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 .
In response to the application filed on June 16, 2025, claims 1-20 and 22 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 06/16/2025. In this action Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wenzel et al. (US Pub. No. 20170286456) and Srinvasulu et al. (US Pub. No. 20210049138) in further view of Ford et al. (US Pub. No. 20170041296). The Srinvasulu et al. reference has been added to include inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets, wherein the machine learning model identifies that the one or more assets upon which to validate comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema, wherein the first command portion is generated by the machine learning model based on the first set of attributes being defined using the first schema and the second command portion is generated by the machine learning model based on the second set of attributes being defined using the second schema.
Applicant’s arguments:
In regards to claim 1 on Page(s) 14, applicant argues “more steps that can be performed in the human mind. Specifically, the Memo clarified that "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)" (Memo, pg. 2). Independent claim 2, as well as the other independent claims, have been amended to recite steps that cannot practically be performed in the human mind.”
Examiner’s Reply:
Generating rules and validating metadata are steps that can be performed in the human mind. The abstract idea recited in the claims is generally linking it to a computer environment.
Applicant’s arguments:
In regards to claim 1 on Page(s) 14, applicant argues “standardized transformation processes" (Specification, 1 4). The Specification goes on to explain how the technical solutions described by the claims solve these technical problems. For example, the claims describe using dynamically generated validation rules including "a first validation rule portion that is generated using a standardized validation process (e.g., corresponding to a standardized schema) and a second validation rule portion that is generated using a validation process selected based on a non-standardized schema that is specific to a respective asset type of the plurality of respective asset types" (Specification, 1 5). The Specification further details how solving these technical problems using these technical solutions can allow "assets in the exchange that may have both standardized and custom schemas" to be validated (Specification, 1 5).”
Examiner’s Reply:
Examiner respectfully disagrees. The alleged improvement in the claims is directed to improving the abstract idea (a better way of validating metadata for searching). The claims do not make a computer itself function better.
Applicant’s arguments:
In regards to claim 1 on Page(s) 14, applicant argues “Moreover, as previously mentioned, the claims are patent eligible at Step 2B because, contrary to MPEP 2106.05.II, which recites that "in Step 2B, examiners should [r]e-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant,"
Examiner’s Reply:
Receiving and displaying data pertaining to a data exchange are conventional computer functions that merely link the abstract idea to a generic computing environment. These I/O steps do not integrate an abstract idea into a practical application. See MPEP 2106.05(g) Insignificant Extra-Solution Activity.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 and 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims 1, 2 and 15 contain 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. There is no support for “storing, within a data exchange, a plurality of assets of a plurality of asset types, and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types for a corresponding asset ….”.
Dependent claims 3-14 and 16-20 and 22 is/are also rejected for inheriting the deficiencies of the independent claims from which they depend on.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1-20 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 2-14 and 22), a medium (claims 15-20), and a system (claims 1) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 2, and 15 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1, 2, and 15 recites the following limitations directed towards a Mental Processes:
querying, based on a request , the data exchange ((The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to query a data exchange)
to identify one or more assets of the plurality of assets stored in the data exchange (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying assets)
in order to validate the metadata associated with each asset of the one or more assets (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by validate metadata);
The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a string)
to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by validate metadata)
to apply to the one or more assets to validate the metadata associated with the one or more assets (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by validate metadata),
wherein the The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying an asset)
upon which to validate comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by validate an attribute),
wherein the first command portion is generated by the The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a command);
generating based on the first data validation operation and the second data validation operation being executed, validation results for the one or more assets indicating that the metadata associated with each asset of the one or more assets has been validated or has failed to be validated (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generate validation results).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 2, and 15:
one or more processors (i.e., as a generic processor/component performing a generic computer function); and
one or more non-transitory, computer-readable media (i.e., as a generic processor/component performing a generic computer function) comprising
instructions that when executed by the one or more processors causes operations comprising:
storing, within a data exchange, a plurality of assets of a plurality of asset types, and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types for a corresponding asset (recites insignificant extra solution activity that amounts to storing asset data);
inputting the request and the metadata associated with the one or more assets into a machine learning model to
