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 .
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 – The claim recites “the feature information is = extracted.” Please delete the equal sign.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11, 12, 25, and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 11 and 25 recite, in the second element, “the plurality of impact weights corresponding to the plurality of features.” This term lacks antecedent basis.
Claims 11 and 25 recite, in the second element, “the plurality of features.” This term lacks antecedent basis.
Claims 12 and 26 are rejected based on their dependence from Claims 11 and 25.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 7, 11, 13, 14, 15, 18, 21, 25, 27, and 28 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Eakins et al. (US 20250290883 A1)[hereinafter “Eakins”].
Regarding Claims 1 and 15, Eakins discloses a method for electronic component authenticity identification [See Fig. 5 and Paragraphs [0065]-[0066]] comprising:
obtaining chip data of an electronic component by providing a voltage to each pin-to-pin connection of the electronic component [See Fig. 2A.Paragraph [0053] – “One or more signal injectors 206 may inject a signal into one or more object ports 204, with one or more signal receivers 208 being used to measure the signal.”Paragraph [0055] – “A reflected wave 212 and/or transmitted wave 214 may be defined in some embodiments as a measure of the response that an object 202 (for example a circuit) presents to a current when a signal (such as a voltage) is applied.”];
extracting feature information of the chip data for reducing noise of the chip data [Paragraph [0065] – “To generate and parse unique device signatures from the rich dataset, dimensionality reduction may be utilized in the form of principal component analysis (PCA).”];
providing the feature information of the chip data to a trained deep learning model [Paragraph [0066] – “In this embodiment, by way of non-limiting example, when a dataset consisting of similar objects (e.g., known authentic objects 504) and an unknown or suspect object 502 may be input into the PCA algorithm, such that the similar objects will form a cluster, with an unknown object appearing at a point some distance away from the centroid or the outer bounds of the cluster. In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510 which, by way of non-limiting example, may be constructed from measurements taken in a production line from a trusted manufacturer.”]; and
providing a user with an authenticity indication for the electronic component based on an output of the trained deep learning model [Paragraph [0066] – “In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510 which, by way of non-limiting example, may be constructed from measurements taken in a production line from a trusted manufacturer. A measured behavior three dimensional graph 512 may be utilized to show points, such that normal behavior points can be visually distinguished from suspect behavior points.”Paragraph [0068] – “In some embodiments these data may be utilized for counterfeit detection”].
Regarding Claim 15, Eakins discloses a corresponding system [Fig. 3B] including a socket for receiving an electronic component [Fig. 3B, see the ports in 324/328]; a processor; and a memory [Paragraph [0047] – “In an embodiment, the computing device 100 includes at least one processor 102 and memory (non-volatile memory 108 and/or volatile memory 110).”] having stored thereon a set of instructions for performing the method using the processor [Paragraph [0047] – “Referring now to FIG. 1, a block diagram illustrates an example of a computing device 100, through which embodiments of the disclosure can be implemented.”].
Regarding Claims 4 and 18, Eakins discloses that the chip data comprises: time-series waveform data [Paragraph [0055] – “A reflected wave 212 and/or transmitted wave 214 may be defined in some embodiments as a measure of the response that an object 202 (for example a circuit) presents to a current when a signal (such as a voltage) is applied.” The waves are inherently measured as time-series data.] mapped from each pin of the electronic component to another pin of the electronic component [Paragraph [0060] – “The multiport configuration depicted in FIG. 3A may be utilized to speed up measurement times while reducing losses due to the fact that no external switches are needed to collect all possible port combinations. … A multiport VNA may be utilized to allow for scattering parameters to be collected between any (for example) two ports of an object. Moreover, a multiport VNA may allow scattering parameter(s) to be collected between any two ports in this embodiment where such a multiport configuration may speed up measurement times while reducing losses due to the fact that no external switches are needed to collect all possible port combinations. Frequency dependent complex S-parameters 316 may be utilized to determine a matrix 318 Sij(f), where x, y denote integrated circuit (IC) pins connected to ports 1 and 2 of a VNA. In some embodiments Sij may be utilized to represent general S-parameter 316 measurements, where i and j may be 1 to n, where n may correspond to the number of VNA ports.”].
