DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/25/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code.(Specification, “[pg-2, line 25-30] The document titled 'SBVR Business Rules Generation from Natural Language Specification" published on January 31, 2011, and available on "https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.450.8989&rep=repl &type=p df" discloses a tool for automatically translating natural language specification to SBVR30 business rules. However, id SVBR rule system does not provide support for AS interfaces, as AS interfaces refer to concepts like sensors, actuators and metrics”). Applicants are required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 1, 3 ,6-8, 10 and 13 are objected to because of the following informalities:
As to Claim 1, 3 ,6-7, 10, 13 and 16:
generating an SR of the new rule template […] should read as generating a SR of the new rule template […].
Claims 2-7 are further objected to by virtue of their dependency on claim 1.
As to Claim 7
generating an NL based audit report to […] should read as generating a NL based audit report to […]
As to Claim 8
an NL processor for generating […] should read as a NL processor for generating […]
an NL generator for generating […] should read as a NL generator for generating […]
an NL feedback including […] should read as a NL feedback including […]
Claims 9-15 are further objected to virtue of their dependency on claim 1.
As to Claim 14
generate an SR of each retrieved rule template […] should read as generate a SR of each retrieved rule template […]
communicate an NL based audit report […] should read as communicate a NL based audit report […]
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function.
(B) the term “means”, or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means”, or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 8:
“NL processor for generating”
“a semantic data mapper for creating “
“a semantic data extractor for generating “
“a semantic data comparator for automatically comparing”
“NL generator for generating.”
Claim 15:
“a sensor and actuator catalog configured to store”
“A rule database configured to store”
“a resource definition database configured to store”
a rule engine configured to create …
“A rule execution database configured to store”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Dependent claims not mentioned inherit the deficiencies of their parent claims.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation(s) 8-15 as stated above invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. (i.e., no association between the structure and the function can be found in the specification.) The specification is devoid of adequate structure to perform the claimed function. Specification [pg-11, line 10-15] The NL processor 108 receives the textual NL representation of the user command from the NL transcriber 106 and extracts the semantic representation (SR) of the user command therefrom. […] Under BRI, in light of specification, the “processor” doesn’t necessarily mean a hardware CPU or NPU. The NL processor is interpreted as being implemented in software to process NL data. Specification [ pg-11, line 20-25], “The semantic data mapper 110 runs the algorithms responsible for implementing the rule definition, acknowledgment and audit requests […]” , “[pg-11, line 25-30] “The semantic data extractor 112 translates the rule abstractions and metadata exposed […].[pg13] The semantic data comparator 114 automatically compares [...] [line 20-25] The NL generator 116 converts an SR model into a NL string. [...] [pg-10, lines 11-16] The sensor and actuator catalog 122 is a database containing metadata information about all sensors and actuators that are supported by AS 104. […] [pg-10, lines 19-35] The rule templates catalog 124 (may be hereinafter also referred to as a rule database 124) is a database containing all automation rule templates currently registered with the AS 104. […]. The resource definitions database 126 contains the definitions for a set of resources associated with the environment, like for example a power cabinet, an HVAC system or an automated door. […] The rule executions database 128 contains a history of all rule instance executions and their runtime data […] The rule engine 130 of the AS 104 creates a corresponding entry in the rule executions database 128, comprising runtime data including but not limited to trigger conditions, [..]There is no disclosure of any particular structure, either explicitly or inherently, to perform the function stated above as claim limitation of claim 8-15. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which filter structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Every dependent claim is subject to the same rejection(s) as the independent claim from which it depends.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1:
The limitation is: “generating a semantic representation (SR) of a textual NL representation of a user command”. There is insufficient antecedent basis for this limitation in the claim. The abbreviation ‘NL’ makes the scope of the claim unclear on what NL represents. Examiner suggests claim limitation should read as “generating a semantic representation (SR) of a textual ‘Natural language (NL)’ representation of a user command”.
Claims 2-7 are further rejected on virtue of their dependencies to claim 1.
As to claim 6
The limitation “command includes an audit query for the AS in response to an observed characteristic of the environment”. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 is further rejected on virtue of dependency to claim 6.
As to claim 13
The limitation “command includes an audit query for the AS in response to an observed characteristic of the environment”. There is insufficient antecedent basis for this limitation in the claim.
Claims 14 and 15 are further rejected on virtue of their dependency on claim 13.
As to claim 15
The limitation “an API communicatively coupled to the NLI”. There is insufficient antecedent basis for this limitation in the claim. The abbreviation ‘NLI’ makes the scope of the claim unclear on what NLI represents. Examiner suggests claim limitation should read as “an API communicatively coupled to the Natural language Interface (NLI)”.
As to claim 16
The limitation “generating a semantic representation (SR) of a textual NL representation of a user command”. There is insufficient antecedent basis for this limitation in the claim. The abbreviation ‘NL’ makes the scope of the claim unclear on what NL represents. Examiner suggests claim limitation should read as “generating a semantic representation (SR) of a textual ‘Natural language (NL)’ representation of a user command”.
The limitation “includes the new rule report including the new rule template” There is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 8-15 is/are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function of the Claim 8-15. The specification does not demonstrate (see the reason stated above) that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Examiner Remarks
In light of Specification,(“[pg-4, line 8-13] Hence, in view of the above, there is a need for a system and method that not only enable a user to define and audit rules in a knowledge-based automation system (AS) using semantic representation of natural language processing without requiring any technical computer coding skills, but also enable two-way communication with a user in NL to provide feedback to a user during run time to enable a user to review and revise the created rules, and also answer queries about created rules.”). and examining elements as recited by the limitations individually and as an ordered combination, as a whole the claim limitations recite what the courts have identified as “significantly more”.
Therefore, claims are eligible subject matter under 35 USC § 101.
Claim Rejections – 35 USC § 103
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.
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.
Claims 1-4, 8-11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bajwa, et. al. “SBVR Business Rules Generation from Natural Language Specification” (“Bajwa”) in view of Armstrong et. Al. European Patent Application: EP-1672537 -A2 (“Armstrong “)
As to Claim 1
Bajwa teaches “computer-implemented method for defining and auditing rules in a knowledge-based automation system (AS), comprising:” (Bajwa, “[Abs] In this paper, we propose an automated approach that automatically translates (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. [defining and auditing rules in a knowledge-based automation system (AS)]. We have used a rule-based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation [A computer-implemented method] of SBVR based Business rules can help in improved and efficient constrained business aspects in typical business modelling.
2. generating a semantic representation (SR) of a textual NL representation of a user command. (Bajwa, “[Abs] In this paper, we present a novel approach of translating [generating] natural languages specification [textual NL representation of a user command] to SBVR business rules [a semantic representation (SR)].We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules.(pg.- 3)In this paper, we present a novel approach NL2SBVR to translate natural language (such as English) specification of business rules to SBVR rules. SBVR rules are generated from the output of the NLP module. To generate SBVR rules, the first step is to create a fact type. A fact type is created by mapping the noun concepts and verb concepts to the fact types available in the SBVR vocabulary array list.”)