executing a first data validation operation comprising the one or more first validation rules to the first set of attributes of the one or more assets based on the first command portion and executing a second data validation operation comprising the one or more second validation rules to the second set of attributes of the one or more assets (recites insignificant extra solution activity that amounts to executing an operation);
causing a user interface comprising the validation results to be displayed, wherein the validation results comprise a customized search experience including the validation results for the one or more assets (recites insignificant extra solution activity that amounts to displaying results data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 2, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 3 and 16:
Step 2A, prong one of the 2019 PEG:
for each of the one or more assets:
selecting an algorithm based on the second schema specific to the first asset type and the second validation operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by selecting an algorithm); and
processing the second set of attributes for each of the plurality of asset types of the one or more assets using the algorithm (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by processing attributes).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4 and 17:
Step 2A, prong one of the 2019 PEG:
determining a first data type for the second schema specific to the first asset type (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a data type);
formatting the second command portion based on the first data type to generate a first formatted command portion (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a formatted command);
comparing the first formatted command portion to a first value of a first attribute of the second set of attributes for the first asset type (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing attributes);
determining a second data type for the second schema specific to a second asset type of the plurality of asset types (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a data type);
formatting the second command portion based on the second data type to generate a second formatted command portion (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a formatted command); and
comparing the second formatted command portion to a first value of a second attribute of the second set of attributes for the second asset type (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing attributes).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5 and 18:
Step 2A, prong one of the 2019 PEG:
determining a respective asset type identifier for each of the plurality of asset types (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an identifier);
Step 2A Prong Two Analysis:
inputting the respective asset type identifier into a database listing schemas corresponding to asset type identifiers
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6 and 19:
Step 2A, prong one of the 2019 PEG:
determining a modification to the second command portion based on the respective set of database objects (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a modification); and
applying the modification to the second command portion prior to executing the second validation operation to respective the second set of attributes of each of the one or more assets (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by applying a modification).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A, prong one of the 2019 PEG:
updating the machine learning model to be updated based on the validation results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating a model).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8:
Step 2A, prong one of the 2019 PEG:
determining an asset terminology identifier based on the second validation operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an identifier);
comparing the asset terminology identifier to a database listing terminology for users performing the second validation operation to determine a modification to the second command portion (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing an identifier); and
applying the modification to the second command portion prior to performing the second validation operation to respective the second set of attributes of each of the one or more assets (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by applying a modification).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A, prong one of the 2019 PEG:
determining a authorization level for user (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an authorization);
comparing the authorization level to a database listing authorization levels corresponding to each of a plurality of operation types (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing an authorization level).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
determining a role for user based on a credential (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a role);
comparing the role to a database listing roles corresponding to each of a plurality of operation types (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing roles).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 11 and 20:
Step 2A, prong one of the 2019 PEG:
determining the first set of attributes for executing the first validation operation based on the command string (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an attribute); and
determining the second set of attributes for executing the second validation operation based on the command string (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an attribute).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 12:
Step 2A, prong one of the 2019 PEG:
determining an attribute set identifier for the first set of attributes based on the command string (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining an attribute);
comparing the attribute set identifier to a database listing attribute sets corresponding to the attribute set identifier to determine the first set of attributes for performing the command string (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing an attribute).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A, prong one of the 2019 PEG:
filtering the plurality of content using a user content subscription setting for a user to obtain filtered content (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by filtering content);
generating the validation results comprising the filtered content (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a result).
