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 .
Status of Claims
Claims 1-20 remain pending and are ready for examination.
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 1-6, 15-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Independent claim 1 recites a method, , independent claim 16 recites a system. Therefore, step 1 is satisfied for claims 1-20.
Step 2A Prong One:
The claim(s) recite(s) mental process steps of:
retrieving a neural network configured to process the measurement dataset to generate a predicted value with predicted measurement uncertainty of a target physical parameter, the neural network being pre-trained based on a plurality of reference datasets for measuring the target physical parameter with known reference values and known uncertainties; (this step recite abstract mental processes that can be performed by the human mind or practicably with pen and paper. MPEP § 2106.04(a)(2)(II). The concept of generating the predicted value is a mental process (e.g., observations, evaluations, judgments, and opinions) that is applied and performed in a computing environment—i.e., an abstract idea. See MPEP § 2106.04(a)(2)(I]); see also Elec. Power Grp., 830 F.3d at 1354 (“[A]nalyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.”’). ).
forward-propagating the measurement dataset taken from the metrology system through the neural network to generate the predicted value with the predicted measurement uncertainty of the target physical parameter having the reference precision higher than indicated by the instrumental precision as a result of the neural network being trained to extract and remove at least a portion of uncertainty caused by imprecision in the metrology system. (this step recite abstract mental processes that can be performed by the human mind or practicably with pen and paper. MPEP § 2106.04(a)(2)(II). The concept of generating the predicted value is a mental process (e.g., observations, evaluations, judgments, and opinions) that is applied and performed in a computing environment—i.e., an abstract idea. See MPEP § 2106.04(a)(2)(I]); see also Elec. Power Grp., 830 F.3d at 1354 (“[A]nalyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.”’). ).
Step 2A Prong Two:
The claim/s recites the combination of the additional elements, the additional elements in the claim are:
receiving a measurement dataset originated by a metrology system characterized by an instrumental precision worse than a reference precision of a calibration metrology system and a set of underlying metrology physical principles; (in claim 1 and 16)
retrieving a neural network configured to process the measurement dataset (in claim 1 and 16)
forward-propagating the measurement dataset taken from the metrology system through the neural network (in claim 1 and 16)
a memory for storing instructions and a processor for executing the instructions
The bold elements above are directed to mere insignificant extra-solution activity. See MPEP 2106.04(d)(I) and 2106.05(g). The act of transmitting data based on the abstract idea fails to integrate the judicial exception into a practical application as it does not differ from those actions that have previously been held to be extra-solution activity, such as “presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, “selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display”, and “requiring a request from a user to view an advertisement and restricting public access.” The judicial exception is not integrated into a practical application because the remaining additional elements amount to nothing more than generic components recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.04(d)(I) and 2106.05(f).
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The core of the claim is to receive a dataset, using (neural network) to process the dataset, and output a newly predicted value with predicted measurement. As discussed above, the additional elements amount to nothing more than mere instructions to apply the exception using generic computer component(s) and insignificant extra-solution activity. These cannot provide an inventive concept, and thus the claims are patent-ineligible.
Claims 2-6, 15 and 17-18 and 20 directed to the same abstract idea without significantly more. The claims either recite an additional insignificant extra-solution activity OR recite an additional mental process to evaluate and judge using pen and paper. There are no additional elements recited in these claims that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea. Therefore, the claims are rejected under the same abstract idea as claim 1 or 16.
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 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.
Claims 1-5, 15, 16-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pandev et al., U.S. Pub No: US 20210165398 A1 (Hereinafter “Pandev”).
Regarding claim 1, Pandev discloses A metrology method, comprising:
receiving a measurement dataset originated by a metrology system characterized by an instrumental precision worse than a reference precision of a calibration metrology system and a set of underlying metrology physical principles (see paragraph [0043-0044, 0056], wherein the system receives measurement data (e.g. measured spectra). See also paragraph [0088], wherein reference metrology system is a trusted measurement system that generates sufficiently accurate measurement results. However Pandev notes that these trusted reference systems are too slow to be used. This corresponds to the instrumental precision worse than a reference precision of a calibration metrology system … as claimed);
retrieving a neural network configured to process the measurement dataset to generate a predicted value with predicted measurement uncertainty of a target physical parameter (see paragraph [0047, 0089], wherein utilizing neural network to process the metrology data. See also paragraph [0056], wherein the trained model provides both an estimate of the value of parameter of interest and the uncertainty of the measured value), the neural network being pre-trained based on a plurality of reference datasets for measuring the target physical parameter with known reference values and known uncertainties (see paragraph [0042, 0046-0047, 0049], wherein the neural network is pre-trained using Design of Experiments (DOE) measurement data combined with known reference values ); and
forward-propagating the measurement dataset taken from the metrology system through the neural network to generate the predicted value with the predicted measurement uncertainty of the target physical parameter having the reference precision higher than indicated by the instrumental precision as a result of the neural network being trained to extract and remove at least a portion of uncertainty caused by imprecision in the metrology system (see paragraph [0056], wherein the trained measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra) collected by the measurement system. Pandev further explain in paragraph [0013], that the traditional training minimizes total measurement uncertainty which is an aggregation errors including precision errors, tool-to-tool matching errors. To improve upon this, In paragraph [0017, 0040-0041], Pandev’s system specifically regularizes the optimization process using domain knowledge, such as the probability distributions associated with measurement precision. ).
