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 .
This is Non-Final Office Action in response to application filed on May 21, 2025 in which claims 1-20 are presented for examination.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite analyze media content associated with at least one first object; identify the at least one first object based on analyzing the media content and by utilizing at least one computer vision technique utilized by the at least one machine learning model; determine whether the at least one first object matches at least one second object corresponding to an asset of a plurality of assets associated with a profile; determine that the at least one first object is the at least one second object; retrieve metadata associated with the at least one second object; and classify the at least one first object as a new asset for inclusion in the plurality of assets associated with the profile. This judicial exception is not integrated into a practical application because the steps can be performed manually in human mind. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim here merely uses the processor as a tool to perform the otherwise mental processes. See October Update at Section I(C)(ii). Thus, the limitations recite concepts that fall into the “mental process” grouping of abstract ideas.
ANALYSIS under Revised Guidance of 2019 PEG:
Statutory Category:
The claims 1-20 are directed to one of the four statutory category (claims 1-13 a system or a machine, 14-19 a method or a process and claim 20 a non-transitory computer-readable medium).
Step 2A – Prong 1: Judicial Exception Recited?
The claim 1 recites the limitations of analyzing media content, identifying an object, comparing it to known assets, retrieving metadata and classifying the first object as a new asset. These steps can be characterized as abstract idea mantal processes (observation, recognition, comparison and classification). The claim also recites utilizing computer vision technique utilized by the machine learning model. However, using the large language model is applying it and is not significantly more than a mental process per Recentive Analytics V. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Thus, the claim recite abstract idea under Step 2A, Prong 1.
Step 2A – Prong 2: integrated into a practical application?
The claim 1 recites limitations or elements: generic “machine learning model”, generic “computer vision technique” that are recited at a high level of generality. The claim recites no specific improvement to model architecture, hardware efficiency or technical performance. They appear to use machine learning model or computer vision as tools to implement the decision process, without improvement to computer functioning, improvement to machine learning architecture or solve a technical problem in computer vision. Instead, the claim resembles result-oriented functional language (analyze, identify, compare and classify). Thus, the claim does not integrate into a practical application.
Step 2B: The claim recites potential additional elements such as memory, processor, machine learning model and computer vision technique. However, these are recited as generic computing components and that are well-understood, routine and conventional and therefor do not provide an inventive concept. Moreover, the claim does not recite a novel training technique, specific feature extraction method, or hardware acceleration. There is nothing here appears to improve computer performance, solve a specific technical problem in networking, storage, and introduce a novel data structure or algorithm. Instead, it is mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Accordingly, these recitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Dependent claim 2 recites “scan the at least one first object or capture the media content associated with the at least one first object…” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 3 recites “utilize the at least one machine learning model to perform feature extraction on the media content, conduct object detection, conduct image captioning, conduct image classification, conduct text classification, conduct audio classification, conduct video classification” abstract idea under step 2A(ii) and “determine whether the at least one first object matches the at least one second object” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 4 recites “determine whether an anomaly exists for the at least one first object by comparing the media content to the metadata…” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 5 recites “input device configured to mark the at least one first object with a first mark to facilitate identification of the at least one first object…” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 6 recites “convert the media content, the metadata, or a combination thereof into a token, a series of tokens” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 7 recites “train the at least one machine learning model by utilizing training data comprising training content, object specifications, manufacturer specifications, feedback relating to an accuracy of at least one determination or prediction made by the at least one machine learning model” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 8 recites “update the metadata based on information obtained from the media content” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 9 recites “automatically generate content describing the at least one first object by utilizing image captioning” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 10 recites “display the metadata associated with the at least one second object on a user interface of a device” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 11 recites “wherein the metadata comprises a size of the at least one first object, a shape of the at least one first object, a dimension of the at least one first object, a life expectancy of the at least one first object, an identification of an alternate object” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 12 recites “capture the media content associated with the at least one first object by utilizing a camera, a sensor, a computing device” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 13 recites “organize the plurality of assets within the profile and according to at least one criteria” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Claim 14 and 20 are rejected due to the similar analysis of claim 1. Claims 15-19 are similar analysis of claims 2-13 and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element in claims 15-19 represent a further mental process step. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer component, then it falls within the “mental processes” group of abstract ideas. Each additional step is considered an abstract idea (mental process step) and does not integrate the judicial exception into a practical application. An additional abstract idea (mental process step) is not sufficient to amount to significantly more than the judicial exception. Therefore, claims 1-20 are not patent eligible.
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.
Claims 1-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20240256592 A1) in view of Tal et al. (US 20230421732 A1).