Step 2A Prong Two Analysis:
receiving a plurality of content published to an application programming interface (“API”) based on the validation results (recites insignificant extra solution activity that amounts to receiving content data);
causing a user interface to be displayed to the user comprising the validation results (recites insignificant extra solution activity that amounts to displaying a user interface).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 14:
Step 2A, prong one of the 2019 PEG:
determining, based on a batch filtering criterion, a respective relevance ranking for a plurality of content related to the validation results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining relevance);
comparing the respective relevance ranking to a respective threshold relevance ranking to determine that the plurality of content is to be published (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by comparing a ranking);
generating the validation results comprising the plurality of content to be published (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a result).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 22:
Step 2A, prong one of the 2019 PEG:
querying, based on a request, the data exchange to identify one or more assets of the plurality of assets stored in the data exchange to validate the metadata associated with each asset of the one or more assets;
comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema, wherein the first command portion is generated by the machine learning model based on the first set of attributes being defined using the first schema and the second command portion is generated by the machine learning model based on the second set of attributes being defined using the second schema;
executing a first validation operation comprising the one or more first validation rules to the first set of attributes of each of the one or more assets based on the first command portion and executing a second validation operation comprising the one or more second validation rules to the second set of attributes of the one or more assets; and
generating, based on the first validation operation and the second validation operation being executed, validation results for the one or more assets indicating that the metadata associated with a given asset of the one or more assets has been validated or has failed to be validated
(The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by performing a process).
Step 2A Prong Two Analysis:
storing, within a data exchange, a plurality of assets of a plurality of asset types and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types of a corresponding asset (recites insignificant extra solution activity that amounts to storing assets);
inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets, wherein the machine learning model identifies that the one or more assets upon which to validate (recites insignificant extra solution activity that amounts to inputting data);
causing the user interface to be displayed comprises causing, using cloud-based I/O circuitry, the user interface to be displayed to the user (recites insignificant extra solution activity that amounts to esdisplaying data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wenzel et al. (US Pub. No. 20170286456) and Srinvasulu et al. (US Pub. No. 20210049138) in further view of Ford et al. (US Pub. No. 20170041296).
With respect to claim 1, Wenzel et al. discloses a system for executing data validation operations across data exchanges that comprise non-homogenous assets and non-standardized data descriptions, the system comprising:
one or more processors (See Fig. 30); and
one or more non-transitory, computer-readable media (See Fig. 30) comprising instructions that when executed by the one or more processors causes operations comprising:
storing, within a data exchange, a plurality of assets of a plurality of asset types, and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types for a corresponding asset (Paragraph 4 discloses define, find, understand, use, and exchange data by managing standards and metadata as organizational assets within an enterprise repository & Paragraph 5 discloses standards specification and management system automatically and dynamically generates an ontology schema based on a data model that describes one or more standards for collection of data (generally referred to as a “data collection standard) from a set of entities in a uniform fashion);
querying, based on a request, the data exchange to identify one or more assets of the plurality of assets stored in the data exchange in order to validate the metadata associated with each asset of the one or more assets (Paragraph 88 discloses Asset generator 30 may use asset templates 32 to assist in the capture process and/or to validate the asset information. In one embodiment, asset generator 30 or a schema generation module generates a virtual schema by applying asset templates 32 to a base schema for an asset);
executing a first data validation operation comprising the one or more first validation rules to the first set of attributes of the one or more assets based on the first command portion and executing a second data validation operation comprising the one or more second validation rules to the second set of attributes of the one or more assets (Paragraph 67 discloses validation policies (e.g., conditional content based on associated values) and role-based approval processes);
generating based on the first data validation operation and the second data validation operation being executed, validation results for the one or more assets indicating that the metadata associated with each asset of the one or more assets has been validated or has failed to be validated (Paragraph 150 discloses a rule may be an annotation describing the mapping and/or transformation occurring between metadata elements, metadata element sets, or value domains. A rule may provide the means to store a description related to the difference or change between assets and may be used to define the way or the reason changes occur). Wenzel et al. does not disclose inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets.
However, Srinvasulu et al. teaches inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets, wherein the machine learning model identifies that the one or more assets upon which to validate comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema, wherein the first command portion is generated by the machine learning model based on the first set of attributes being defined using the first schema and the second command portion is generated by the machine learning model based on the second set of attributes being defined using the second schema (Paragraph 80 discloses a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data. For example, features from this data can be extracted to generate metadata profiles, which can be used to train the machine learning model. Once trained, the machine learning model can be configured to receive input data (e.g., processed incoming data) and generate data quality improvement predictions).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Srinvasulu et al. with Wenzel et al. This would have facilitated sending, storing, and searching data. See Srinvasulu et al. Paragraph 3-4. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: database exchanges.
