovered concepts and existing concepts. Using this information, the controller determines possible relationships between the newly discovered concept and the current ontology.”, Figs 5 & 6, [0038], “The controller moves on to Relationship Identification to identify relationships between this new node and already existing concepts in the Ontology.” Figs 7 & 8, [0041], “FIG. 8 shows the relationships chosen from among the possibilities for the ontology map 40”, Fig 11, step 86, [0052], “Changes are identified at 86 by identifying new objects, identifying relationships between subsystem objects and existing objects. Once the change has been identified, such as updating the relationship on the ontological map, the change is executed at 88”);
e. accepting various restraints and/or filters from the user ([0003], “Restrictions define the constraints placed on classes, objects and entities. Rules define conditions and results such as those in if-then-else statements, logical inferences, etc.”) to:
i. control possible types of relationships between properties ([0020], “an Adaptive Ontology Controller”, [0023], [0025]);
ii. Limit length of relationship chains (Fig 7, [0041], “The thickness of the line indicates the affinity of the relationship.”, [0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]);
iii. exclude properties by type or value ([0045], “If the affinity falls below a certain threshold, the controller excludes the concept from the ontology map automatically.”)
f. identifying those properties which are excluded by the filters ([0045], “If the affinity falls below a certain threshold, the controller excludes the concept from the ontology map automatically.”);
g. generating a Previously Presented ontology based upon the desired ontological concepts and the properties which passed the filters (Fig 7 & 8, [0041], [0047]);
h. treating the generated ontology as a set of desired ontological properties and repeating steps (b) to (h) until no Previously Presented properties are identified in step (d) ([0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]).
14. Regarding claim 41, Mark teaches the invention as claimed in claim 40 above and further teaches wherein the electronic processing device utilizes a rules-based approach ([0003], “Restrictions define the constraints placed on classes, objects and entities. Rules define conditions and results such as those in if-then-else statements, logical inferences, etc.”).
15. Regarding claim 42, Mark teaches the invention as claimed in claim 40 above and further teaches wherein the electronic processing conditionally allows a user to prune an ontology based upon the user being authorized ([0015], “Virtual personal assistants for knowledge workers must have the ability to personalize, customize, and adapt to each specific user of the system.”, [0032-0033], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology”).
16. Regarding claim 43, Mark teaches Apparatus for browsing and editing an ontology with automatic enforcement of relevant function ([0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]), the apparatus including an electronic processing device that generates an editable image of an ontology by:
a. Accepting a user specified ontology (Fig 2, [0033], Fig 11, step 81, [0051], “gathers all observable data, such as query history, click stream data, data from personal sources such as email, contacts, accounts, leads, etc. At 84, this data is then analyzed and conceptualized. This may include identifying recursive patterns, meaning and intent based upon corrections and click stream data, preferences through explicit or implicit indicators, and identifying lingo, usage and concept synonyms.”);
b. Generating a visual representation of that ontology consisting of concepts and object properties (Figs 3-8);
c. Accepting a point and click identification of a specific item in the displayed ontology ([0026], “The controller continues an ongoing process of strengthening or weakening the affinity index for newly included concepts based upon user inputs such as query requests and click stream data.”, [0033], “the user's `click stream` or the history of selections made through a graphical user interface such as a web page by clicking on a particular selection, provides information as to not only the links selected, but also on a particular sequence of selections.”, [0037], [0045], “The controller will then monitor the user's inputs, such as queries and click stream analysis.”, [0047], “the controller monitors users query requests and response click stream.”);
d. identifying those ontological properties such as, but not limited to, concepts, object properties, data properties and notations associated with the ontology item identified by the user ([0003], “Common components of an ontology include objects, instances, classes, attributes, relations, restrictions, rules, axioms and events.”, Fig 2, [0033], Fig 11, step 81, [0051]);
e. optionally restricting the visualization to only those properties to immediately related properties in the ontology to be edited ([0003], “Restrictions define the constraints placed on classes, objects and entities. Rules define conditions and results such as those in if-then-else statements, logical inferences, etc.”);
f. identifying all relationships to the identified properties to the identified properties in the ontology to be edited ([0022], “Adaptive Ontology typically consist of five phases: concept identification; relationship identification; concept inclusion; concept exclusion; and concept and relationship personalization.”, [0025], “To perform relationship identification, the controller uses indexing, clustering, classification and frequency counts to identify relationships between newly discovered concepts and existing concepts. Using this information, the controller determines possible relationships between the newly discovered concept and the current ontology.”, Figs 5 & 6, [0038], “The controller moves on to Relationship Identification to identify relationships between this new node and already existing concepts in the Ontology.” Figs 7 & 8, [0041], “FIG. 8 shows the relationships chosen from among the possibilities for the ontology map 40”, Fig 11, step 86, [0052], “Changes are identified at 86 by identifying new objects, identifying relationships between subsystem objects and existing objects. Once the change has been identified, such as updating the relationship on the ontological map, the change is executed at 88”);
g. Accepting various restraints and/or filters from the user ([0003], “Restrictions define the constraints placed on classes, objects and entities. Rules define conditions and results such as those in if-then-else statements, logical inferences, etc.”) to:
i. control possible types of relationships between properties ([0020], “an Adaptive Ontology Controller”, [0023], [0025]);
ii. Limit length of relationship chains (Fig 7, [0041], “The thickness of the line indicates the affinity of the relationship.”, [0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]);
iii. exclude properties by type or value ([0045], “If the affinity falls below a certain threshold, the controller excludes the concept from the ontology map automatically.”)
