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 .
DETAILED ACTION
The instant application having Application No. 18351729 has a total of 12 claims pending in the application.
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.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Therefore, claims 1-13 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Processing the instance data using one or more … algorithms to derive knowledge to be added to the knowledge model” A user mentally or with pencil and paper looks at data to see if its suitable to be added to their graph.
“augmenting the knowledge model to represent the derived knowledge” The user mentally or with pencil and paper adds the data to their graph.
“Verifying the derived knowledge prior to augmentation by requiring that the derived knowledge appears in at least a threshold number of independent data instances or occurs over a threshold period, and only augmenting the knowledge model upon satisfying this criterion” The user mentally or with pencil and paper examines the sources to make sure they meet the criteria to be included.
“Using the augmented knowledge model to perform at least one control or monitoring action in the industrial automation system based on the derived knowledge” The user mentally or with pencil and paper looks at the industrial system and uses the information to examine it.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“data analytics algorithm” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote a generic machine learning algorithm with no limitations or details beyond generic, off the shelf algorithms.
“obtaining instance data relating to at least one component of an industrial automation system, wherein the component represent an instance of at least one entity in the knowledge model” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“data analytics algorithm” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote a generic machine learning algorithm with no limitations or details beyond generic, off the shelf algorithms.
“obtaining instance data relating to at least one component of an industrial automation system, wherein the component represent an instance of at least one entity in the knowledge model” (MPEP 2106.05(d)(II) indicate that merely “receiving or transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2-9 and 11-12, these claims contain additional mental steps and generic machine learning algorithms similar to claim 1, and are rejected for similar reasons.
As per claim 10, this claim contains similar mental steps to claim 1, and is rejected for similar reasons.
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 1-12 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 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 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.
As per claim 1, this claim contains the limitation “verifying the derived knowledge prior to augmentation by requiring that the derived knowledge appears in at least a threshold number of independent data instances or occurs over a period of time, and only augmenting the knowledge model upon satisfying this criterion.” However, this limitation is not supported by the specification. At no time does the specification discuss verifying the knowledge by whether or not the knowledge occurs over a threshold period. It also at no time discusses the use of independent data instances.
At best, the specification describes verifying by “a threshold number of appearances maybe required before knowledge is deemed safe to add.” However, it does not require “independent data instances.” It just requires “a threshold number of appearances.” At no time does the specification describe a threshold related to a period of time. This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 2-12, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al (“Learning to Update Knowledge Graphs by Reading News”) in view of Wu et al (US 20200201875 A1) and Defelice (US 20190236139 A1).
As per claim 1, Tang discloses, “A method of automatically augmenting” (Pg.2632, abstract; EN: this denotes the model automatically updating the knowledge graph). “a knowledge model… the method comprising” (Pg.2632, abstract; EN; this denotes a knowledge graph (i.e. knowledge model)).
“Obtaining instance data relating to at least one component … wherein the component represents an instance of at least one entity in the knowledge model” (Pg.2634, particularly C1, the 1Hop-Subrgaph section; EN: this denotes updating the Barack Obama and Michelle Obama sections based upon new data discovered by the system, with the Barack Obama and Michelle Obama being examples of components of the knowledge graph).
“Processing the instance data using one or more data analytics algorithms to drive knowledge to be added to the knowledge model” (Pg.2634, particularly C1, the 1Hop-Subrgaph section; EN: this denotes updating the Barack Obama and Michelle Obama sections based upon new data discovered by the system).
“augmenting the knowledge model to represent the derived knowledge” (Pg.2634, particularly C1, the 1Hop-Subrgaph section; EN: this denotes updating the Barack Obama and Michelle Obama sections based upon new data discovered by the system However, Tang fails to explicitly disclose, “representing one or more automation engineering domains”, “component of an industrial automation system”, “verifying the derived knowledge prior to augmentation by requiring that the derived knowledge appears in at least a threshold number of independent data instances or occurs over a threshold period, and only augmenting the knowledge model upon satisfying this criterion”, and “using the augmented knowledge model to perform at least one control or monitoring action in the industrial automation system based on the derived knowledge”
Wu discloses, “representing one or more automation engineering domains” (Abstract; EN: this denotes knowledge graphs used for industrial operations).
“component of an industrial automation system” (Pg.5, particularly paragraph 0046; EN: this denotes portions of the knowledge graphs being components of the industrial process).
“Verifying the derived knowledge prior to augmentation by requiring … and only augmenting the knowledge model upon satisfying this criterion” (pg.8, particularly paragraph 0066; EN: this denotes looking at potential new information and determining if it is appropriate to be added to the system, and if not, it is not added).
“using the augmented knowledge model to perform at least one control or monitoring action in the industrial automation system based on the derived knowledge” (Pg.2, particularly paragraph 0023; EN: this denotes the knowledge graph being used in relation to controlling and monitoring the specific industrial operation).
Defelice discloses, “…requiring that the derived knowledge appears in at least a threshold number of independent data instances or occurs over a threshold period…” (Pg.4, particularly paragraph 0050; EN: this denotes using multiple sources to support information being considered for a system).
Tang and Wu are analogous art because both involve knowledge graphs.
Before the effective filing date it would have been obvious to one skilled in the art of knowledge graphs to combine the work of Tang and Wu in order to automatically update knowledge graphs relating to industrial systems.
The motivation for doing so would be to allow “the customized industrial graph knowledge base to be updated as new knowledge is gained” (Wu, Pg.7, paragraph 0064) or in the case of Tang, allow the system to read data other than news in order to update knowledge graphs for other types of data.
Therefore before the effective filing date it would have been obvious to one skilled in the art of knowledge graphs to combine the work of Tang and Wu in order to automatically update knowledge graphs relating to industrial systems.