Wenzel et al. as modified by Srinvasulu et al. does not disclose causing a user interface comprising the validation results to be displayed, wherein the validation results comprise a customized search experience including the validation results for the one or more assets
However, Ford et al. teaches causing a user interface comprising the validation results to be displayed, wherein the validation results comprise a customized search experience including the validation results for the one or more assets (Paragraph 77 discloses hen the external user accesses the secure exchange system, the secure exchange system may recognize the user and associate the user with a particular one of the companies A and B. Using this recognition, the secure exchange system may present a customized browser interface which makes the secure exchange system look like it is operated by or branded for the selected company).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wenzel et al. and Srinvasulu et al. with Ford et al. This would have facilitated sending, storing, and searching data. See Ford et al. Paragraph 4-7. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: database exchanges.
Claim(s) 2-20 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wenzel et al. (US Pub. No. 20170286456) and Srinvasulu et al. (US Pub. No. 20210049138).
With respect to claim 2, Wenzel et al. discloses a method, comprising:
storing, within a data exchange, a plurality of assets of a plurality of asset types, and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types for a corresponding asset (Paragraph 4 discloses define, find, understand, use, and exchange data by managing standards and metadata as organizational assets within an enterprise repository & Paragraph 5 discloses standards specification and management system automatically and dynamically generates an ontology schema based on a data model that describes one or more standards for collection of data (generally referred to as a “data collection standard) from a set of entities in a uniform fashion);
querying, based on a request, the data exchange to identify one or more assets of the plurality of assets stored in the data exchange in order to validate the metadata associated with each asset of the one or more assets (Paragraph 88 discloses Asset generator 30 may use asset templates 32 to assist in the capture process and/or to validate the asset information. In one embodiment, asset generator 30 or a schema generation module generates a virtual schema by applying asset templates 32 to a base schema for an asset);
executing a first data validation operation comprising the one or more first validation rules to the first set of attributes of the one or more assets based on the first command portion and executing a second data validation operation comprising the one or more second validation rules to the second set of attributes of the one or more assets (Paragraph 67 discloses validation policies (e.g., conditional content based on associated values) and role-based approval processes);
generating based on the first data validation operation and the second data validation operation being executed, validation results for the one or more assets indicating that the metadata associated with each asset of the one or more assets has been validated or has failed to be validated (Paragraph 150 discloses a rule may be an annotation describing the mapping and/or transformation occurring between metadata elements, metadata element sets, or value domains. A rule may provide the means to store a description related to the difference or change between assets and may be used to define the way or the reason changes occur). Wenzel et al. does not disclose inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets.
However, Srinvasulu et al. teaches inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets, wherein the machine learning model identifies that the one or more assets upon which to validate comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema, wherein the first command portion is generated by the machine learning model based on the first set of attributes being defined using the first schema and the second command portion is generated by the machine learning model based on the second set of attributes being defined using the second schema (Paragraph 80 discloses a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data. For example, features from this data can be extracted to generate metadata profiles, which can be used to train the machine learning model. Once trained, the machine learning model can be configured to receive input data (e.g., processed incoming data) and generate data quality improvement predictions).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Srinvasulu et al. with Wenzel et al. This would have facilitated sending, storing, and searching data. See Srinvasulu et al. Paragraph 3-4. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: database exchanges.
