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 action is in response to claims dated 11/14/2023.
Claims pending in the case: 1-12
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claim 1 recites a “A functional test failure prediction (FTFP) engine…”. However, such engine may be software per se. There is no associated structural component within the claimed limitations, and as such the claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
All claims dependent on this/these claims, is/are also rejected due to their direct or indirect dependencies.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
comparing an outcome of the algorithm to the specific functional parameters to assess whether, were the product-specific tests actually applied, the product design would meet or exceed the specified functional parameters (Assessing test results is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
learning from at least the actual application of the product-specific tests, and eventual performance of a product resultant from the product design and the manufacturing design (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
A functional test failure prediction (FTFP) engine embodied in non-transitory computing code for execution by at least one processor (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).), comprising:
a plurality of inputs, capable of receiving at least: a product design; a manufacturing design for the product design; a plurality of specified functional parameters for the product design; bills of materials for the product design; and prior outcome feedback;
at least one algorithm for virtually applying a plurality of product-specific tests to the product design and the manufacturing design (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions) ;
a comparator capable of comparing (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
at least one learning module capable of learning (This high level recitation of the machine learning model / neural network and training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
a feedback loop to provide at least the comparator outcome and the learning of the learning module back to the plurality of inputs as the prior outcome feedback (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Moreover, displaying information for user interaction is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(a), x. An improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018) and MPEP 2106.05(g), iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)))
a graphical user interface output capable of providing at least the outcome of the comparator to a user (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
This claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2, 5, 9-12 pertain to types of data (This appears to be directed to the specification of data and a restriction to a particular type of source data. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Dependent claim 3-4 pertain to receiving and sending data (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Moreover, displaying information for user interaction is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(a), x. An improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018) and MPEP 2106.05(g), iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016))
Dependent claim 7-8 pertain to using AI model (This high level recitation of the machine learning model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Hence these claims are rejected as being abstract.
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.
Claim(s) 1-3, 5-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (Efficient Statistical Model Checking of Hardware Circuits With Multiple Failure Regions ) in view of Horie (US 20230044533).
Regarding Claim 1, Kumar teaches, a functional test failure prediction (FTFP) engine (Kumar: abstract, Pg. 945 col 2 [2]: statistical model checking) embodied in non-transitory computing code for execution by at least one processor, comprising:
a plurality of inputs, capable of receiving at least: a product design; a manufacturing design for the product design; a plurality of specified functional parameters for the product design; bills of materials for the product design; and prior outcome feedback (Kumar: Fig. 2, PG. 947 col 2 section A [2], Pg. 949 col 2 section IV [1]: parameters of the design with reliability properties (functional parameters); hardware circuit model M encodes the components (materials); Pg. 951 col 1 [1]: variational Bayes uses a prior distribution of samples (prior outcome));
at least one algorithm for virtually applying a plurality of product-specific tests to the product design and the manufacturing design (Kumar: Fig. 2, Pg. 950 col 2 [1]: Monte Carlo simulations (tests));
a comparator capable of comparing an outcome of the algorithm to the specific functional parameters to assess whether, were the product-specific tests actually applied, the product design would meet or exceed the specified functional parameters (Kumar: Fig. 2, Pg. 952 col 2 [1], Pg. 948 col 2 section C: compare estimated failure rate against threshold);
at least one learning module capable of learning from at least the actual application of the product-specific tests, and eventual performance of a product resultant from the product design and the manufacturing design (Kumar: Pg. 949 col 1 section D [1]: Variational Bayes learns from the tests and their performance);
a feedback loop to provide at least the comparator outcome and the learning of the learning module back to the plurality of inputs as the prior outcome feedback (Kumar: Pg. 949 col 1 section D [1], Pg. 951 col 1 [1]: “Variational Bayes required a prior distribution of the samples to be specified, as an initial condition of the iterative process” – prior outcome feedback; The feedback loop is then established by using the learned Gaussian mixture
distribution (see equation (7)) in the Monte Carlo simulation as the updated distribution); and
While Kumar does not specifically recite
a graphical user interface output capable of providing at least the outcome of the comparator to a user,
It is obvious that the outcomes are provided to the user using a graphical user interface which are being used to generate the charts in Fig. 6-8;
Nonetheless, Horie teaches, a graphical user interface output capable of providing at least the outcome to a user (Horie: [124, 202]: user interface for inputting and outputting information);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kumar and Horie because the combination would enable facilitating inputting and outputting information using an user interface as is done in the art.