Regarding claim 2, Pandev discloses wherein the instrumentation precision is associated with at least one systematic error of at least one instrumental component of the metrology system (see paragraph [0007,0013, 0027-0028, 0081], wherein for example, precision errors, tool-to-tool matching errors, or parameter tracking errors corresponds to systematic error as claimed).
Regarding claim 3, Pandev discloses wherein the instrumentation precision is associated with an instability of at least one instrumental component of the metrology system (see paragraph [0050-0051, 0076]).
Regarding claim 4, Pandev discloses wherein the plurality of reference datasets are generated via physical simulation based on the set of underlying metrology physical principles with the known reference values and known uncertainties of the target physical parameter (see paragraph [0046, 0061], wherein reference values 155 are simulated).
Regarding claim 5, Pandev discloses wherein the plurality of reference datasets are generated by one or more calibration metrology systems based on the set of underlying metrology physical principles and having reference precisions higher than the instrumental precision of the metrology system (see paragraph [0088],).
Regarding claim 15, Pandev discloses wherein a loss function for training the neural network (see paragraph [0050, 0054, 0086-0087]) comprises an optimization parameter representing measurement uncertainty of the target physical parameter, the optimization parameter being dependent on the plurality of reference datasets (see paragraph [0040, 0050-0051, 0086-0087]).
Claims 16-18 and 20 are rejected under the same rationale as claims 1-5 and 15.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-14 are rejected under 35 U.S.C. 103 as being unpatentable over Pandev et al., U.S. Pub No: US 20210165398 A1 (Hereinafter “Pandev”) in view of (“Machine learning designed optical lattice atom interferometer”, 5 pages, 2022) (Hereinafter “Colussi”).
Regarding claim 6, Pandev fails to explicitly disclose the limitation below.
Colussi discloses an atomic interferometer (see page1-3).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Pandev to include the missing limitation, as taught by Colussi, since doing so would improve parameter estimation accuracy and reduce uncertainties.
Regarding claim 7, Pandev in view of Colussi further disclose wherein the measurement dataset comprises at least one atomic interferogram image (Colussi, see page2 and fig.2(a), wherein applied acceleration induces a phase difference between the two arms of the interferometer, whose value can be estimated from images of the interference pattern of the expanded cloud).
Regarding claim 8, Pandev in view of Colussi further disclose wherein the atomic interferometer comprises an atomic point source interferometer using an atomic cloud as a measurement medium (Colussi see page 1, 3, wherein atom-based quantum technologies can be traced back to the Nobel prize winning advancements in the trapping and cooling of neutral atoms using laser light pioneered more than three decades ago).
Regarding claim 9, Pandev in view of Colussi further disclose wherein the atomic interferometer is disposed in a non-inertia reference frame and the target physical parameter comprises an angular rotation or linear acceleration of the non-inertia reference frame relative to an inertia reference frame (Colussi see page 2, wherein due to the required release of the cloud for free-space propagation in conventional CAIs, the interferometer operation times are tied to the free-fall distance, which can be on the order of seconds for a meters long atomic fountain apparatus,7 and therefore the sensitivity is constrained by the size of the device. Such systems also lack the robustness required to operate in harsh dynamical environments typical of real-world navigational applications, where vibration and rotation noise can quickly cause loss of contrast in the interference measurements.8 This problem can be resolved by confining the atoms such that the sensitivity of the device is not constrained by its size. see also page 3).
Regarding claim 10, Pandev in view of Colussi further disclose wherein the instrumentation precision of the atomic interferometer is associated with at least an imperfection in controlling a temperature of the atomic cloud (Colussi see page 1).
Regarding claim 11, Pandev in view of Colussi further disclose wherein the instrumentation precision of the atomic interferometer is associated with at least an imperfection in controlling an optical manipulation of the atomic cloud in a generation of the measurement dataset (Colussi see page 2-3).
Regarding claim 12, Pandev in view of Colussi further disclose wherein the imperfection comprises at least one of an optical wavelength imperfection, an optical pulse area imperfection, and an optical geometric alignment imperfection (Colussi see page 2-3).
Regarding claim 13, Pandev in view of Colussi further disclose wherein the at least one atomic interferogram image comprises a set of sine and cosine images generated from a set of measured atomic from the metrology system with a predefined set of phase offsets (Colussi see page 2 and fig.2(a)).
Regarding claim 14, Pandev in view of Colussi further disclose wherein the atomic interferometer is arranged in a Mach-Zehnder interferometry configuration (Colussi see page 1 and fig 1(c)).