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 3, Wenzel et al. discloses the method of claim 2, wherein executing the second validation operation comprises:
for each of the one or more assets:
selecting an algorithm based on the second schema specific to the first asset type and the second validation operation (Paragraph 276 discloses standards specification and management system 18 may use mappings from a metadata attribute type to specification attribute or specification element as input to an algorithm to export the metadata content into a submission standard); and
processing the second set of attributes for each of the plurality of asset types of the one or more assets using the algorithm (Paragraph 276 discloses standards specification and management system 18 may use mappings from a metadata attribute type to specification attribute or specification element as input to an algorithm to export the metadata content into a submission standard).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 4, Wenzel et al. discloses the method of claim 2, wherein executing the second validation operation comprises:
determining a first data type for the second schema specific to the first asset type (Paragraph 70 discloses asset templates 32 may include a plurality of asset templates where each of the asset templates controls the properties and/or metadata that are to be included in a captured asset of a particular asset type);
formatting the second command portion based on the first data type to generate a first formatted command portion (Paragraph 72 discloses A concept asset may correspond to a concept in a concept domain. A table asset may correspond to a table for formatting, storing, tabulating, and/or collecting data);
comparing the first formatted command portion to a first value of a first attribute of the second set of attributes for the first asset type (Paragraph 113 discloses On import of the RDF, asset Library 34 has a base (i.e. static) RDF schema that describes asset templates in a generic context and the RDF instance data is parsed and compared with asset templates in data model 36);
determining a second data type for the second schema specific to a second asset type of the plurality of asset types (Paragraph 70 discloses asset templates 32 may include a plurality of asset templates where each of the asset templates controls the properties and/or metadata that are to be included in a captured asset of a particular asset type);
formatting the second command portion based on the second data type to generate a second formatted command portion (Paragraph 72 discloses A concept asset may correspond to a concept in a concept domain. A table asset may correspond to a table for formatting, storing, tabulating, and/or collecting data); and
comparing the second formatted command portion to a first value of a second attribute of the second set of attributes for the second asset type (Paragraph 113 discloses On import of the RDF, asset Library 34 has a base (i.e. static) RDF schema that describes asset templates in a generic context and the RDF instance data is parsed and compared with asset templates in data model 36).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 5, Wenzel et al. discloses the method of claim 2, wherein performing the second process of the operation executing the second validation operation comprises:
determining a respective asset type identifier for each of the plurality of asset types (Paragraph 240 includes generate the assets based on one or more asset templates that define which properties are to be included in the assets and/or which metadata attributes (e.g., identifiers, classifiers, artifacts, relationships) are to be included in the assets); and
inputting the respective asset type identifier into a database listing schemas corresponding to asset type identifiers to determine a respective set of database objects for each of the plurality of asset types (Paragraph 240 includes generate the assets based on one or more asset templates that define which properties are to be included in the assets and/or which metadata attributes (e.g., identifiers, classifiers, artifacts, relationships) are to be included in the assets).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 5. Regarding claim 6, Wenzel et al. discloses the method of claim 5, further comprising:
determining a modification to the second command portion based on the respective set of database objects (Paragraph 236 discloses Standards specification and management system 18 subjects the proposed modifications to a governance process, and approves or rejects each of the proposed modifications based on the outcome of the governance process (308)); and
applying the modification to the second command portion prior to executing the second validation operation to the second set of attributes of each of the one or more assets (Paragraph 236 discloses Standards specification and management system 18 subjects the proposed modifications to a governance process, and approves or rejects each of the proposed modifications based on the outcome of the governance process (308)).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 7, Wenzel et al. discloses the method of claim 2, wherein inputting the request and the metadata associated with the one or more assets into the machine learning model to generate the first command portion comprises:
updating the machine learning model to be updated based on the validation results (Paragraph 80 discloses a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data. For example, features from this data can be extracted to generate metadata profiles, which can be used to train the machine learning model. Once trained, the machine learning model can be configured to receive input data (e.g., processed incoming data) and generate data quality improvement predictions). The motivation to combine statement previously provided in the rejection of independent claim 2 provided above, combining the Wenzel et al. reference and the Srinvasulu et al. reference is applicable to dependent claim 7.