3. generating an SR of the new rule template generating an SR of the new rule template based on a rule-based graph model of the AS. (Bajwa, “[pg-3] Business vocabulary defines a particular business domain. In SBVR 1.0, business vocabulary can have two major types of elements [3]: Concepts and Fact Types. A concept is a key term that represents a business entity in a particular domain. […]. In our research we use a UML class model as a business domain. Hence, the business vocabulary comes from the target UML class model. With respect to UML class model: [based on a rule-based graph model of the AS] class names and their attributes names are represented as noun concepts, object names are represented as individual concepts, operation names are named as action verbs, and the associations and generalizations are represented as fact types.[ generating an SR of the new rule template]
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In example of figure 2, person, vehicle, car are noun concepts. Similarly, the getName, getAge, getSalary are verb concepts and association ownership is fact type in SBVR. In SBVR, the business rules represent particular business logic in a specific context. Each SBVR business rule is based on at least one fact type. […] Our tool Rule Generator supports both SBVR notations.”).
Bajwa does not teach:
creating a new rule template for the AS based on the SR of the user command when the user command includes a new rule definition request.
storing the new rule template in a rule database of the AS in an inactive state
automatically comparing the SR of the new rule template with the SR of the user command
to automatically determine a deviation of the new rule template from the user command during run-time
integrating comparison results into NL feedback for providing to the user, wherein the NL feedback includes a new rule report including the new rule template, and the determined deviation
receiving a user's NL response to the NL feedback
and automatically updating during run-time, a state of the new rule template in the rule database based on the SR of the user response
Armstrong teaches “creating a new rule template for the AS based on the SR of the user command when the user command includes a new rule definition request;” (Armstrong, “paragraph [0014] The domain expert or the system administrator can be human, computer implemented, or the [user command] any combination thereof. Operation 102 [includes a new rule definition request;] generates a semanticization rule set 110. [creating a new rule template for the AS] Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110.”).
Examiner notes: Creating a new rule template For the AS of the user command is interpreted as generating semanticiation rule 110 and definition request is interpreted as processing operation in 102. The system generates rule template as a result of rule creation from the user command.
2. storing the new rule template in a rule database of the AS in an inactive state; (Armstrong “Paragraph [0014] Operation 102 generates a semanticization rule set 110. Once, at operation 102, [in an inactive state] the rule set 110 is available, at operation 104, [storing the new rule template] semantic instance(s) 118 can be generated based upon the rule set 110. [in a rule database of] A "semantic instance" 118 is a set of description(s) on an individual item based on a concept(s). An item(s) can be any part of input data 108. generating an SR of the new rule template based on a rule-based graph model of the AS;”).
Examiner notes: Inactive state is interpreted as operation 102 where rule is generated but not yet applied and rules are stored between the operation 102- and operation 104.
3. automatically comparing the SR of the new rule template with the SR of the user command, (Armstrong, paragraph 0050, 0067 “[0050] The parser then applies the regular expression to each record in the sample data 114, recording the start and end positions of any matches it finds. [automatically comparing the SR of the new rule template ] [0067] selecting, at least one ontology 116, which typically in the present invention is selected by a user; at operation 254, selecting one (or more as the case may be) input data from among the input data 108; at operation 256, selecting an ontology concept from the selected ontology 116, which typically in the present invention is selected by the user; at operation 258, mapping the selected ontology concept to the one (or more) input data selected, which typically in the present invention incorporates the user's assistance/interaction; at operation 260, generating a mapping or data structure capture rule based upon the mapping of the selected ontology concept to the one (or more) input data, [with the SR of the user command] which is performed by the semanticization rule editor 106;“).
4. To automatically determine a deviation of the new rule template from the user command during run-time;(Armstrong “paragraph [0051] After each record has been processed, the total number of matches for a particular regular expression is Checked. The regular expression is rejected automatically, if the number of match count does not fall within the tolerance level (the number of records ± the tolerance value). [automatically determine a deviation of the new rule template from the user command during run-time;] In this case, the parse returns to operation 2.”).
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5. integrating comparison results into NL feedback for providing to the user, wherein the NL feedback includes a new rule report including the new rule template, and the determined deviation; (Armstrong, paragraph, 0054 0072, 0073, 0074, 0075, “[0054] At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject the suggestion of the regular expression as the structure rule of [into NL feedback for providing to the user ][…][0072] In FIG. 6, for each selected ontology class and all of its properties mapped to a data point 108, as shown in the ontology viewer 200 and the rule viewer 204 (i.e., indicated, via a mapping by selecting "Add a Rule" 300, by the same "K" value, which in this example is highlighted orange for COMMENT (Description: ... ), highlighted yellow for NAME, highlighted red for SEQUENCE, highlighted dark green for SHORT-NAME, and highlighted light green for SYNONYMS),the "mapping rules" are determined based on "R" data structure capture element by associating a data point (e.g., text) with a rule, via the "Associate Text with Rule" 302 (operation 260 in FIG. 5) and providing suggested matches 306 for acceptance, rejection and/or optimization (operations 262, 264 and/or 266 in FIG. 5). [providing to the user]In particular, FIG. 6 shows that the parser 106 has just completed for data point 205 discovering similar data 308 for the NAME ontology class property, in a remainder of a sample 114 of a database 108, which is highlighted in yellow upon selecting "Associate Text with Rule" 302 and the parser 106 provides similar data suggestions 308 displayed by red color font. [integrating comparison results] [0073] Upon acceptance of suggestions and a successful completion of an error checking mechanism, a semantic instance can be created, via "Generate an Instance" selection 304, using the following procedure. [0073] Upon acceptance of suggestions and successful completion of an error checking mechanism, a semantic instance can be created, via "Generate an Instance" selection 304, using the following procedure. For each row of the same color "K," create an instance of the class with property values using "column" information stored. [a new rule report including the new rule template]. [0075] 2. Run Error Checking Mechanisms: This data validation process contains a set of tests to check for errors from the data files, e.g. the correct data files are being properly semanticized; [and the determined deviation].”).
6. receiving a user's NL response to the NL feedback; (Armstrong, “paragraph [0054] at operation 156, the user can either accept or reject [receiving a user's NL response to] the suggestion [NL feedback] of the regular expression as the structure rule of the input data 108”).
7. and automatically updating during run-time, a state of the new rule template in the rule database based on the SR of the user response. (Armstrong, Paragraph 0067, 0071 “[0067] at operation 268, semanticizing the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108, based upon an acceptable mapping suggestion, which typically in the present invention a mapping rule is accepted, if the user accepts a mapping suggestion by the semanticizer rule editor 106 that maps the selected ontology concept to the sample input data 114.[0071] Although the description herein with reference to FIGS. 6-7 is directed to instance generation for all data points from open data files 108 [in the rule database] in the data pane 202 (three data points >gi ... are displayed in the data pane 202 of FIG. 6), a user may choose to create semantic instances of a few selected [based on the SR of the user response] data points from open data files 108. This is an important capability since the data semanticizer 100 can generate updated semantic instances 118 as needed on demand. [and automatically updating during run-time, a state of the new rule template] For example, a single record from a database 108 can be annotated and used instead of generating a large set of semantic instances from all the records in the database 108.”).
