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 REM filed 05/06/2026.
4. Claims 40 and 43 are independent claims.
5. The office action is made Final.
Examiner Note
6. 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
7. 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.
8. 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;
9. 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.
10. 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 ([0022], “Adaptive Ontology enables a virtual personal assistant to adapt to its user's needs (desired ontological) just like a real assistant. Adaptive Ontology typically consist of five phases: concept identification”, [0023], “Concept identification specifies a new concept that needs to be added to the ontology map. adapts the ontology and includes the new concept in a process referred to as adaptive ontology concept identification”, [0026], “newly included concepts based upon user inputs such as query requests and click stream data.”, [0036], “The process of concept identification specifies that these are new concepts.”),
object properties ([0003], “Common components of an ontology include objects. Objects are entities such as a person, a company, a name, etc. Attributes are properties and characteristics that an object or a class may have.”, [0023], “An ontology map is a topological representation of objects in an ontology and how they relate to each other.”),
data properties ([0017], “changing, and/or modifying knowledge based on explicit and implicit user feedback, user data such as from user profiles, and preference learning.”, Fig 2, [0020], “click stream data”, [0036], Fig 11, [0051], “the controller gathers all observable data, such as query history, click stream data, data from personal sources such as email, contacts, accounts, leads, etc.”),
annotations and labels from a user ([0003], “Common components of an ontology include objects, instances, classes, attributes, relations, restrictions, rules, axioms and events.”, Fig 2, [0033], “In FIG. 2, the learning module 22 receives as inputs user queries, where the user directly enters a query into the system. The query history 36, the user's `click stream` or the history of selections made through a graphical user interface. data sources and markers 38 and explicit indicators 40. Other inputs may include implicit indicators, user profiles, user demographics, and even psychographic analysis of past behaviors. All of this information is used by the learning module to develop personalization of the ontology map for the controller 30.” Fig 11, step 81, [0051], “gathers all observable data, such as … 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 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 those ontological properties associated with the ontology identified by the user ([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 new ontology based upon the desired ontological concepts and the properties which passed the filters ([0018], “Adaptive Ontology provides a method and mechanism for ontologies to adapt to users' terminology, usage patterns, preferences and priorities”, [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”, [0022], “Adaptive Ontology enables a virtual personal assistant to adapt to its user's needs just like a real assistant.”, [0026], “After determining the affinity index between the newly discovered concept and nodes in the ontology map, the controller will pick relationships with the greatest affinity index. It will then update the ontology to include the new concept in the process of concept inclusion.”, [0033], “The query history 36 of a particular user allows the system to identify particular terms that may be unique to the user and customize ontology according”, 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 new 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.”, [0024], “The deep analysis”, [0027], “pruning ontology”, [0032], “the interactions with the user provide information to the system that allows the system to adapt and refine the ontology.”, [0040], “This process may then repeat by iterating on the Key Object to any desired depth or dimension.”, [0044], [0048], “a change refinement subsystem”, [0050]).
11. 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.”).
12. 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”).
13. 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 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 new 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 new ontology based upon the user input ([0018], “Adaptive Ontology provides a method and mechanism for ontologies to adapt to users' terminology, usage patterns, preferences and priorities”, [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”, [0022], “Adaptive Ontology enables a virtual personal assistant to adapt to its user's needs just like a real assistant.”, [0026], “After determining the affinity index between the newly discovered concept and nodes in the ontology map, the controller will pick relationships with the greatest affinity index. It will then update the ontology to include the new concept in the process of concept inclusion.”, [0033], “The query history 36 of a particular user allows the system to identify particular terms that may be unique to the user and customize ontology according”, Fig 7 & 8, [0041], [0047]).
14. 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.”).
15. 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).
16. Regarding claim 46, Mark teaches the invention as claimed in claim 45 above and further teaches wherein the electronic processing accepts new 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]).
17. Regarding claim 47, Mark teaches the invention as claimed in claim 45 above and further teaches wherein the electronic processing conditionally accepts new 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”).
18. 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.”).
Respond to Amendments and Arguments
19. In the remarks received 05/06/2026, Claims 40, 43, 46, and 47 have been amended for clarification, and Applicant respectfully submits that Mark does not teach or suggest the features of claims 40 or 43.
Regarding claim 40, Mark differs from the present application as it is generating an ontology, not pruning it. The pruning ontology in Mark differs from the present application because Mark teaches purging data potentially used to create an ontology, not pruning an existing ontology. Mark's adaptive ontology is being built, and is adding elements, not pruning them. The claims differ from the teachings of Mark which relate to gathering all observable data from disparate data to create an ontology and does not prune existing ontology. Mark merely identifies data which could become part of an ontology.
Mark differs from the current application as it describes how the disparate data is analyzed in order to create an ontology and uses the data to map it to the terminology required to create an ontology, but never does the reverse.
Mark also differs from the current application as it defines restrictions applied to creating ontological terms from the disparate data whereas the current application defines which ontological paths should be examined with a view to pruning those paths.
Mark also does not teach or suggest generating a new ontology based upon the desired ontological concepts and the properties which passed the filters.
Regarding claim 42, Mark allows the user to prune disparate data in order to create an ontology, whereas the current application allows the user to identify which parts of an ontology should be retained in order to meet their requirements.
Regarding claim 43, Mark does not contain anything relating to pruning an existing ontology, at no stage does the user of the disclosure in Mark need to accept a user specified ontology.
Mark refers to the strength/affinity of a single relationship to determine if it is, in fact, an object property/Relationship for inclusion in the ontology being created at 88. It does not refer to an analysis of the related properties of a set of relationships which have already been defined in the ontology.
Examiner presents the following responses to Applicant’s arguments:
Applicant's arguments received on 05/06/2026 have been fully considered but they are not persuasive. Referring to the previous Office action, Examiner has cited relevant portions of the references as a means to illustrate the systems as taught by the prior art. As a means of providing further clarification as to what is taught by the references used in the first Office action, Examiner has expanded the teachings for comprehensibility while maintaining the same grounds of rejection of the claims, except as noted above in the section labeled “Status of Claims.” This information is intended to assist in illuminating the teachings of the references while providing evidence that establishes further support for the rejections of the claims.
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.
Mark teaches the features of claims 40 or 43. Examiner direct Applicant attention to the preamble of the independent claim 40, “Apparatus for pruning an ontology without loss of relevant function” and claim 43, “Apparatus for browsing and editing an ontology with automatic enforcement of relevant function”, which are equivalent of what Mark discloses, see:
Abstract “a relationship identification sub-system to create relationships between at least some of the plurality of identified knowledge concepts, and attribute affinity weights to the relationships, a change refinement sub-system to modify at least one of the plurality of nodes, affinity weights and relationships based upon information associated with the user, and a non-transitory knowledge store to store the information associated with the user pertaining to a sub-plurality of the plurality of identified knowledge concepts.”, [0018], “Adaptive Ontology provides a method and mechanism for ontologies to adapt to users' terminology, usage patterns, preferences and priorities”, [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.” [0025], “Using this information, the controller determines possible relationships between the newly discovered concept and the current ontology.”, [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]).
CONCLUSION
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HICHAM SKHOUN/Primary Examiner, Art Unit 2164
Prosecution Insights