h. visually indicating those properties which are excluded by the filters ([0045], “If the affinity falls below a certain threshold, the controller excludes the concept from the ontology map automatically.”);
i. accepting input from a user to either define Previously Presented ontology properties, or to modify or delete existing ontology properties ([0017], “a system, method, and apparatus for adapting current knowledge based on user preferences as well as improving, changing, and/or modifying knowledge based on explicit and implicit user feedback, user data such as from user profiles, and preference learning.”, Fig 7 & 8, [0041], [0047]);
j. validating the integrity of user input ([0017], “a system, method, and apparatus for adapting current knowledge based on user preferences as well as improving, changing, and/or modifying knowledge based on explicit and implicit user feedback, user data such as from user profiles, and preference learning.”, Fig 7 & 8, [0041], [0047])
k. repeating all steps (c ) to (j) ([0019], “Adaptive Ontology enables the system to adapt its ontology so that new concepts and relationships can be developed or strengthened based on machine learning.”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.” [0044], [0048], “a change refinement subsystem”, [0050]); and
1. generating a Previously Presented ontology based upon the user input (Fig 7 & 8, [0041], [0047]).
17. Regarding claim 44, Mark teaches the invention as claimed in claim 43 above and further teaches wherein the electronic processing device utilises a rules-based approach ([0003], “Restrictions define the constraints placed on classes, objects and entities. Rules define conditions and results such as those in if-then-else statements, logical inferences, etc.”).
18. Regarding claim 45, Mark teaches the invention as claimed in claim 43 above and further teaches wherein the electronic processing displays data defined by the ontology being displayed (Figs 3-8).
19. Regarding claim 46, Mark teaches the invention as claimed in claim 45 above and further teaches wherein the electronic processing accepts Previously Presented or edited data from a user ([0017], “a system, method, and apparatus for adapting current knowledge based on user preferences as well as improving, changing, and/or modifying knowledge based on explicit and implicit user feedback, user data such as from user profiles, and preference learning.”, Fig 7 & 8, [0041], [0047]).
20. Regarding claim 47, Mark teaches the invention as claimed in claim 45 above and further teaches wherein the electronic processing conditionally accepts Previously Presented or edited data from a user based upon the user being authorized to read, update of add data ([0015], “Virtual personal assistants for knowledge workers must have the ability to personalize, customize, and adapt to each specific user of the system.”, [0032-0033], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology”).
21. Regarding claim 48, Mark teaches the invention as claimed in claim 47 above and further teaches wherein the ontological visualization generated by the electronic processing device may be saved as a self-contained set computer programs and screen definitions and deployed as a data maintenance application ([0054], “an adaptive ontology provides a learning system that customizes itself in an automated fashion for a particular user.”).
CONCLUSION
22. The prior art made of record and not relied upon is considered pertinent to applicant s disclosure.
Mirhaji et al (US 9542647 B1)
Miranker et al (US 20150269223 A1)
Sacco (US 20080133490 A1)
Seal (US 20150178372 A1)
Turdakov et al (US 20130138696 A1)
Gattani et al (US 8626491 B2)
Mehra et al (US 20100280989 A1)
Hwang et al (US 20020078090 A1)
Guo et al (US 20140279837 A1)
Tsatsou et al (US 20100281025 A1)
He et al (US 20130018828 A1)
Coletto (WO 2015059637 A1)
Fang (WO 2014134796 A1)
Hu (EP 2731062 A1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HICHAM SKHOUN whose telephone number is (571)272-9466. The examiner can normally be reached Normal schedule: Mon-Fri 10am-6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached at 5712701698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HICHAM SKHOUN/Primary Examiner, Art Unit 2164