Armstrong and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Armstrong it would have been obvious for a person of ordinary skill in the art to apply the teachings of Armstrong to Bajwa before the effective filing date of the claimed invention to generate a large set of semantic rule set, reducing error and increasing efficiency while applying rules to the data. (Armstrong, “paragraph [0026] Priority is used to increase efficiency while reducing errors, when, at operation112, applying a plurality of generated mapping rules 110 of the input data 108. Priority can be used to determine erroneous application of a rule set 110. When high priority rules cannot be applied, semantic instance creation process stops, whereas low priority rules can be safely ignored.”) Additionally, one would be motivated to do so to speed up mapping, and quickly and effectively incorporate user assistance to turn tedious tasks of data structuring into a collaborative, efficient process. (paragraph [0019] However, the data semanticizer 100 can efficiently (e.g., simply, quickly, and highly effectively) incorporate assistance from a user, which will make the process of capturing
the structure of data 108 easier. However, the data semanticizer 100 substantially reduces expert human assistance and dependency in semanticizing a large volume of data 108 in any format and in any domain.”)
As to Claim 16
Bajwa teaches “A non-transitory computer readable medium configured to store a program causing a processor of a computer” [Bajwa, [pg-4] Our tool Rule Generator supports both SBVR notations. [pg-3]. Our approach NL2SBVR is implemented in a tool ‘Rule Generator’ as a proof of concept [PG-6] To find the bugs in the working of the tool experiments were performed to carry out the dynamic verification of the software tool. [A non-transitory computer readable medium configured to store a program causing a processor of a computer].
Examiner notes: Bajwa discloses the use of rule generators and software tools. (Since, the software tool needs a processor/ memory / computer to run/execute, it is necessarily understood by one in the ordinary skill in the art that the processor and memory is disclosed by Bajwa to run a verification using software tool.
And for all the other limitations of Claim 16, it is rejected on the same basis as Claim 1. As the Claim are Analogous.
As to Claim 2
Bajwa, in view of Armstrong teaches the method of claim 1.
Bajwa further teaches “mapping the SR of the user command with the rule-based graph model of the AS to create the new rule template.” (Bajwa, “[pg-3], This algorithm is used in our approach to parse English text and extract SBVR syntactic elements. [the SR of the user command] Our approach extracts SBVR vocabulary from a class model. Afterwards, the SBVR elements are UML [1] mapped to SBVR vocabulary to ensure that the output SBVR rules will be targeting a particular business domain. Finally, the SBVR rule is [to create the new rule template]. generated from UML [rule-based graph model] mapped [mapping] SBVR elements.”).
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 1
As to Claim 3
Bajwa, in view of Armstrong teaches the method of claim 1.
Armstrong further teaches “generating an SR of the user response to the NL feedback during run-time” (Armstrong, paragraph [0069] the mapping of the selected ontology concept to the one input data, or any combination thereof, which typically in the present invention the mapping rule modification or optimization incorporates the user's assistance/interaction; and, at operation 266, [during run-time] if a mapping rule suggestion is accepted, at operation 268, semanticizing the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108, based upon an acceptable mapping suggestion [generating an SR of the user response to the NL feedback], which typically in the present invention a mapping rule is accepted, if the user accepts a mapping suggestion by the semanticizer rule editor 106 that maps the selected ontology concept to the sample input data 114
2. and automatically updating the inactive state of the new rule template in the rule database to an active state during run-time, (Armstrong, paragraph 0015, 0067 [0015], Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. [the new rule template in the rule database] [0067] at operation 266 [the inactive state] if a mapping rule suggestion is accepted, at operation 268, [an active state during run-time], (semanticizing [and automatically updating] the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108.
Examiner Notes: Inactive state is interpreted as operation 266 waiting user confirmation and active state is interpreted as operation 268 i.e. rule executing state. Automatically updating from inactive to active state is interpreted as state change upon approval of the new rule template i.e. mapping suggestion.
3. when the user approves the new rule template, user. (Armstrong, “Paragraph [54], at operation 156, the user examines the suggestion. At operation 156, the user can either accept or [when the user approves] rejects the suggestion of the regular expression as the structure rule [the new rule template], of the input data 108.”).
4. wherein in the active state, the new rule template is executable to perform an automated action (Armstrong, “Paragraph [14] Operation 102 generates a semanticization rule set 110. Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. A "semantic instance" 118 is a set of description(s) on an individual item based on a concept(s).Paragraph [67] at operation 266, if a mapping rule suggestion is accepted, if a mapping rule suggestion is accepted, at operation 268, [wherein in the active state] semanticizing the input data [an automated action] 108 by applying or populating the generated optimized mapping rule to entire input data 108,[ the new rule template is executable to perform] based upon an acceptable mapping suggestion, which typically in the present invention a mapping rule is accepted. ”).
Examiner notes: Under BRI, Active state is interpreted as at operation 268, as a rule executing state, and automated action is interpreted as semanticizing process i.e. Applying rule to the input data.
5. an environment controlled by the AS (Armstrong “Paragraph [0020] With the data semanticizer 100, the procedure of annotating data with semantics can be completed with reduced human interactions.”).
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 1
As to Claim 4
Bajwa, in view of Armstrong teaches the method of claim 1.
Armstrong further teaches “performing one of: deleting the new rule template from the rule database and marking the new rule template as rejected, when the user rejects the new rule template.
(Armstrong [Paragraph 54] At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject [performing one of]: the suggestion of the regular expression as the structure rule of [when the user rejects the new rule template]. the input data 108. [0051] After each record has been processed, the total number of matches for a particular regular expression is Checked. The regular expression is rejected automatically, [marking the new rule template as rejected, AND deleting the new rule template from the rule database] if the number of match count does not fall within the tolerance level (the number of records ± the tolerance value). In this case, the parse returns to operation 2.
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 1.
As to Claim 8
Recall the Claim interpretation mentioned above. The Claims are examined, with regards to 112(f) and 112(b) issues mentioned and as best understood by examiner.
Bajwa teaches “A system for defining and auditing rules in a knowledge-based automation system (AS), comprising:a natural language (NL) interface.” (Bajwa, abs, pg-7 “[Abs] In this paper, we propose an automated approach that automatically translates (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. [defining and auditing rules in a knowledge-based automation system (AS)]. We have used a rule-based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in typical business modelling. [Pg-7]
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[NL interface]
Bajwa teaches “A system for defining and auditing rules in a knowledge-based automation system (AS), comprising: a natural language (NL) interface “(Bajwa, “[Abs] In this paper, we propose an automated approach that automatically translates (such as English) specification [natural language (NL) interface] of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. [defining and auditing rules in a knowledge-based automation system (AS)]. We have used a rule-based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in typical business modelling.