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 8, Wenzel et al. discloses the method of claim 2, wherein executing the second validation operation comprises:
determining an asset terminology identifier based on the second validation operation (Paragraph 240 includes generate the assets based on one or more asset templates that define which properties are to be included in the assets and/or which metadata attributes (e.g., identifiers, classifiers, artifacts, relationships) are to be included in the assets);
comparing the asset terminology identifier to a database listing terminology for users performing the second validation operation to determine a modification to the second command portion (Paragraph 236 discloses Standards specification and management system 18 subjects the proposed modifications to a governance process, and approves or rejects each of the proposed modifications based on the outcome of the governance process (308)); and
applying the modification to the second command portion prior to performing the second validation operation to the second set of attributes of each of the one or more assets (Paragraph 236 discloses Standards specification and management system 18 subjects the proposed modifications to a governance process, and approves or rejects each of the proposed modifications based on the outcome of the governance process (308)).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 9, Wenzel et al. discloses the method of claim 2, wherein inputting the request and the metadata associated with the one or more assets into the machine learning model to generate the command string comprises:
determining an authorization level for user (Paragraph 67 discloses workflow manager 29 provides a workflow engine configurable and manageable by authorized users 23. Workflow manager 29, for example, provides fully configurable governance process automation engine gives repository administrators control over object approval processes on a fine-grained basis, defining type-specific processes with content cardinality rules (e.g., information is optional for an object in draft status and mandatory in published status), inline validation policies (e.g., conditional content based on associated values) and role-based approval processes supporting any combination of parallel and sequential commenter and approver steps); and
comparing the authorization level to a database listing authorization levels corresponding to each of a plurality of operation types (Paragraph 67 discloses workflow manager 29 provides a workflow engine configurable and manageable by authorized users 23. Workflow manager 29, for example, provides fully configurable governance process automation engine gives repository administrators control over object approval processes on a fine-grained basis, defining type-specific processes with content cardinality rules (e.g., information is optional for an object in draft status and mandatory in published status), inline validation policies (e.g., conditional content based on associated values) and role-based approval processes supporting any combination of parallel and sequential commenter and approver steps).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 10, Wenzel et al. discloses the method of claim 2, wherein inputting the request and the metadata associated with the one or more assets into the machine learning model to generate the command string comprises:
determining a role for user based on a credential (Paragraph 67 discloses workflow manager 29 provides a workflow engine configurable and manageable by authorized users 23. Workflow manager 29, for example, provides fully configurable governance process automation engine gives repository administrators control over object approval processes on a fine-grained basis, defining type-specific processes with content cardinality rules (e.g., information is optional for an object in draft status and mandatory in published status), inline validation policies (e.g., conditional content based on associated values) and role-based approval processes supporting any combination of parallel and sequential commenter and approver steps); and
comparing the role to a database listing roles corresponding to each of a plurality of operation types (Paragraph 67 discloses workflow manager 29 provides a workflow engine configurable and manageable by authorized users 23. Workflow manager 29, for example, provides fully configurable governance process automation engine gives repository administrators control over object approval processes on a fine-grained basis, defining type-specific processes with content cardinality rules (e.g., information is optional for an object in draft status and mandatory in published status), inline validation policies (e.g., conditional content based on associated values) and role-based approval processes supporting any combination of parallel and sequential commenter and approver steps).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 11, Wenzel et al. discloses the method of claim 2, further comprising:
determining the first set of attributes for executing the first validation operation based on the command string (Paragraph 73 discloses Each of the assets in the data model may include one or more metadata attributes. Each of the metadata attributes may correspond to one of a plurality of metadata attribute types (e.g., metadata knowledge types)); and
determining the second set of attributes for executing the second validation operation based on the command string (Paragraph 73 discloses Each of the assets in the data model may include one or more metadata attributes. Each of the metadata attributes may correspond to one of a plurality of metadata attribute types (e.g., metadata knowledge types)).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 12, Wenzel et al. discloses the method of claim 2, further comprising:
determining an attribute set identifier for the first set of attributes based on the command string (Paragraph 73 discloses Example metadata attribute types include, for example, an identifier metadata attribute type, a classifier metadata attribute type, an artifact metadata attribute type, and a relationship metadata attribute type (alternatively referred to, respectively, as identifiers, classifiers, artifacts, and relationships). A schema may define which metadata attributes are included in each asset of a particular asset type); and
comparing the attribute set identifier to a database listing attribute sets corresponding to the attribute set identifier to determine the first set of attributes for performing the command string (Paragraph 73 discloses Example metadata attribute types include, for example, an identifier metadata attribute type, a classifier metadata attribute type, an artifact metadata attribute type, and a relationship metadata attribute type (alternatively referred to, respectively, as identifiers, classifiers, artifacts, and relationships). A schema may define which metadata attributes are included in each asset of a particular asset type).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 13, Wenzel et al. discloses the method of claim 2, further comprising:
receiving a plurality of content published to an application programming interface ("API") based on the validation results (Paragraph 103 discloses bi-directional data sharing have been challenging to accomplish, and only very limited sets of data are shared via APIs, FTP, or file transfer between companies);
filtering the plurality of content using a user content subscription setting for a user to obtain filtered content (Paragraph 112 discloses When adding a listing 202 to the consumed shares 156, it may be filtered based on the identity of the consumer that adds it, i.e. data that is relevant to the consumer's role within the company and/or the purpose of their analysis);
generating the validation results comprising the filtered content (Paragraph 133 discloses complex classifier refers to a classifier that has more than one set of name/value pairs that may be related to a taxonomy that filters one pair down from the previous pair); and
causing a user interface to be displayed to the user comprising the validation results (Paragraph 126 discloses Standards specification and management system 18 may include one or more user interfaces).