Regarding claim 1 and similar claim 20, O’Neill discloses a system (system of Fig.1, O’Neill), comprising:
a memory that stores instructions (computing device/platform 120 includes memory, Fig.1, O’Neill); and
a processor (computing device/platform 120 includes processor, Fig.1, and ¶[0019], O’Neill) that executes the instructions to configure the processor to:
analyze, by utilizing at least one machine learning model (¶[0123]-[0125]), media content associated with at least one first object (¶[0097]-[0099] and [0141]-[0143], O’Neill, i.e., analyzing media content to identify specific content elements e.g., products, objects, scene settings or visual emotions, etc.);
identify the at least one first object based on analyzing the media content and by utilizing at least one computer vision technique utilized by the at least one machine learning model (¶[0137], [0139] and [0141], O’Neill, i.e. using machine learning model for content analysis);
determine whether the at least one first object matches at least one second object corresponding to an asset of a plurality of assets associated with a profile (¶[0118]-[0120], [0137] and [0139], O’Neill, i.e., determining/matching the analyzed specific content segment/current hash “first object” with stored video/stored hash “second object” in the database);
determine, based on the at least one first object being determined to match the at least one second object, that the at least one first object is the at least one second object (¶[0118]-[0120], [0137] and [0139], O’Neill);
retrieve, based on the at least one first object matching the at least one second object, metadata associated with the at least one second object (¶[0141], [0191] and [0196], O’Neill, i.e., retrieving and generate report/metadata associated with media content object being used); and
classify the at least one first object in the plurality of assets associated with the profile (¶[0139] and [0279]-[0280], O’Neill).
O’Neill, however, does not explicitly disclose classify, based on the at least one first object being determined to not match the at least one second object, the at least one first object as a new asset for inclusion in the plurality of assets.
Tal discloses classify, based on the at least one first object being determined to not match the at least one second object (¶[0102]-[0103], Tal), the at least one first object as a new asset for inclusion in the plurality of assets (¶[0102]-[0103] and [0156], Tal).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having O’Neill and Tal before them to classify object if the system does not recognize an object as an existing item, it classifies the object as new and adds it to its database, thereby reducing redundant processing and improving overall system efficiency (¶[0103], Tal). it would have been obvious to one skilled in the art to substitute one known method for another to achieve the efficient system.
Regarding claim 2, O’Neill/Tal combination discloses scan the at least one first object or capture the media content associated with the at least one first object (¶[0047]-[0049], Tal), and wherein the processor is further configured to present a holographic label on the at least one first object (¶[0047]-[0049] and [0065], Tal), in proximity to the at least one first object (¶[0148], Tal), or a combination thereof, wherein the processor is configured to enable, if the at least one first object matches the at least one second object, the metadata associated with the at least one second object to be presented in response to an interaction with the holographic label (¶[0047]-[0049] and [0148], Tal).
Regarding claim 3, O’Neill/Tal combination discloses utilize the at least one machine learning model to perform feature extraction on the media content (¶[0087]-[0088], O’Neill), conduct object detection (¶[0087]-[0088] and [0118], O’Neill), conduct image captioning, conduct image classification, conduct text classification, conduct audio classification, conduct video classification (¶[0143] and [0168], O’Neill), or a combination thereof, to: identify the at least one first object (¶[0103],[0143] and [0168], O’Neill); and determine whether the at least one first object matches the at least one second object (¶[0103],[0209] and [0215]-[0216], O’Neill).
Regarding claim 4, O’Neill/Tal combination discloses determine whether an anomaly exists for the at least one first object by comparing the media content to the metadata (¶[0102]-[0103] and [0156], Tal), prior media content taken of the at least one first object, activity performed by or on the at least one first object, behavior conducted by or on the at least one first object (¶[0102]-[0103] and [0156], Tal), at least one manufacturer specification associated with the at least one first object, at least one specification specified by an owner of the at least one first object, or a combination thereof (¶[0102]-[0103] and [0156], Tal).
Regarding claim 5, O’Neill/Tal combination discloses identify the at least one first object based on the first mark (¶[0087]-[0088], O’Neill); and qualify the first mark to be associated with that least one first object by generating a unique identifier to associate the first mark with the at least one first object (¶[0087]-[0089] and [0118], O’Neill).
Regarding claim 6, O’Neill/Tal combination discloses convert the media content, the metadata, or a combination thereof into a token, a series of tokens, or a combination thereof (¶[0087]-[0089] and [0118], O’Neill).
Regarding claim 7, O’Neill/Tal combination discloses train the at least one machine learning model by utilizing training data comprising training content (¶[0135], O’Neill), object specifications, manufacturer specifications, feedback relating to an accuracy of at least one determination or prediction made by the at least one machine learning model (¶[0135], O’Neill), or a combination thereof (¶[0087]-[0089] and [0118], O’Neill).
Regarding claim 8, O’Neill/Tal combination discloses update the metadata based on information obtained from the media content (¶[0135] and [0598], O’Neill).
Regarding claim 9, O’Neill/Tal combination discloses automatically generate content describing the at least one first object by utilizing image captioning (¶[0147] and [0434], O’Neill).
Regarding claim 10, O’Neill/Tal combination discloses display the metadata associated with the at least one second object on a user interface of a device (Fig. 2A, O’Neill).
Regarding claim 11, O’Neill/Tal combination discloses wherein the metadata comprises a size of the at least one first object (¶[0011] and [0134]-[0135], O’Neill), a shape of the at least one first object, a dimension of the at least one first object, an life expectancy of the at least one first object (¶[0011] and [0134]-[0135], O’Neill), an identification of an alternate object that serves as a substitute for the at least one first object (¶[0071]-[0072], Tal), repair information for the at least one first object (¶[0202]-[0204], Tal), warranty information for the at least one first object, service information for the at least one first object, at least one recommendation associated with the at least one first object, or a combination thereof (¶[0202]-[0204], Tal).