Bajwa teaches “NL processor for generating a semantic representation (SR) of a textual NL representation of a user command” (Bajwa, Abs, pg-3, pg-4, “[Abs], In this paper, we present a novel approach of translating [generating] natural languages specification [textual NL representation of a user command] to SBVR business rules [a semantic representation (SR)].We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules.(pg.- 3)In this paper, we present a novel approach NL2SBVR to translate natural language (such as English) specification of business rules to SBVR rules. SBVR rules are generated from the output of the NLP module. [pg-4] Our tool Rule Generator supports both SBVR notations. [pg-3]. Our approach NL2SBVR is implemented in a tool ‘Rule Generator’ as a proof of concept [PG-6] To find the bugs in the working of the tool experiments were performed to carry out the dynamic verification of the software tool. [comprising: an NL processor]”)
2. a semantic data extractor for generating an SR of the new rule template based on a rule-based graph model of the AS; (Bajwa, “[pg-3] Business vocabulary defines a particular business domain. In SBVR 1.0, business vocabulary can have two major types of elements [3]: Concepts and Fact Types. A concept is a key term that represents a business entity in a particular domain. […]. In our research we use a UML class model as a business domain. Hence, the business vocabulary comes from the target UML class model. With respect to UML class model: [based on a rule-based graph model of the AS] class names and their attributes names are represented as noun concepts, object names are represented as individual concepts, operation names are named as action verbs, and the associations and generalizations are represented as fact types.[ a semantic data extractor generating an SR of the new rule template]
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In example of figure 2, person, vehicle, car are noun concepts. Similarly, the getName, getAge, getSalary are verb concepts and association ownership is fact type in SBVR. In SBVR, the business rules represent particular business logic in a specific context. Each SBVR business rule is based on at least one fact type. […] Our tool Rule Generator supports both SBVR notations.”).
Bajwa does not explicitly teach
a semantic data mapper for creating a new rule template for the AS based on the SR of the user command when the user command includes a new rule definition request.
storing the new rule template in a rule database of the AS in an inactive state
a semantic data comparator for automatically comparing the SR of the new rule template with the SR of the user command,
To automatically determine a deviation of the new rule template from the user command during run-time
Armstrong teaches a semantic data mapper for creating a new rule template for the AS based on the SR of the user command when the user command includes a new rule definition request; (Armstrong, “Pg-5, paragraph [0014] The domain expert or the system administrator can be human, computer implemented, or [the user command] any combination thereof. Operation 102 [includes a new rule definition request;] generates a semanticization rule set 110. [a semantic data mapper for creating a new rule template for the AS] Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110.”).
Examiner notes: Creating a new rule template For the AS of the user command is interpreted as generating semanticiation rule 110 and definition request is interpreted as processing operation in 102. The system generates rule template as a result of rule creation from the user command.
2. storing the new rule template in a rule database of the AS in an inactive state; (Armstrong “Paragraph [0014] Operation 102 generates a semanticization rule set 110. Once, at operation 102, [in an inactive state] the rule set 110 is available, at operation 104, [storing the new rule template] semantic instance(s) 118 can be generated based upon the rule set 110. [in a rule database of] A "semantic instance" 118 is a set of description(s) on an individual item based on a concept(s). An item(s) can be any part of input data 108. generating an SR of the new rule template based on a rule-based graph model of the AS;”).
Examiner notes: Inactive state is interpreted as operation 102 where rule is generated but not yet applied and rules are stored between the operation 102- and operation 104.
3. a semantic data comparator for automatically comparing the SR of the new rule template with the SR of the user command, (Armstrong, paragraph 0050,0067 “[0050] The parser then applies the regular expression to each record in the sample data 114, recording the start and end positions of any matches it finds. [a semantic data comparator for automatically comparing the SR of the new rule template] [0067] selecting, at least one ontology 116, which typically in the present invention is selected by a user; at operation 254, selecting one (or more as the case may be) input data from among the input data 108; at operation 256, selecting an ontology concept from the selected ontology 116, which typically in the present invention is selected by the user; at operation 258, mapping the selected ontology concept to the one (or more) input data selected, which typically in the present invention incorporates the user's assistance/interaction; at operation 260, generating a mapping or data structure capture rule based upon the mapping of the selected ontology concept to the one (or more) input data, [with the SR of the user command] which is performed by the semanticization rule editor 106; “).
4. To automatically determine a deviation of the new rule template from the user command during run-time;(Armstrong “paragraph [0051] After each record has been processed, the total number of matches for a particular regular expression is Checked. The regular expression is rejected automatically, if the number of match count does not fall within the tolerance level (the number of records ± the tolerance value). [automatically determine a deviation of the new rule template from the user command during run-time;] In this case, the parse returns to operation 2.”).
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5. and an NL generator for generating NL feedback including a new rule report that includes the new rule template, and the determined deviation, (Armstrong “paragraph, 0054 0072, 0073, 0074, 0075, “[0054] At operation 156, the user can either accept or reject the suggestion of the regular expression as the structure rule of the input data 108. [and an NL generator for generating NL feedback] [0072] In FIG. 6, for each selected ontology class and all of its properties mapped to a data point 108, as shown in the ontology viewer 200 and the rule viewer 204 (i.e., indicated, via a mapping by selecting "Add a Rule" 300, by the same "K" value, which in this example is highlighted orange for COMMENT […]a semantic instance can be created, via "Generate an Instance" selection 304, using the following procedure. [0073] Upon acceptance of suggestions and successful completion of an error checking mechanism, a semantic instance can be created, via "Generate an Instance" selection 304, using the following procedure. For each row of the same color "K," create an instance of the class with property values using "column" information stored. [a new rule report that includes the new rule template]. [0075] 2. Run Error Checking Mechanisms: This data validation process contains a set of tests to check for errors from the data files, e.g. the correct data files are being properly semanticized; [and the determined deviation].”).
6. [wherein the NL processor] receives user NL's response a user's NL response to the NL feedback, (Armstrong, “paragraph [0054] at operation 156, the user can either accept or reject [receives user NL's response a] the suggestion [user's NL response to the NL feedback of the regular expression as the structure rule of the input data 108”).
Examiner notes: wherein NL processor is disclosed by Bajwa [pg-6]
7. and the semantic data mapper updates the state of the new rule template during run-time, in the rule database based on the SR of the user response. (Armstrong, Paragraph 0067, 0071 “[0067] at operation 268, semanticizing the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108, based upon an acceptable mapping suggestion, which typically in the present invention a mapping rule is accepted, if the user accepts a mapping suggestion by the semanticizer rule editor 106 that maps the selected ontology concept to the sample input data 114.[0071] Although the description herein with reference to FIGS. 6-7 is directed to instance generation for all data points from open data files 108 [in the rule database] in the data pane 202 (three data points >gi ... are displayed in the data pane 202 of FIG. 6), a user may choose to create semantic instances of a few selected [based on the SR of the user response] data points from open data files 108. This is an important capability since the data semanticizer 100 can generate updated semantic instances 118 as needed on demand. [and the semantic data mapper updates a state of the new rule template during run-time,] For example, a single record from a database 108 can be annotated and used instead of generating a large set of semantic instances from all the records in the database 108.”).