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 14, Wenzel et al. discloses the method of claim 2, further comprising:
determining, based on a batch filtering criterion, a respective relevance ranking for a plurality of content related to the validation results (Paragraph 382 discloses The dynamic entitlement management facility may provide for group level policy control with policy set ranking, external share restrictions, device location restrictions, policies that permit sharing by device, access to approved domains to restrict browser and kiosk asks, secure shared link policies, password, password strength, link expiration, mobile data plan policies, mobile editing policies, restricted third-party applications, mobile sync controls, automated deletion, file control policies, and the like);
comparing the respective relevance ranking to a respective threshold relevance ranking to determine that the plurality of content is to be published (Paragraph 382 discloses The dynamic entitlement management facility may provide for group level policy control with policy set ranking, external share restrictions, device location restrictions, policies that permit sharing by device, access to approved domains to restrict browser and kiosk asks, secure shared link policies, password, password strength, link expiration, mobile data plan policies, mobile editing policies, restricted third-party applications, mobile sync controls, automated deletion, file control policies, and the like); and
generating the validation results comprising the plurality of content to be published (Paragraph 221 discloses he retrieval results, which includes a list of assets generated based on the RDF file and RDF schema).
With respect to claim 15, Wenzel et al. discloses one or more non-transitory, computer-readable media comprising instructions that when executed by one or more processors causes operations comprising:
storing, within a data exchange, a plurality of assets of a plurality of asset types, and metadata associated with each asset of the plurality of assets, the metadata comprising (i) one or more first attributes defined using a first schema that is generic to each asset type of the plurality of asset types and (ii) one or more second attributes defined using a second schema specific to a first asset type of the plurality of asset types for a corresponding asset (Paragraph 4 discloses define, find, understand, use, and exchange data by managing standards and metadata as organizational assets within an enterprise repository & Paragraph 5 discloses standards specification and management system automatically and dynamically generates an ontology schema based on a data model that describes one or more standards for collection of data (generally referred to as a “data collection standard) from a set of entities in a uniform fashion);
querying, based on a request, the data exchange to identify one or more assets of the plurality of assets stored in the data exchange in order to validate the metadata associated with each asset of the one or more assets (Paragraph 88 discloses Asset generator 30 may use asset templates 32 to assist in the capture process and/or to validate the asset information. In one embodiment, asset generator 30 or a schema generation module generates a virtual schema by applying asset templates 32 to a base schema for an asset);
executing a first data validation operation comprising the one or more first validation rules to the first set of attributes of the one or more assets based on the first command portion and executing a second data validation operation comprising the one or more second validation rules to the second set of attributes of the one or more assets (Paragraph 67 discloses validation policies (e.g., conditional content based on associated values) and role-based approval processes);
generating based on the first data validation operation and the second data validation operation being executed, validation results for the one or more assets indicating that the metadata associated with each asset of the one or more assets has been validated or has failed to be validated (Paragraph 150 discloses a rule may be an annotation describing the mapping and/or transformation occurring between metadata elements, metadata element sets, or value domains. A rule may provide the means to store a description related to the difference or change between assets and may be used to define the way or the reason changes occur). Wenzel et al. does not disclose inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets.