Regarding Claims 7 and 21, Eakins discloses that the obtaining the chip data comprises:
testing each pin of the electronic component to be connected to another pin of the electronic component [Paragraph [0060] – “The multiport configuration depicted in FIG. 3A may be utilized to speed up measurement times while reducing losses due to the fact that no external switches are needed to collect all possible port combinations. … A multiport VNA may be utilized to allow for scattering parameters to be collected between any (for example) two ports of an object. Moreover, a multiport VNA may allow scattering parameter(s) to be collected between any two ports in this embodiment where such a multiport configuration may speed up measurement times while reducing losses due to the fact that no external switches are needed to collect all possible port combinations. Frequency dependent complex S-parameters 316 may be utilized to determine a matrix 318 Sij(f), where x, y denote integrated circuit (IC) pins connected to ports 1 and 2 of a VNA. In some embodiments Sij may be utilized to represent general S-parameter 316 measurements, where i and j may be 1 to n, where n may correspond to the number of VNA ports.”];
determining time-series waveform data based on the testing [Paragraph [0055] – “A reflected wave 212 and/or transmitted wave 214 may be defined in some embodiments as a measure of the response that an object 202 (for example a circuit) presents to a current when a signal (such as a voltage) is applied. At higher frequencies, this may qualitatively describe how efficiently injected energy couples into an object 202. In some embodiments, due to complex mutual coupling effects present at higher frequencies (by way of non-limiting example, those in excess of 1 MHz), impedance may become a dynamic function of frequency and path within an object 202 (such as a circuit). Thus, in some embodiments this may lead to a unique frequency dependent upon reflection and/or transmission spectra for a particular electromagnetic wave path within an object 202. In this embodiment a unique identifying signature of an object may be generated based upon a combination of the object port(s) involved and the reflection/transmission frequency/wave.” The waves are inherently measured as time-series data.]; and
uploading the time-series waveform data as the chip data of the electronic component to the trained deep learning model [Paragraph [0066] – “In this embodiment, by way of non-limiting example, when a dataset consisting of similar objects (e.g., known authentic objects 504) and an unknown or suspect object 502 may be input into the PCA algorithm, such that the similar objects will form a cluster, with an unknown object appearing at a point some distance away from the centroid or the outer bounds of the cluster. In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510 which, by way of non-limiting example, may be constructed from measurements taken in a production line from a trusted manufacturer.”].
Regarding Claims 11 and 25, Eakins discloses determining a plurality of impact weights based on an attention mechanism [Paragraph [0065] – “To generate and parse unique device signatures from the rich dataset, dimensionality reduction may be utilized in the form of principal component analysis (PCA). This technique may construct an orthogonal basis which maximizes the total variance in a dataset based on a weighted linear combination of observation variables.”]; and
determining the output of the trained deep learning model based on the plurality of impact weights corresponding to the plurality of features [Paragraph [0066] – “In this embodiment, by way of non-limiting example, when a dataset consisting of similar objects (e.g., known authentic objects 504) and an unknown or suspect object 502 may be input into the PCA algorithm, such that the similar objects will form a cluster, with an unknown object appearing at a point some distance away from the centroid or the outer bounds of the cluster. In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510 which, by way of non-limiting example, may be constructed from measurements taken in a production line from a trusted manufacturer. A measured behavior three dimensional graph 512 may be utilized to show points, such that normal behavior points can be visually distinguished from suspect behavior points.”].
Regarding Claims 13 and 27, Eakins discloses that the trained deep learning model is trained [Paragraph [0066] – “In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms”] with a plurality of feature information of training chip data sets [Paragraph [0065] – “Turning now to FIG. 5, a schematic illustration 500 depicting statistical analysis utilizing a behavioral database for a suspect object 502 and known authentic objects 504 is shown according to various embodiments.”] and a plurality of authenticity ground truth labels [Paragraph [0066] – “measurements taken in a production line from a trusted manufacturer.”] corresponding to training chip data sets, the training chip data sets comprising an authentic chip data set and a counterfeit chip data set [Paragraph [0066] – “In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510”].
Regarding Claims 14 and 28, Eakins discloses that the trained deep learning model is trained further with a plurality of model indications corresponding to the training chip data sets [Paragraph [0066] – “In this way, metrics such as distance from centroid, distance from cluster bounds, etc. may be established for pass/fail criteria for counterfeit detection. These metrics may be established and checked automatically using machine learning algorithms against the behavioral database 510”].