Examiner notes: The Combination of Bajwa, Armstrong. teaches the method of claim 1. Furthermore, Bajwa discloses the use of rule generators and software tools. (Since, the software tool needs a processor/ /computer to run/execute , It is necessarily understood by one in the ordinary skill in the art that the processor within the device/computer performs the method of claim 1.
Armstrong and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Armstrong it would have been obvious for a person of ordinary skill in the art to apply the teachings of Armstrong to Bajwa before the effective filing date of the claimed invention to generate a large set of semantic rule set, reducing error and increasing efficiency while applying rules to the data. (Armstrong, “paragraph [0026] Priority is used to increase efficiency while reducing errors, when, at operation112, applying a plurality of generated mapping rules 110 of the input data 108. Priority can be used to determine erroneous application of a rule set 110. When high priority rules cannot be applied, semantic instance creation process stops, whereas low priority rules can be safely ignored.”) Additionally, one would be motivated to do so to speed up mapping, and quickly and effectively incorporate user assistance to turn tedious task of data structuring into a collaborative, efficient process. (paragraph [0019] However, the data semanticizer 100 can efficiently (e.g., simply, quickly, and highly effectively) incorporate assistance from a user, which will make the process of capturing
the structure of data 108 easier. However, the data semanticizer 100 substantially reduces expert human assistance and dependency in semanticizing a large volume of data 108 in any format and in any domain.)
As to Claim 9
Bajwa, in view of Armstrong teaches the System of claim 8.
Bajwa further teaches “wherein the semantic mapper is configured to map the SR of the user command with the rule-based graph model of the AS to create the new rule template.” (Bajwa, “[pg-3], This algorithm is used in our approach to parse English text and extract SBVR syntactic elements. [wherein the semantic mapper is configured to map the SR of the user command] Our approach extracts SBVR vocabulary from a class model. Afterwards, the SBVR elements are UML [1] mapped to SBVR vocabulary to ensure that the output SBVR rules will be targeting a particular business domain. Finally, the SBVR rule is [to create the new rule template]. generated from UML [rule-based graph model] mapped [mapping] SBVR elements.”).
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 8
As to Claim 10
Bajwa, in view of Armstrong teaches the system of claim 8.
Armstrong further teaches “wherein the semantic data mapper is configured to generate an SR of the user’s NL response to the NL feedback during run-time” (Armstrong, paragraph [0069] the mapping of the selected ontology concept to the one input data, or any combination thereof, which typically in the present invention the mapping rule modification or optimization incorporates the user's assistance/interaction; and, at operation 266, [during run-time] if a mapping rule suggestion is accepted, at operation 268, semanticizing the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108, based upon an acceptable mapping suggestion [to generate an SR of the user’s NL response to the NL feedback during run-time], which typically in the present invention a mapping rule is accepted, if the user accepts a mapping suggestion by the semanticizer rule editor 106 that maps the selected ontology concept to the sample input data 114
2. and automatically updating the inactive state of the new rule template in the rule database to an active state during run-time, , (Armstrong, paragraph 0015, 0067 [0015], Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. [the new rule template in the rule database] [0067] at operation 266 [the inactive state] if a mapping rule suggestion is accepted, at operation 268, [an active state during run-time], (semanticizing [and automatically updating] the input data 108 by applying or populating the generated optimized mapping rule to entire input data 108.
Examiner Notes: Inactive state is interpreted as operation 266 waiting user confirmation and active state is interpreted as operation 268 i.e. rule executing state. Automatically updating from inactive to active state is interpreted as state change upon approval of the new rule template i.e mapping suggestion.
3. when the user approves the new rule template, user. (Armstrong, “Paragraph [54], At operation 156, the user examines the suggestion. At operation 156, the user can either accept or [when the user approves] rejects the suggestion of the regular expression as the structure rule [the new rule template], of the input data 108.”).
4. wherein in the active state, the new rule template is executable to perform an automated action (Armstrong, “Paragraph [14] Operation 102 generates a semanticization rule set 110. Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. A "semantic instance" 118 is a set of description(s) on an individual item based on a concept(s).Paragraph [67] at operation 266, if a mapping rule suggestion is accepted, if a mapping rule suggestion is accepted, at operation 268, [wherein in the active state] semanticizing the input data [an automated action] 108 by applying or populating the generated optimized mapping rule to entire input data 108,[ the new rule template is executable to perform] based upon an acceptable mapping suggestion, which typically in the present invention a mapping rule is accepted. ”).
Examiner notes: Under BRI, Active state is interpreted as operation 268, as a rule executing state, and automated action is interpreted as semanticizing process i.e Applying rule to the input data.
5. an environment controlled by the AS
(Armstrong “Paragraph [20] With the data semanticizer 100, the procedure of annotating data with semantics can be completed with reduced human interactions.”).
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 8
As to Claim 11
Bajwa, in view of Armstrong teaches the System of claim 8.
Armstrong further teaches “wherein semantic data mapper is configured to perform one of: deleting the new rule template from the rule database and marking the new rule template as rejected, when the user rejects the new rule template.
(Armstrong [Paragraph 54] At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject [wherein semantic data mapper is configured perform one of]: the suggestion of the regular expression as the structure rule of [when the user rejects the new rule template]. the input data 108. [0051] After each record has been processed, the total number of matches for a particular regular expression is Checked. The regular expression is rejected automatically, [marking the new rule template as rejected, AND deleting the new rule template from the rule database] if the number of match count does not fall within the tolerance level (the number of records ± the tolerance value). In this case, the parse returns to operation 2.
Bajwa and Armstrong are combinable for the same rationale as set forth above with respect to Claim 8
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable” (“Bajwa”) in view (“Armstrong”) and in view of Omoigui et.al., Pre- Grant Publication No. US 20100070448 A1 (“Omoigui”)
As to Claim 5
Bajwa, in view of Armstrong teaches the method of claim 1.
Armstrong teaches “(digitally signing) the new rule template (with a unique identifier) of the user and associating the user details with the SR of the new rule template, when the user approves the new rule template. (Armstrong, paragraph 0087, 0088, [0087] The data semanticizer 100 assists users in generating rule sets 110 to be applied to a large data set 108 consisting of similar pattern files and automates the process of anotating the data 108 with rule sets 110. [New rule template This approach minimizes the human effort and dependency involved in annotating data with semantics. [0088] Additionally, the automated data annotation process of the data semanticizer 100 allows rapid development of semantic data 118.[and associating the user details with the SR of the new rule template]Test results show that two files, each containing 550 Fast-A formatted protein sequences can be annotated using the BIO PAX-LEVEL 1 ontology 116without error in approximately 20 seconds once the user has accepted the suggestions., [when the user approves the new rule template.]
Bajwa in view of Armstrong does not teach:
digitally signing with a unique identifier of the user
Omoigui teaches digitally signing with a unique identifier of the user (Omoigui, paragraph “[0401] Term coined by members of the Semantic Web research community that refers to a chain of authorization that users of the Semantic Web can use to validate assertions and statements. Based on work in mathematics and cryptography, digital signatures provide proof that a certain person wrote (or agrees with) a document or statement. Users can preferably digitally sign [digitally signing] all of their RDF statements. That way, users can be sure that they wrote them (or at least vouch for their authenticity). Users simply tell the program whose signatures to trust. Each can set their own levels of trust (or paranoia), and the computer can decide how much of what it reads to believe. [With a unique identifier of the user] “).