However, Srinvasulu et al. teaches inputting the request and the metadata associated with the one or more assets into a machine learning model to generate a command string comprising a first command portion indicating one or more first validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets and a second command portion indicating one or more second validation rules to apply to the one or more assets to validate the metadata associated with the one or more assets, wherein the machine learning model identifies that the one or more assets upon which to validate comprise a first set of attributes defined using the first schema and a second set of attributes defined using the second schema, wherein the first command portion is generated by the machine learning model based on the first set of attributes being defined using the first schema and the second command portion is generated by the machine learning model based on the second set of attributes being defined using the second schema (Paragraph 80 discloses a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data. For example, features from this data can be extracted to generate metadata profiles, which can be used to train the machine learning model. Once trained, the machine learning model can be configured to receive input data (e.g., processed incoming data) and generate data quality improvement predictions).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Srinvasulu et al. with Wenzel et al. This would have facilitated sending, storing, and searching data. See Srinvasulu et al. Paragraph 3-4. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: database exchanges.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 3, because claim 16 is substantially equivalent to claim 3.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 4, because claim 17 is substantially equivalent to claim 4.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 5, because claim 18 is substantially equivalent to claim 5.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 6, because claim 19 is substantially equivalent to claim 6.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 11, because claim 20 is substantially equivalent to claim 11.
The Wenzel et al. reference as modified by Srinvasulu et al. teaches all the limitations of claim 2. Regarding claim 22, Wenzel et al. discloses the method of claim 2, wherein:
storing comprising storing the plurality of assets within the data exchange using cloud-based storage circuitry (Paragraph 196 discloses standards specification and management system 18 may be a three-tier application architecture using industry-standard Java Platform, Enterprise Edition (Java EE). Standards specification and management system 18 may be implemented as a robust and highly scalable deployment platform with internal or external hosting, or cloud deployment available);
querying comprises querying the data exchange using cloud-based control circuitry (Paragraph 88 discloses Asset generator 30 may use asset templates 32 to assist in the capture process and/or to validate the asset information. In one embodiment, asset generator 30 or a schema generation module generates a virtual schema by applying asset templates 32 to a base schema for an asset);
executing comprises executing the first validation operation and the second validation operation using the cloud-based control circuitry (Paragraph 67 discloses validation policies (e.g., conditional content based on associated values) and role-based approval processes); and
generating comprises generating the validation results using the cloud-based control circuitry, wherein cloud-based I/O circuitry displays a user interface comprising the validation results (Paragraph 150 discloses a rule may be an annotation describing the mapping and/or transformation occurring between metadata elements, metadata element sets, or value domains. A rule may provide the means to store a description related to the difference or change between assets and may be used to define the way or the reason changes occur). Wenzel et al. does not disclose inputting comprises inputting the request and the metadata associated with the one or more assets into the machine learning model using the cloud-based control circuitry.
However, discloses inputting comprises inputting the request and the metadata associated with the one or more assets into the machine learning model using the cloud-based control circuitry (Paragraph 80 discloses a trained machine learning model, such as a neural network, that is trained based on data with a known structure, such as labeled and/or categorized data. For example, features from this data can be extracted to generate metadata profiles, which can be used to train the machine learning model. Once trained, the machine learning model can be configured to receive input data (e.g., processed incoming data) and generate data quality improvement predictions).
he motivation to combine statement previously provided in the rejection of independent claim 2 provided above, combining the Wenzel et al. reference and the Srinvasulu et al. reference is applicable to dependent claim 22.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 20150058627 is directed to DATA DRIVEN SCHEMA FOR PATIENT DATA EXCHANGE SYSTEM: [Paragraph 25] A patient data exchange system may comprise at least one device (e.g., a plurality of devices). The devices in the patient data exchange system may include implantable medical devices, device programmers for implantable medical devices (i.e., programmer devices), personal computers, mobile computing devices, server computing devices, and/or other types of computing devices. The devices in the patient data exchange system may exchange patient data. Such patient data may include data regarding patients. For example, the patient data may include clinical study data collected from a patient. In another example, the patient data may include an electronic medical record or an electronic health record. In another example, the patient data may include insurance records regarding a patient. In yet another example, the patient data may include prescription medication data regarding a patient.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154