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
This Final office action is in response to the application filed on April 18, 2023, the amendments to the claims filed on April 17, 2024, the Request for Continued Examination filed on September 18, 2024, the amendments to the claims February 4, 2025, the Request for Continued Examination filed on May 29, 2025, and the amendments to the claims filed on October 13, 2025.
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 21-34 and 36-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 21-34 and 36-41 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method Claim 21 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 31 and product Claim 41. Claim 21 recites the limitations of obtaining a first image from a database; extracting first machine learning features from the first image using the first convolutional neural network; obtaining a second image from a client device; extracting second machine learning features from the second image using the first convolutional neural network or a second convolutional neural network; identifying one or more attributes of the second image using the first convolutional neural network or the second convolutional neural network; calculating a comparison metric based on the extracted first machine learning features of the first image and the extracted second machine learning features of the second image by inputting, into a classifier neural network, the extracted first machine learning features of the first image and the extracted second machine learning features of the second image; and confirming the identified one or more attributes of the second image based on the comparison metric satisfying a matching criterion.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes/mathematical processes. Analyzing and identifying images by confirming identified attributes of an image based on a comparison metric satisfying a matching criterion recites concepts performed in the human mind/mathematical relationships. Particularly, in light of the specification (especially paragraph [099]), the extracting steps involve extracting features from an image that may include a logo, a shape of headlights, a shape of taillights of the vehicle, as examples. The ability for a human to recognize and identify (broadest reasonable interpretation of “extract”) vehicle brand logos, headlight, and taillight shapes, and then compare those shapes to similar shapes in other images is ubiquitous and certainly able to be practically performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as concepts performed in the human mind/mathematical relationships, then it falls within the “Mental Processes”/ “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 31 and 41 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a database and client device as hardware. The claims also recite the additional elements of training a first convolutional neural network that includes classified images with metadata that is used for preliminary search to reduce the computing resources used for machine learning; and first and second convolutional and classifier neural networks and a classifier neural network in Claims 21, 31, and 41. The training of a convolutional neural network and the three neural networks themselves are described at a high level of generality. They are involved in automating a manual process of comparing images and finding similar features. The claims, nor the specification include descriptions of this training or these neural networks as improvements to technology or a technical field, rather, they are being used as a tool to improve the recited judicial exception (in the predictable way that training and using neural networks to classify data is intended for). (see MPEP 2105.06(f)) The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, claims 21, 31, and 41 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0042, 0053, 0057] about implementation using general purpose or special purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. In addition, performing the judicial exception steps using neural networks merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 21, 31, and 41 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 22-30, 32-34, and 36-40 further define the abstract idea that is present in their respective independent claims 21, 31, and 41 and thus correspond to Mental Processes/Mathematical Concepts and hence are abstract for the reasons presented above. Claims 22, 23, 28, 32, and 38 further set forth details of the neural networks generally, but do not include any details about how the details of the neural networks are integrated into the abstract idea. For example, the details of claim 28 are one of many options described in paragraph [042] of the specification, but there is no discussion of how these details are critical to solving a technical problem or improving a technical field. In other words, there is no nexus between the neural network details claimed and the integration of the abstract idea. Claims 24, 25, 29, 34, and 39 further define the use and calculations of the comparison metric (it is noted that elements of claim 25 are also included in independent claim 41); Claims 26 and 36 further define the type of data in the image and search information; Claims 27 and 37 further define the metadata; Claims 30, 33, and 40 further define extracted machine learning features. Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 22-30, 32-34, and 36-40 are directed to an abstract idea. Thus, the claims 21-34 and 36-41 are not patent-eligible.
Response to Arguments
Applicant's arguments filed October 13, 2025 have been fully considered but they are not persuasive.
Applicant’s arguments regarding the 35 USC 101 rejection of record (Remarks, pages 9-13) are acknowledged, however they are not persuasive. Specifically, applicant’s arguments that the claims do not fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (Remarks, pages 9-10) are moot in view of the amendments to the claims. Applicant further argues that the claim limitations do not fall within Mental Processes citing the August 4, 2025 memo, “Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind…Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping[.]” (Remarks, pages 10-11, emphasis added by applicant), however these arguments are not commensurate with the scope of the claims. In the instant case the claimed machine learning is being used as a tool to identify a vehicle from a picture, something that can be practically performed in the human mind. Relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” OIP, 788 F.3d at 1363 (citing Alice, 134 S.Ct. at 2359). Therefore, the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.
Applicant’s arguments that, “claim 21 as a whole integrates into a practical application because it is directed to improvements [in] machine learning by minimizing computing resources used and speeding up [the] process for identifying images” (Remarks, pages 11-13), are acknowledged, however they are not persuasive as they are not commensurate with the scope of the specification. Specifically, paragraph [0132] of the specification recites:
“In step 1610,server system 105 may determine whether the
associated metadata includes a word of interest based on the preliminary search. If the metadata does not include a word of interest (step 1610: No), server system 105 may continue to step 1612, classifying the image as unlabeled, and/or discarding it. If instead the metadata includes a word of interest (step 1610: Yes), server system 105 may continue to step 1614 and perform a second search for attributes in metadata. A "word of interest" may include any vehicle-related word such as "exterior,""interior,""car,""vehicle,""front view,""side view,""rear view,""Ford,""Honda,""2006,""2007,""XL,""4L,""horsepower," or the like. Such staggered search for metadata improves computer functionality by minimizing the resources that are used to classify images creating a stratified approach that quickly identifies images that are not of interest and then devoting more resources to images that overcome an initial threshold. Indeed, because the determination in step 1610 may be performed with specialized computers, such as FPGAs, specifically programmed to perform the keyword search in metadata, process 1600 may improve the overall speed of the server system 105.”
It is clear from paragraph [0132] that the improvement to the computer functionality is not in “machine learning by minimizing computing resources used and speeding up [the] process for identifying images” as applicant argues but rather the improvement is in minimizing the amount of resources used to classify images and identify images that are not of interest and shifting those resources to other images. Therefore, in the claimed invention, the computer has not been improved. The non-technological process that the software is performing may have been improved but, according to Alice, improving the process without any technological innovation is not statutory. The computer still operates according to its known and standard capabilities. A reduction of load on the computer does not bring about an improvement to the computer, it merely offers resources to other processes that are running on the computer.
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 LINDSAY M MAGUIRE whose telephone number is (571)272-6039. The examiner can normally be reached Monday to Friday 8:30 to 5:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached on (571) 270-3614. 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.
Lindsay Maguire
10/24/25
/LINDSAY M MAGUIRE/Primary Examiner, Art Unit 3619