Omoigui and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Omoigui it would have been obvious for a person of ordinary skill in the art to apply the teachings of Omoigui to Bajwa before the effective filing date of the claimed invention to ensure enhance privacy, security and support secure and automated workflows in a regulated environment. (Omoigui “Paragraph [0360], In describing collections of pages that represent a single logical "document", for describing intellectual property rights of Web pages, and for expressing the privacy preferences of a user as well as the privacy policies of a Web site. RDF with digital signatures is preferably a component of building the "Web of Trust" for electronic commerce, collaboration, and other applications.”).
As to Claim 12
Bajwa, in view of Armstrong teaches the system of claim 8.
Armstrong further teaches “semantic data mapper is configured to (digitally signing) the new rule template (with a unique identifier) of the user and associating the user details with the SR of the new rule template, when the user approves the new rule template. (Armstrong, paragraph 0087, 0088, [0087] The data semanticizer 100 assists users in generating rule sets 110 to be applied to a large data set 108 consisting of similar pattern files and automates the process of anotating the data 108 with rule sets 110. [semantic data mapper is configured to the new rule template This approach minimizes the human effort and dependency involved in annotating data with semantics. [0088] Additionally, the automated data annotation process of the data semanticizer 100 allows rapid development of semantic data 118.[and associating the user details with the SR of the new rule template]Test results show that two files, each containing 550 Fast-A formatted protein sequences can be annotated using the BIO PAX-LEVEL 1 ontology 116without error in approximately 20 seconds once the user has accepted the suggestions., [when the user approves the new rule template.]
Bajwa in view of Armstrong does not teach:
digitally signing with a unique identifier of the user
Omoigui teaches digitally signing with a unique identifier of the user (Omoigui, paragraph “ [0401] Term coined by members of the Semantic Web research community that refers to a chain of authorization that users of the Semantic Web can use to validate assertions and statements. Based on work in mathematics and cryptography, digital signatures provide proof that a certain person wrote (or agrees with) a document or statement. Users can preferably digitally sign [digitally signing] all of their RDF statements. That way, users can be sure that they wrote them (or at least vouch for their authenticity). Users simply tell the program whose signatures to trust. Each can set their own levels of trust (or paranoia), and the computer can decide how much of what it reads to believe. [With a unique identifier of the user] “).
Omoigui and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Omoigui it would have been obvious for a person of ordinary skill in the art to apply the teachings of Omoigui to Bajwa before the effective filing date of the claimed invention to ensure enhance privacy, security and support secure and automated workflows in a regulated environment. (Omoigui “Paragraph [0360], in describing collections of pages that represent a single logical "document", for describing intellectual property rights of Web pages, and for expressing the privacy preferences of a user as well as the privacy policies of a Web site. RDF with digital signatures is preferably a component of building the "Web of Trust" for electronic commerce, collaboration, and other applications.”).
Claims 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable” (“Bajwa”) in view (“Armstrong”) and in view of Manaf et.al., “SBVR2Alloy: an SBVR to Alloy compiler” (“Manaf “).
As to Claim 6
Bajwa, in view of Armstrong teaches the method of claim 1.
Bajwa Further teaches “mapping an SR of the user command with one or more rule executions stored in a rule execution database of the AS” (Bajwa “[pg-6] To generate SBVR rules, the first step is to create a fact type. A fact type is created by mapping the noun concepts and verb concepts to the fact types available in the SBVR vocabulary array list. Atomic formulization is used to map the input text to a suitable target fact type [mapping an SR of the user command with one or more rule executions] in SBVR vocabulary. [stored in a rule execution database of the AS]. The mapped fact type is used to generate a SBVR rule by applying a set of logical formulations. As For the different types of syntactic structures used in English language, respective types of logical formulations have been defined. Following are the details that how we have incorporated these logical formulations to map English language text into SBVR rule.
Armstrong teaches” wherein recorded execution outcome of each rule matches the observed characteristic.” (Armstrong, paragraph [0037]-[0050]
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Under the BRI, recorded execution outcome is interpreted as a list of records containing the data which the user desires to parse.[0044] and each rule matching the observed characteristic is interpreted as line [0049]. based upon a set of templates, which matches the example substring.”).
2. retrieving one or more rule template corresponding to one or more rule executions from the rule database; (Armstrong, paragraph “[0061] The semanticization rule editor 106 takes samples 114 from a collection of data 108 and its corresponding ontology 116 [retrieving one or more rule template corresponding to as input and assists users in defining the semanticization rule set 110[one or more rule executions from the rule database] per data collection 108. Typically, in the present invention, rule set 110 is generated with assistance from a domain expert who is familiar with data collection. “).
3. and retrieving one or more users associated with each retrieved rule template, and an execution history of each rule template from the [rule execution database.] (Armstrong, paragraph “[0052] Otherwise, the list of matches made by the parse is presented to the user for examination, as suggestions. If the user accepts these suggestions, then the parsing is complete. Otherwise, the regular expression (pattern) is rejected, and the parser returns to operation 2. [and an execution history] The process continues until the user accepts the parser's matches or the parser runs out regular expressions. Therefore, the output of the pattern generator/parser 106 is a list of suggested matches. [0053] FIG. 2 is a flow chart of semanticizing email text as input electronic data, according to an embodiment of the present invention. More particularly, an example of semanticization by the semanticizer 100 according to the above process operations 1 through 5, using emails (email messages/text), [retrieving one or more users associated with each retrieved rule template, and an execution] as input data 108, and using the above-described regular expressions for the "R" data structure capture element to determine a region of the "W" data structure capture element, which is a mapping to the "C" data structure capture element, in a sample 114 of the input data 108, is shown with reference to FIG 2.
Examiner notes: Armstrong para [0061] teaches rule execution database.
Bajwa in view of Armstrong does not teach:
when the user command includes an audit query for the AS in response to an observed characteristic of the environment
Manaf teaches “when the user command includes an audit query for the AS in response to an observed characteristic of the environment,” (Manaf “[pg-79] SBVR is gaining ground in systems specification and modelling due to its declarative nature. The work in [32] translates UML / OCL into SBVR to bridge the gap between developers and business users. [33] translates SBVR into a process BPMN and decision model which is used to validate SBVR rules. In [34] SBVR is used to generate a relational database and transform SBVR business rules to SQL queries that can be executed against the data set. [an audit query for the AS in response to an observed characteristic of the environment] This widens the spectrum in which to consider user interaction, [when the user command includes] moving from process-driven to user-driven development of information systems.”).
Manaf and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Manaf it would have been obvious for a person of ordinary skill in the art to apply the teachings of Manaf to Bajwa before the effective filing date of the claimed invention to allows business analysts and developers to specify requirements in structured English, which is easier to understand. Additionally, one would be motivated to do so to automatically trace and validate the records that led to user command. (Manaf,” [abs] We present a compilation tool SBVR2Alloy which is used to automatically generate as well as validate service choreographies specified in structured natural language”).
