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 .
Information Disclosure Statement
The information disclosure statement submitted has been considered by the Examiner and made of record in the application file.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 41-60 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 119978258. Although the claims at issue are not identical, they are not patentably distinct from each other because it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter of the broader instant recitation.
Instant Application
Pat. # 11978258
Comment
41. A processor, comprising: one or more circuits to use one or more neural networks to: generate an encoding of input data; and
1. A processor, comprising: one or more circuits to use one or more neural networks to infer a classification characteristic
Substantially similar. Input data for neural network is encoded, transformed, etc.
use the generated encoding to detect whether the input data is out-of-distribution based, at least in part, on two or more individually weighted loss functions.
and a plurality of additional characteristics about input information based, at least in part, on the one or more neural networks being trained to infer each of the plurality of additional characteristics about the input information using a plurality of individually weighted loss functions corresponding to the plurality of additional characteristics.
Substantially similar. Except for out-of-distribution. See 103 rejection below and Ahmed prior art below.
Claims 42-47 are obvious over claims 2-7 of Pat. # 11978258.
Instant Application
Pat. # 11978258
Comment
48. A system, comprising: one or more processors to use one or more neural networks to: generate an encoding of input data;
8. A system, comprising: one or more processors to calculate parameters corresponding to one or more neural networks, at least in part, by: training the one or more neural networks to infer a classification characteristic
Substantially similar. Input data for neural network is encoded, transformed, etc.
and use the generated encoding to detect whether the input data is out-of-distribution based, at least in part, on two or more individually weighted loss functions.
and a plurality of additional characteristics about input information based, at least in part on the one or more neural networks being trained to infer each of the plurality of additional characteristics about the input information using a plurality of individually weighted loss functions corresponding to the plurality of additional characteristics; and one or more memories to store the parameters.
Substantially similar. Except for out-of-distribution. See 103 rejection below and Ahmed prior art below.
Claims 49-54 are obvious over claims 9-15 of Pat. # 11978258.
Instant Application
Pat. # 11978258
Comment
55. A method comprising: using one or more neural networks to: generate an encoding of input data;
23. A method comprising: training one or more neural networks to infer a classification characteristic about input information
Substantially similar. Input data for neural network is encoded, transformed, etc.
and use the generated encoding to detect whether the input data is out-of-distribution based, at least in part, on two or more individually weighted loss functions.
based, at least in part, on training the one or more neural networks to infer each of a plurality of additional characteristics about the input information using a plurality of individually weighted loss functions corresponding to the plurality of additional characteristics.
Substantially similar. Except for out-of-distribution. See 103 rejection below and Ahmed prior art below.
Claims 56-60 are obvious over claims 24-28 of Pat. # 11978258.
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 41-60 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The rejection follows the two-step framework set forth in Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208 (2014), and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012), as articulated in the 2019 Revised Patent Subject Matter Eligibility Guidance and MPEP 2106, and as further informed by the July 2024 Update: Subject Matter Eligibility Guidance for AI-Assisted Inventions and accompanying Examples 47–49. Claim 41 is taken as representative; claims 48 and 55 recite substantially identical limitations in system and method form and are rejected on the same basis, with differences noted below.
Step 1 — Statutory Category (MPEP 2106.03)
Claim 41 is directed to one or more processors comprising one or more circuits, which falls within the statutory category of a machine. Claim 48 is directed to a system comprising one or more processors (machine). Claim 55 is directed to a method (process). Accordingly, each claim falls within a statutory category, and the analysis proceeds to Step 2A.
Step 2A, Prong One — The Claims Recite a Judicial Exception (MPEP 2106.04(a))
Claim 41 recites the following limitations that fall within the enumerated groupings of abstract ideas:
(a) “generate an encoding of input data” — under its broadest reasonable interpretation, generating an encoding of input data is a mathematical operation that transforms one set of numerical values into another set of numerical values (a feature vector or embedding). This limitation therefore recites a mathematical concept (mathematical calculations and relationships). MPEP 2106.04(a)(2)(I). To the extent the encoding is construed more generally as characterizing or summarizing data, the limitation alternatively recites a mental process, as a person could observe data and form a simplified representation or characterization of it in the mind or with pen and paper. MPEP 2106.04(a)(2)(III).
(b) “use the generated encoding to detect whether the input data is out-of-distribution based, at least in part, on a first individually weighted loss function and a combination of two or more second individually weighted loss functions” — this limitation recites a mathematical concept. A loss function is a mathematical formula; applying an individual weight to each of a plurality of loss functions and combining the weighted loss functions is a weighted mathematical combination (e.g., L = w₀L₀ + Σ wᵢLᵢ); and determining whether data is “out-of-distribution” based on such a combination amounts to comparing a computed mathematical value or score against a distribution or threshold. MPEP 2106.04(a)(2)(I); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018) (claims directed to “selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results” are abstract). This limitation additionally recites a mental process: evaluating whether an observed data sample is anomalous or unlike previously seen samples is an observation, evaluation, and judgment that can be practically performed in the human mind, at least for simple inputs. See July 2024 Example 47, claim 2 (steps of “detecting one or more anomalies” and “analyzing the one or more detected anomalies” recite mental processes); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353–54 (Fed. Cir. 2016) (collecting information and “analyzing information by steps people go through in their minds, or by mathematical algorithms” are abstract).
The recitation that the mathematical concepts and mental processes are performed “based, at least in part” on the identified loss functions does not remove the limitations from the abstract-idea groupings; the claim merely describes the abstract evaluation at a high level of generality without reciting any additional non-abstract acts. Accordingly, the claims recite an abstract idea, and the analysis proceeds to Prong Two.