Defelice and Tang modified by Wu are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Defelice and Tang modified by Wu in order to use multiple sources to confirm information.
The motivation for doing so would be to allow the system to have “each fact … accompanied by a representation of the confidence that the system has in each separate fact” (Defelice, Pg.4, paragraph 0050) or in the case of Tang modified by Wu, allow the system to only include reliable data that has been verified by multiple sources to be added to their system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Defelice and Tang modified by Wu in order to use multiple sources to confirm information.
As per claim 2, Tang discloses, “wherein the one or more data analytics algorithms for processing the instance data to derive knowledge to be added to the knowledge model comprises one or more machine learning algorithms” (Pg.2633, C2, second paragraph; EN: this denotes the use of a neural model).
As per claim 3, Tang discloses, “further comprising creating a machine learning model to derive the knowledge to be added to the knowledge model” (Pg.2633, C2, second paragraph; EN: this denotes the use of a neural model).
As per claim 4, Tang discloses, “wherein creating the machine learning model comprises obtaining instance data relating to a first instance entity in the knowledge model and using the instance data of the first instance as target data for training the machine learning model” (Pg.2636, C1-C2, particularly section 4.1; EN: this denotes training the model with data related to the knowledge graph).
As per claim 5, Tang discloses, “Wherein training the model further comprises obtaining instance data relates to at least one second instance of a second entity in the knowledge model and using the instance data of the at least one second instance as feature data for training the machine learning model” (Pg.2636, C1-C2, particularly section 4.1; EN: this denotes training the model with data related to the knowledge graph).
As per claim 6, Tang discloses, “wherein the first and second concepts are selected based on a direct or indirect link therebetween in the knowledge model” (Pg.2636, C2, First paragraph; EN: this denote data about the concept and the connection between hops between aspects of the knowledge graph).
As per claim 7, Tang discloses, “further comprising identifying one or more further concepts relating to the second concept in the knowledge model, and obtaining instance data for those further concepts for use in training the model” (Pg.2636, C1-C2, particularly section 4.1; EN: this denotes training the model with data related to the knowledge graph).
As per claim 8, Tang discloses, “validating the created machine learning model” (pg.2636, C2, first paragraphs; EN: this denotes the use of validation data).
As per claim 9, Tang discloses, “further comprising using the created machine learning model to process new instance data to generate one or more responses to be added to the knowledge model” (Pg.2634, particularly C1, the 1Hop-Subrgaph section; EN: this denotes updating the Barack Obama and Michelle Obama sections based upon new data discovered by the system).
AS per claim 10, Tang discloses, “further comprising using the augmented knowledge model to perform semantic…” (pg.2638 particularly C2, first paragraph; EN: This denotes the system responding to data semantically).
Wu discloses, “querying” (pg.1, particularly paragraph 0002; EN: this denotes the system dealing with queries related to the data in the knowledge graph).
Claim Rejections - 35 USC § 103
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al (“Learning to Update Knowledge Graphs by Reading News”) in view of Wu et al (US 20200201875 A1) and Defelice (US 20190236139 A1) and further in view of Potts et al (US 20200293712 A1).
As per claim 11, Tang fails to explicitly disclose, “comprising annotating the derived knowledge in the knowledge model to indicate its being algorithm derived.”
Potts discloses, “comprising annotating the derived knowledge in the knowledge model to indicate its being algorithm derived” (Pg.12, particularly paragraph 0118; EN: this denotes giving confidence labels to automatically derived annotations. When combined with the Tang reference, this denotes the various labels/decisions created by the automatic system).
Potts and Tang are analogous art because both involve model training.
Before the effective filing date it would have been obvious to one skilled in the art of model training to combine the work of Tang and Potts in order to label automatically generated data.
The motivation for doing so would be to “include a confidence value presenting a probability with which an automatic annotator correctly identified a given entity type for an entity mentioned in the text of a document” (Potts, Pg.12, particularly paragraph 0118) or in the case of Tang, allow the system to label added data with a confidence value in order to inform the user of how confident the system is in properly labeling of data added to the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of model training to combine the work of Tang and Potts in order to label automatically generated data.
As per claim 12, Potts discloses, “comprising annotating the derived knowledge in the knowledge model to indicate its uncertainty” (Pg.12, particularly paragraph 0118; EN: this denotes giving confidence labels to automatically derived annotations. When combined with the Tang reference, this denotes the various labels/decisions created by the automatic system).
Response to Arguments
In pg.6, the Applicant argues in regards to the rejection under U.S.C. 101,
The claims have been amended to recite using the augmented knowledge model to perform at least one control or monitoring action in the industrial automation system based on the derived knowledge. The Specification explicitly teaches that the augmented knowledge model can be applied to control or monitor the industrial system, for instance to detect faults, optimize operations, or trigger alarms.
This feature grounds the allegedly abstract steps in a concrete industrial application. The updated knowledge is actually used to influence physical system operation (e.g., fault diagnosis or adaptive control). Such a limitation shows a technological outcome and effect (improving industrial system performance or reliability), by integrating the judicial exception into a specific practical use. This feature is not a mere nominal environment, but actively links the method to improved operation of an automated industrial process.
In response, the Examiner maintains the rejection as shown above. Merely adding that the system can optionally generically “control” the industrial automation system, or monitor action of the industrial automation system does not change this from an abstract idea. The act of monitoring an industrial automation system can be performed in the human mind, with the human watching the automated industrial system to see if it is operating correctly. Merely adding steps that can be performed in the human mind does not cause the claim to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1-12 have been considered but are either repetitions of the above arguments, or moot in view of the new ground(s) of rejection.
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 BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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