As to Claim 7
Bajwa in view of Armstrong and in view of Manaf teaches the method of claim 6.
Armstrong generating an SR of each retrieved rule template, user information and an execution history of each retrieved rule template. (Armstrong)[0053] FIG. 2 is a flow chart of semanticizing email text as input electronic data, [generating an SR] according to an embodiment of the present invention. More particularly, an example of semanticization by the semanticizer 100 according to the above process operations 1 through 5, using emails (email messages/text), as input data 108, and using the above-described regular expressions for the "R" data structure capture element to determine a region of the "W" data structure capture element, which is a [of each retrieved rule template of each retrieved rule template. ] mapping to the "C" data structure capture element, in a sample 114 of the input data 108, is shown with reference to FIG 2. [0054] In FIG. 2, at operation 150, the input file 108 contains a set of email headers [user information,] and "dean@cs.umd.edu" is the example substring - "W" data structure capture element - which is mapped (as shown via a displayed highlight) to a selected ontology concept from the ontology 116 (not shown in FIG. 2, but see FIG. 4) and serves as sample data 114 from the input file 108. At operation 152, the pattern generator (also referred to as the semanticization rule editor 106) attempts to approximate the structure of the given input file 108 based on regular expression templates 160. At operation 154, , [and an execution history] the pattern generator 106 suggests a regular expression 160, to capture the structure of the input file, to the user. At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject the suggestion of the regular expression as the structure rule of the input data 108.
Bajwa further teaches “and generating an NL based audit report to the user based on the generated SR”(Bajwa, [pg-3] The SBVR produces [and generating an] a SBVR rule [on the generated SR] in the form of text string that is further formatted using the SBVR notation i.e. Structured English [an NL based audit report to the user based] described in the section 2.4.”).
Bajwa, Armstrong and Manaf are combinable for the same rationale as set forth above with respect to Claim 6.
As to Claim 13
Bajwa, in view of Armstrong teaches the system of claim 8.
Bajwa Further teaches “wherein the semantic data mapper is configured to map: an SR of the user command with one or more rule executions stored in a rule execution database of the AS” (Bajwa “[pg-6] To generate SBVR rules, the first step is to create a fact type. A fact type is created by mapping the noun concepts and verb concepts to the fact types available in the SBVR vocabulary array list. Atomic formulization is used to map the input text to a suitable target fact type [wherein the semantic data mapper is configured to map: an SR of the user command with one or more rule executions] in SBVR vocabulary. [stored in a rule execution database of the AS]. The mapped fact type is used to generate a SBVR rule by applying a set of logical formulations. As For the different types of syntactic structures used in English language, respective types of logical formulations have been defined. Following are the details that how we have incorporated these logical formulations to map English language text into SBVR rule.
Armstrong teaches” wherein recorded execution outcome of each rule matches the observed characteristic.” (Armstrong, paragraph [0037]-[0050]
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Under the BRI, recorded execution outcome is interpreted as a list of records containing the data which the user desires to parse.[0044] and each rule matching the observed characteristic is interpreted as line [0049]. based upon a set of templates, which matches the example substring.”).
2. retrieve one or more rule template corresponding to one or more rule executions from the rule database; (Armstrong, paragraph “[0061] The semanticization rule editor 106 takes samples 114 from a collection of data 108 and its corresponding ontology 116 [retrieve one or more rule template corresponding to as input and assists users in defining the semanticization rule set 110[one or more rule executions from the rule database] per data collection 108. Typically, in the present invention, rule set 110 is generated with assistance from a domain expert who is familiar with data collection. “).
3. and retrieve one or more users associated with each retrieved rule template, and an execution history of each rule template from the [rule execution database.] (Armstrong, paragraph “[0052] Otherwise, the list of matches made by the parse is presented to the user for examination, as suggestions. If the user accepts these suggestions, then the parsing is complete. Otherwise, the regular expression (pattern) is rejected, and the parser returns to operation 2. [and an execution history] The process continues until the user accepts the parser's matches or the parser runs out regular expressions. Therefore, the output of the pattern generator/parser 106 is a list of suggested matches. [0053] FIG. 2 is a flow chart of semanticizing email text as input electronic data, according to an embodiment of the present invention. More particularly, an example of semanticization by the semanticizer 100 according to the above process operations 1 through 5, using emails (email messages/text), [retrieve one or more users associated with each retrieved rule template,] as input data 108, and using the above-described regular expressions for the "R" data structure capture element to determine a region of the "W" data structure capture element, which is a mapping to the "C" data structure capture element, in a sample 114 of the input data 108, is shown with reference to FIG 2.
Examiner notes: Armstrong para [0061] teaches rule execution database.
Bajwa in view of Armstrong does not teach:
when the user command includes an audit query for the AS in response to an observed characteristic of the environment
Manaf teaches “when the user command includes an audit query for the AS in response to an observed characteristic of the environment,” (Manaf “[pg-79] SBVR is gaining ground in systems specification and modelling due to its declarative nature. The work in [32] translates UML / OCL into SBVR to bridge the gap between developers and business users. [33] translates SBVR into a process BPMN and decision model which is used to validate SBVR rules. In [34] SBVR is used to generate a relational database and transform SBVR business rules to SQL queries that can be executed against the data set. [an audit query for the AS in response to an observed characteristic of the environment] This widens the spectrum in which to consider user interaction, [when the user command includes] moving from process-driven to user-driven development of information systems.”).
Manaf and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Manaf it would have been obvious for a person of ordinary skill in the art to apply the teachings of Manaf to Bajwa before the effective filing date of the claimed invention to allows business analysts and developers to specify requirements in structured English, which is easier to understand. Additionally, one would be motivated to do so to automatically trace and validate the records that led to user command. (Manaf,” [abs] We present a compilation tool SBVR2Alloy which is used to automatically generate as well as validate service choreographies specified in structured natural language”).
As to Claim 14
Bajwa in view of Armstrong and in view of Manaf teaches the method of claim 13.
Armstrong generating an SR of each retrieved rule template, user information and an execution history of each retrieved rule template. (Armstrong)[0053] FIG. 2 is a flow chart of semanticizing email text as input electronic data, [generating an SR] according to an embodiment of the present invention. More particularly, an example of semanticization by the semanticizer 100 according to the above process operations 1 through 5, using emails (email messages/text), as input data 108, and using the above-described regular expressions for the "R" data structure capture element to determine a region of the "W" data structure capture element, which is a [of each retrieved rule template of each retrieved rule template. ] mapping to the "C" data structure capture element, in a sample 114 of the input data 108, is shown with reference to FIG 2. [0054] In FIG. 2, at operation 150, the input file 108 contains a set of email headers [user information,] and "dean@cs.umd.edu" is the example substring - "W" data structure capture element - which is mapped (as shown via a displayed highlight) to a selected ontology concept from the ontology 116 (not shown in FIG. 2, but see FIG. 4) and serves as sample data 114 from the input file 108. At operation 152, the pattern generator (also referred to as the semanticization rule editor 106) attempts to approximate the structure of the given input file 108 based on regular expression templates 160. At operation 154, , [and an execution history] the pattern generator 106 suggests a regular expression 160, to capture the structure of the input file, to the user. At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject the suggestion of the regular expression as the structure rule of the input data 108.