Regarding claim 2, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the product design, the specified functional parameters, and the manufacturing design are manually uploaded to the inputs from a graphical user interface (Horie: [204]: user input design which may be done by user).
Regarding claim 3, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the product design, the specified functional parameters, and the manufacturing design are automatically uploaded to the inputs (Horie: [204]: Selected design file automatically uploaded as input).
Regarding claim 5, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the product-specific tests include current, voltage, power, and error rate testing (Kumar: Pg. 947 section III A [1]: variations in voltage current as source or error) .
Regarding claim 6, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the outcome of the comparator includes weak links in the product design (Kumar: Pg. 947 section VII A [1]: “The delay of the SRAM cell depends on the threshold voltages of the transistors. An SRAM cell is said to fail if its delay exceeds a predefined
timing constraint." - A threshold voltage is a parameter that determines when the
transistor starts conducting. Hence, by identifying critical threshold voltages, the
critical transistor is identified, which then represents a weak link between the
component it connects.).
Regarding claim 7, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the learning module comprises an artificial intelligence (Al) (Kumar: Abstract, Pg. 945 col 2 [4] : “employ variational Bayes, a variational inference technique used in machine learning, to infer the distribution of the rare events in the circuit”).
Regarding claim 8, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the comparator outcome is a probabilistic prediction of compliance with the specified functional parameters (Kumar: Pg. 948 col 2 section C [2]: “SMC obtains an estimate of the failure rate ….comparing this estimated failure rate …”; Pg. 946 col 1 [2]: probabilistic info from machine learning model).
Regarding claim 9, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the prior outcome feedback includes historic data (Kumar: Pg. 950 col 2: sampled data (historic data) to train the Gaussian mixture distribution) (Horie: [85]: historic data as training data).
Regarding claim 10, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the historic data includes atypically high failure rates for elements on the bills of materials (Kumar: Pg. 948 col 1 section B [2]: rare event scenario).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (Efficient Statistical Model Checking of Hardware Circuits With Multiple Failure Regions ) and Horie (US 20230044533) in view of Nulman (US 20210249316).
Regarding claim 4, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the automatic upload is using ODB++ (Horie: [200, 204]: use CAD design file – ODB is a well -know CAD to CAM data exchange format);
Nulman teaches, using ODB++ (Nulman: [39]: “converted CAD/CAM data packages can be, for example,… ODB++…);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kumar, Horie and Nulman because the combination would enable using one of the options of CAD/CAM data uploads known in the art.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (Efficient Statistical Model Checking of Hardware Circuits With Multiple Failure Regions ) and Horie (US 20230044533) in view of Sasaki (US 9385924).
Regarding claim 11, Kumar and Horie teach the invention as claimed in claim 1 above and, wherein the bills of materials (Kumar: Fig. 2, PG. 947 col 2 section A [2], Pg. 949 col 2 section IV [1]: parameters of the design with reliability properties (functional parameters); hardware circuit model M encodes the components (materials));
The examiner finds that it is obvious for bills of materials to include components which are compatible and incompatible based on the connections in the design.
Nonetheless, Sasaki further teaches, material includes incompatibility as between parts (Sasaki: col 6 lines 48-67: device list with components; Claim 1: “the first device component being incompatible with the target device and the second device component being compatible with the target device “);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kumar, Horie and Sasaki because the combination would enable using a device list indicating compatible and incompatible components to easily identify suitable components and facilitate the design process (see Sasaki [5-6]).
Regarding claim 12, Kumar and Horie teach the invention as claimed in claim 1 above and,
Sasaki further teaches, wherein the bills of materials includes incompatibility of parts with the manufacturing design (Sasaki: col 6 lines 48-67: device list with components; Claim 1: “the first device component being incompatible with the target device and the second device component being compatible with the target device “);
The same motivation to combine stated above applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
Prosecution Insights