Step 2A, Prong Two — The Judicial Exception Is Not Integrated Into a Practical Application (MPEP 2106.04(d))
The claims recite the following additional elements beyond the judicial exception: “one or more processors” / “a system” (claims 41, 48); “one or more circuits” (claim 41); and “one or more neural networks” used to perform the generating and detecting (claims 41, 48, 55). Claim 55 recites no hardware at all beyond “using one or more neural networks.”
The processors, circuits, and system are recited at a high level of generality and amount to no more than generic computer components used as tools to perform the abstract idea. Reciting a judicial exception with instructions to “apply it” on a computer is not a practical application. MPEP 2106.05(f); Alice, 573 U.S. at 223–26. Unlike claim 1 of July 2024 Example 47 — which recited a specific application-specific integrated circuit structure comprising an array of neurons, registers, processing elements, and synaptic circuits with memory storing connection weights — the instant claims recite no particular structure, architecture, or arrangement of circuits. The bare recitation of “one or more circuits to use one or more neural networks” does not confine the claim to any particular machine. MPEP 2106.05(b).
The recitation of “one or more neural networks” likewise does not integrate the abstract idea into a practical application. The neural networks are invoked merely as a tool to perform the mathematical operations of encoding and scoring, and are claimed at the highest level of generality (any number of networks, of any type, trained in any manner). The Federal Circuit has held that claims that “do no more than apply established methods of machine learning” to a particular task or environment, without reciting any asserted improvement in the machine-learning technique itself, are directed to an abstract idea. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). Although the specification describes a particular multi-task self-supervised training framework in which weights are automatically selected for each of a plurality of transformation-based auxiliary tasks using in-distribution training data only (see, e.g., published ¶¶ describing geometric and non-geometric transformations and automated transformation-weight selection), these asserted improvements are not recited in claims 41, 48, or 55. The claims recite only that detection is “based, at least in part,” on individually weighted loss functions — i.e., the result of using weighted losses — not any particular way of selecting the weights, any particular auxiliary tasks or transformations, or any particular network architecture. Features not claimed cannot integrate the exception into a practical application. MPEP 2106.04(d) (the improvement must be reflected in the claim).
Further, the claims do not apply or use the result of the detection in any way. The claims end with detecting whether the input data is out-of-distribution; no limitation recites excluding the input from inference, generating an alert, controlling a machine, or taking any other action in response to the detection. Compare July 2024 Example 47, claim 3 (eligible where the claimed method used the anomaly determination to drop malicious network packets and block traffic in real time, thereby improving network security), with Example 47, claim 2 (ineligible where the claim merely detected and analyzed anomalies and output results). The instant claims parallel claim 2, not claim 3. The generation of an “encoding” is itself part of the abstract mathematical process and, in any event, constitutes mere data gathering or intermediate data manipulation that is insignificant extra-solution activity. MPEP 2106.05(g); Elec. Power Grp., 830 F.3d at 1354–55. Limiting the abstract idea to the field of neural networks or “input data” generally is a field-of-use limitation that does not confer eligibility. MPEP 2106.05(h).
Accordingly, the additional elements, considered individually and in combination, do not integrate the recited abstract idea into a practical application. The claims are directed to the abstract idea, and the analysis proceeds to Step 2B.
Step 2B — The Claims Do Not Recite Significantly More (MPEP 2106.05)
As discussed above, the additional elements amount to (i) generic computing hardware performing generic computing functions and (ii) the use of neural networks as a generic tool for mathematical analysis. Generic processors and circuits performing encoding, scoring, and detection functions are well-understood, routine, and conventional. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018); MPEP 2106.05(d)(II) (receiving or transmitting data, performing repetitive calculations, and storing/retrieving information are recognized as well-understood, routine, and conventional functions of a generic computer). The specification itself evidences the conventionality of the additional elements: it describes implementing the embodiments using commercially available, general-purpose deep-learning frameworks (e.g., PyTorch, TensorFlow, Caffe) executing on conventional CPUs/GPUs and data-center hardware, and acknowledges that training neural networks with weighted combinations of loss terms, including self-supervised auxiliary objectives, was known. See MPEP 2106.05(d)(I) (an express statement in the specification demonstrating conventionality may support a Step 2B finding). Viewed as an ordered combination, the claims add nothing beyond performing the abstract mathematical evaluation on generic hardware; the ordering (encode, then score) is the conventional sequence inherent in the abstract idea itself. Alice, 573 U.S. at 225.
Therefore, claims 41, 48, and 55 do not amount to significantly more than the abstract idea itself and are not patent eligible.
Allowable Subject Matter
Claims would be allowable over prior art, however, other outstanding issues remain.
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 response to this Office Action should be faxed to (571) 273-8300 or mailed to:
Commissioner for Patents
P.O. Box 1450
Alexandria, VA 22313-1450
Hand-delivered responses should be brought to
Customer Service Window
Randolph Building
401 Dulany Street
Alexandria, VA 22314
Any inquiry concerning this communication or earlier communications from the
Examiner should be directed to Fayyaz Alam whose telephone number is (571) 270-1102. The Examiner can normally be reached on Monday-Friday from 9:30am to 7:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Jeanette Parker can be reached on (571) 270-3647. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free) or 703-305-3028.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist/customer service whose telephone number is (571) 272-2600.
Fayyaz Alam
August 18, 2026
/FAYYAZ ALAM/
Primary Examiner, Art Unit 2646
Prosecution Insights