Bajwa further teaches “and generating an NL based audit report to the user based on the generated SR”(Bajwa, [pg-3] The SBVR produces [and generating an] a SBVR rule [on the generated SR] in the form of text string that is further formatted using the SBVR notation i.e. Structured English [an NL based audit report to the user based] described in the section 2.4.”).
Bajwa, Armstrong and Manaf are combinable for the same rationale as set forth above with respect to Claim 13.
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable” (“Bajwa”) in view (“Armstrong”) and in view of Warner et. al., Pre- Grant Publication No. US 20150286969 A1(“Warner”).
As to Claim 15
Bajwa, in view of Armstrong teaches the system of claim 8.
Armstrong further teaches “an API communicatively coupled to the NLI” (Armstrong, paragraph 0010, 0085, “[0010] FIG. 8 is a diagram of a computing device network and a data semanticizer of the present invention used by a task computing environment to implement task computing on the computing device network. [NLI] [0085] FIG. 8, the task computing environment 500 architecture, [communicatively coupled] for example, comprises a presentation layer 506, a web service application programming interface (API) 508,[ an API] a middleware layer 510, a service layer 512, and a realization layer 514. The data semanticizer 100 provides resource and service abstractions (realization layer 514) based upon input data 108 in any format and in any domain, using generated semantic instances 118, and creates a task computing environment 500 based upon the resource and service abstractions 514 of the input data 108.
2. a rule database configured to store one or more rule templates registered with the AS; (Armstrong paragraph [0014] Operation 102 generates a semanticization rule set 110. [a rule database configured to store] Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. A "semantic instance" 118 is a set of description(s) on an individual item based on a concept(s). An item(s) can be any part of input data 108. [0021]In FIG. 1, as an example of operation 106, to generate a mapping rule 110 to map a concept to input data by capturing a structure of input data, comprises defining an atomic rule comprising, for example, a set of 6-tuples <C, W,R, K, P, 0> as annotation or data structure [one or more rule templates] capture elements
Examiner notes: Under BRI a rule database is interpreted as semanticization rule set configured to store the rule template interpreted as structured set of rule. Rule set 110 contains many rules which are generated and registered with AS to use.
3. a rule engine configured to create one or more rule instances from the one or more rule templates and execute the one or more rule instances in relation to a set of defined resources; (Armstrong, Paragraph 0062, 0063, [0062] Semanticizer engine 112: The semanticizer engine 112 [a rule engine] is a programmed computer processor that typically in the present invention runs in the background, which takes a large collection of data 108 and a semanticization rule set 110 [one or more rule templates] to be applied to [and execute] this data collection 108 and produces semantic instances 118 [to create one or more rule instances in relation to a set of defined resources ] corresponding to the data collection 108.[0063] Several additional components developed by FUJITSU LIMITED, Kawasaki, Japan, assignee of the present application, or others can be added to the ontology viewer tools 200 and the data viewer 202 environments. These include ontology mapping tools, inference engines, and data visualization tools. Ontology mapping tools.
4. and a rule execution database configured to store historical data pertaining to the one or more rule instance executions. (Armstrong, “paragraph [0073] Upon acceptance of suggestions and a successful completion of an error checking mechanism, a semantic instance can be created, via "Generate an Instance" selection 304, using the following procedure: [0074] 1. For each row of the same color "K," create an instance of the class with property values using "column" information stored. [to store historical data pertaining to the one or more rule instance executions]”).
Bajwa further teaches a resource definition database configured to store one or more definitions of one or more resources associated with the environment; (Bajwa, pg. –2, pg-3, “[pg-2], “A SBVR rule is the key constituent of SBVR standard. A SBVR rule can easily be machine processed to...generate formal representations such databases [a resource definition database configured to store] ...[pg-3] The SBVR rules can be of two types [3]: definitional rules and behavioral rules: Definitional Rules or structural rules are used to define an organization’s setup. [one or more definitions of one or more resources associated with the environment]").
Bajwa in view of Armstrong does not teach:
a sensor and actuator catalog configured to store metadata information about one or more sensors and actuators, wherein the one or more sensors and actuators represent one or more abstractions in the rule model of the AS
Warner teaches “a sensor and actuator catalog configured to store metadata information about one or more sensors and actuators, wherein the one or more sensors and actuators represent one or more abstractions in the rule model of the AS;” (Warner abs, paragraph 0047, 0203, “[abs] In a semantic database, a performance context [actuator catalog] that receives and stores sensor data [configured to store metadata information about one or more sensors and actuators]output monitoring managed assets persisted in a time-series database, a workflow context that determines the workflow process necessary to manage the managed asset based on the governance policies and sensor data, implemented using a workflow engine that supports a declarative workflow language, a decision context that contains business rules encoded in a declarative grammar, implemented using a business rule engine and in which the business rules define conditions under which an asset is corrected. [0047] System 100 receives and processes massive amounts of data, the outputs from sensors 102, using model 106. Model 106 includes and applies a plurality of ontologies 108, [represent one or more abstractions in the rule model of the AS] organizational mission data 110, protected assets and processes data 112 and risk management data 114 to the data received from sensors 102. , [wherein the one or more sensors] [0203] Each adjudication context is an RDF Individual that encapsulates mappings between a managed asset 1018 and the managed asset's 1018 associated metrics (read from sensors 1014 and stored in performance context 1004), thresholds (contained in ontologies in policy context 1002), rules (business rules contained in decision context 1008), actions (taken by actuators 1016[actuators] in response to decision context 1008 decision and owners (of managed assets 1018).”).
Warner and Bajwa are related to the same field of endeavor (Semantic Representation). In view of the teachings of Warner it would have been obvious for a person of ordinary skill in the art to apply the teachings of Warner to Bajwa before the effective filing date of the claimed invention to integrate and store data from multiple sources while maintaining scalability and transparency. (Warner “paragraph [0041] Accept inputs from sensors, applications, data streams, etc. (Observe); [0042] Perform associations with numerous data sources and apply numerous ontologies to the observed data (Orient). These associations create a continuously updated context of the managed environment based on harvested data (e.g., metadata) from the observations as assessed against the ontologies of structured policies and the relationships within and between them;”).
Prior Art of Record:
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Raimo et. al. (US 20200042642 A1) is directed to extract domain knowledge and then build a multiple searchable knowledge source. At runtime, the chatbot queries about those sources and ranks or selects among their answers with a semantic matcher. This lets the system answer questions using the best source for the user’s intent.
Ehsani et. al. (US 20140108019 A1) is directed to mapping a user spoken/sensor input into a semantic representation. Additionally, it also discloses a rule database to retrieve templates and keeping track of historical execution along with generating output responses to the user based on rule.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIROJ KOIRALA whose telephone number is (571)270-0748. The examiner can normally be reached Monday -Friday 8am-5pm.
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/N.K./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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