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) 2 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eakins et al. (US 20250290883 A1)[hereinafter “Eakins”] and Blass et al. (US 20200327244 A1)[hereinafter “Blass”].
Regarding Claims 2 and 16, Eakins fails to disclose providing the authenticity indication in response to a permission based on location information of the user of the electronic component, wherein the location information includes an internet protocol address.
However, Blass discloses allowing access to database data using location information including IP addresses [Abstract – “The access manager component permits a user to define a set of allowed IP addresses and/or a range of allowed IP addresses for connecting to a database and/or accessing data within a database.”Paragraph [0098] – “The system in some examples permits a user to customize control of which user devices and/or locations can access and update data at the application layer without altering system infrastructure. The system creates access restrictions based on IP addresses coming into the dbase.”]. It would have been obvious to use such a data security scheme to permit/deny access to the authenticity indication in order to prevent undesired users from accessing that information.
Claim(s) 5 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eakins et al. (US 20250290883 A1)[hereinafter “Eakins”] and Natanzon (US 20200226102 A1).
Regarding Claims 5 and 19, Eakins fails to disclose that the chip data is included in one compressed file, wherein the one compressed file comprises a plurality of subfiles.
However, Natanzon discloses the use of a data compression strategy where a plurality of subfiles is compressed into one compressed file [Paragraph [0036] – “A ZIP file is an example of a compressed archive file format.”Paragraph [0037] – “ZIP is an archive file format that supports lossless data compression. … A ZIP file is a data container that may store a set of files in compressed form. A ZIP file or ZIP container may contain any number of entries including one or more files or directories that may have been compressed into a single ZIP file.”]. It would have been obvious to use such a scheme to reduce the amount of data needed for transmission/storage.
Claim(s) 8 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eakins et al. (US 20250290883 A1)[hereinafter “Eakins”] and Saracin et al., Echo Cancellation Using the LMS Algorithm, U.P.B. Sci. Bull., 2009 [hereinafter “Saracin”].
Regarding Claims 8 and 22, Eakins fails to disclose that the feature information is extracted based on the chip data applying a polynomial function, wherein the polynomial function for extracting the feature information comprises: p(x) =
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55
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aixi where p(x) is an extracted feature, aᵢ is a coefficient that minimizes a mean squared error, xi is the chip data, and n is a degree.
However, Saracin discloses the use of the recited function in optimizing the estimation of a data set [See the use of the Wiener filter in section 2.2 and the equation at the bottom of Fig. 1 – “The Wiener filter offers an optional estimation of Nk, nk” and “w(i) are the coefficients of the Wiener filter.”]. It would have been obvious to use the recited function in determining appropriate coefficients in order to best estimate the feature information.
Claim(s) 12 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eakins et al. (US 20250290883 A1)[hereinafter “Eakins”] and Han et al. (US 20180174036 A1)[hereinafter “Han”].
Regarding Claims 12 and 26, Eakins fails to disclose that the trained deep learning model is an artificial recurrent neural network (RNN) architecture using an input gate, an output gate, a forget gate, and a new input gate, and wherein the input gate corresponds to the plurality of features, the output gate corresponds to the output, the forget gate is determined based on an input vector and a hidden state vector, and the new input gate is determined based on the input vector and the hidden state vector.
However, Han teaches the use of such a machine learning system [See Paragraph [0027]]. It would have been obvious to use such a type of machine learning for the machine learning because it could “make the training procedure much more stable and allow the model to learn long-term dependencies” [See Paragraph [0022] of Han].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20180268172 A1 – ELECTRONIC DEVICE AUTHENTICATION SYSTEM
US 20160282394 A1 – ELECTRONIC COMPONENT CLASSIFICATION
US 20170289786 A1 – APPARATUS AND METHOD FOR RAPID ELECTRONIC DEVICE DISCOVERY
US 20210365762 A1 – DETECTING BEHAVIOR PATTERNS UTILIZING MACHINE LEARNING MODEL TRAINED WITH MULTI-MODAL TIME SERIES ANALYSIS OF DIAGNOSTIC DATA
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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