Claim 19 is rejected under the same rationale as claims 6, 8-9.
Response to Arguments
Applicant’s amendment and arguments regarding the 35 U.S.C. 101 rejection, has been considered but are not persuasive.
(1) Applicant “disagrees with the Section 101 rejections. Soley for purposes of advancing the prosecution, Applicant has amended independent claims 1 and 16 to explicitly recite processing by the claimed neural network measurement data with low instrumental precision to obtain derivation of physical parameters having accuracy of a higher-precision calibration instrument. This feature was already included in the original independent claims and is further clarified by the amendment above. The claim amendment is supported by at least paragraphs [0023]-[0025], [0033]-[0034], [0077] of the published application. The specification explains that the claimed neural network is trained and configured to recognize and remove instrumental imprecision that can be recoverable, which reflects technical improvements achieved by recited claim elements. The claims as amended, additionally include explicit language about such technical improvements. Specifically, Applicant submits that such physical measurements and the processing of the measurements are inherently non-abstract, non-routine, and further integrate other claim elements into a practical application under Step 2A Prong II of the 2019 and subsequent USPTO Patent Eligibility Guidelines. The underlying application is practical in that it facilitates an effective calibration of physical measurement to achieve higher precision than dictated by instrumentation limitation by using historical correlation recognizable in a trained neural network.”
(1) Examiner respectfully disagrees.
First, Evaluation under Step 2A, Prong One (Abstract Idea) The claims remain directed to an abstract idea. While the claims recite a neural network processing physical measurements, the core action—"forward-propagating the measurement dataset through the neural network to generate the predicted value... having a precision higher than indicated by the instrumental precision"—is fundamentally a mathematical algorithm and a mental process. Analyzing information by applying algorithms and models to generate a predicted value, even a highly accurate one, falls squarely within the abstract idea category of mental processes (MPEP § 2106.04(a)(2)).
Second, Evaluation under Step 2A, Prong Two (Integration into a Practical Application) Applicant’s argument that the amendment constitutes a "technical improvement" integrating the concept into a practical application is unpersuasive.
Lack of Physical Application: To demonstrate a technical improvement or practical application, the claim must do more than simply output newly calculated data. While the specification may describe the neural network recognizing and removing instrumental imprecision, the claims themselves only recite gathering data, processing it through an algorithm, and outputting a newly calculated "predicted value". Claiming an algorithm that produces a more accurate or precise numerical result—without claiming a step that actually uses that result to control a machine, physically calibrate a physical metrology tool, or effectuate a physical transformation—does not integrate the abstract idea into a practical application.
Insignificant Extra-Solution Activity: Gathering the measurement dataset from the metrology system is mere data gathering (pre-solution activity). Outputting the predicted physical parameter is mere data output (post-solution activity). Under MPEP § 2106.05(g), neither of these steps is sufficient to integrate the mathematical calculation into a practical application.
Finaly, Evaluation under Step 2B (Significantly More) Because the claims are directed to an abstract idea and are not integrated into a practical application under Step 2A, they must be evaluated under Step 2B. The additional elements recited in the claims (e.g., "a memory for storing instructions and a processor for executing the instructions") amount to nothing more than generic computer components invoked at a high level of generality. Applying an abstract mathematical model to yield a more precise predicted value using a generic processor does not provide the requisite "inventive concept" to transform the nature of the claim into a patent-eligible application.
Therefore, the 101 is maintained.
Applicant is strongly advised to amend the claims to affirmatively recite how the higher-precision predicted value is subsequently used to physically control, adjust, or alter a downstream manufacturing or calibration process, thereby tying theses steps to a specific physical result.
Applicant’s arguments regarding the 35 U.S.C. 102 rejection have been considered but now are moot in view of new grounds of rejection necessitated by Applicant’s amendment.
Note:
Applicant argument regarding “Pandev deals with a distinctly different problem and discloses nothing that has to do with generating higher precision prediction from measurement made by low precision instrumentation.”
The new reference of Pandev, as explained in the above rejection, explains in paragraph [0043-0044, 0056, 0088] the system receives measurement data (e.g. measured spectra), wherein reference metrology system is a trusted measurement system that generates sufficiently accurate measurement results. However Pandev notes that these trusted reference systems are too slow to be used. This explicitly maps to the claim’s dynamic of gathering standard data from a lower-precision that is worse than the highly accurate reference tool used for calibration. In paragraph [0056], Pandev further discloses the trained measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra) collected by the measurement system. Pandev further explain in paragraph [0013], that the traditional training minimizes total measurement uncertainty which is an aggregation errors including precision errors, tool-to-tool matching errors. To improve upon this, In paragraph [0017, 0040-0041], Pandev’s system specifically regularizes the optimization process using domain knowledge, such as the probability distributions associated with measurement precision.
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 MAHER N ALGIBHAH whose telephone number is (571)272-0718. The examiner can normally be reached on Monday-Thursday.
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/MAHER N ALGIBHAH/Primary Examiner , Art Unit 2165