Regarding claim 12, O’Neill/Tal combination discloses capture the media content associated with the at least one first object by utilizing a camera, a sensor, a computing device, or a combination thereof (¶[0048]-[0049], Tal).
Regarding claim 13, O’Neill/Tal combination discloses wherein the processor is configured to organize the plurality of assets within the profile and according to at least one criteria (¶[0094]-[0095], Tal).
Regarding claim 14, O’Neill discloses a method, comprising:
analyzing, by utilizing instructions from a memory that are executed by a processor and by utilizing at least one machine learning model (¶[0123]-[0125]), media content associated with at least one first object (¶[0097]-[0099] and [0141]-[0143], O’Neill, i.e., analyzing media content to identify specific content elements e.g., products, objects, scene settings or visual emotions, etc.);
identifying the at least one first object based on analyzing the media content and by utilizing at least one computer vision technique utilized by the at least one machine learning model (¶[0137], [0139] and [0141], O’Neill, i.e. using machine learning model for content analysis);
determining whether the at least one first object matches at least one second object corresponding to an asset of a plurality of assets associated with a profile (¶[0118]-[0120], [0137] and [0139], O’Neill, i.e., determining/matching the analyzed specific content segment/current hash “first object” with stored video/stored hash “second object” in the database);
determining, based on the at least one first object matching the at least one second object, that the at least one first object is the at least one second object (¶[0118]-[0120], [0137] and [0139], O’Neill);
obtaining, based on the at least one first object matching the at least one second object, metadata associated with the at least one second object (¶[0141], [0191] and [0196], O’Neill, i.e., retrieving and generate report/metadata associated with media content object being used); and
classify the at least one first object in the plurality of assets associated with the profile (¶[0139] and [0279]-[0280], O’Neill).
O’Neill, however, does not explicitly disclose classify, based on the at least one first object being determined to not match the at least one second object, the at least one first object as a new asset for inclusion in the plurality of assets.
Tal discloses classify, based on the at least one first object being determined to not match the at least one second object (¶[0102]-[0103], Tal), the at least one first object as a new asset for inclusion in the plurality of assets (¶[0102]-[0103] and [0156], Tal).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having O’Neill and Tal before them to classify object if the system does not recognize an object as an existing item, it classifies the object as new and adds it to its database, thereby reducing redundant processing and improving overall system efficiency (¶[0103], Tal). it would have been obvious to one skilled in the art to substitute one known method for another to achieve the efficient system.
Regarding claim 15, O’Neill/Tal combination discloses determining a condition associated with the at least one first object based on utilizing the at least one machine learning model to analyze the media content associated with the at least one first object (¶[0097]-[0099] and [0141]-[0143], O’Neill).
Regarding claim 16, O’Neill/Tal combination discloses generating the metadata based on analyzing the media content, based on a manual input by a user, based on a signal from at least one other object, or a combination thereof (¶[0097]-[0099] and [0141]-[0143], O’Neill).
Regarding claim 19, O’Neill/Tal combination discloses determining whether the at least one first object needs to be repaired, replaced, modified, maintained, or a combination thereof, based on the analyzing of the media content (¶[0139] and [0143], O’Neill).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20240256592 A1) in view of Tal et al. (US 20230421732 A1) and further in view of Moore (US 6347319 B1).
Regarding claim 17, O’Neill/Tal combination discloses all of the claimed limitations as discussed above, except marking the at least one first object by utilizing an infrared pen, an ultraviolet pen, or a combination thereof. Moore discloses method of obtaining listings of information from databases, wherein the listing is provided to a user who marks at least some of the listed objects by utilizing a pen or other marking device to form a visible mark on at least some of the listed objects (col.3, lines 35-59, Moore). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified O’Neill and Moore before them to utilize the pen or other marking device to form a visible mark on at least some of the listed objects. The motivation for using a pen or other marking device is to create a visible mark that enables easy identification, tracking, or differentiation of objects.
Regarding claim 18, O’Neill/Tal/Moore combination discloses utilizing semantic segmentation to perform the marking of the at least one first object (¶[0162]-[0163] and [0170], O’Neill).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Robson et al. (US 20100106672 A1) disclose automated metadata generation of learning and knowledge objects.
Brown et al. (US 20110131247 A1) disclose semantic management of enterprise resources.
Moore et al. (US 20190362154 A1) disclose object detection from visual search queries.
Tofighbakhsh et al. (US 20200413113 A1) disclose video object tagging based on machine learning.
Cohen-Tidhar et al. (US 20210334547 A1) disclose system, device, and method for generating and utilizing content-aware metadata.
Sahasi et al. (US 20230004833 A1) disclose methods, systems, and apparatuses for model selection and content recommendations.
Deutsch et al. (US 20250139057 A1) disclose systems and methods for generation of metadata by an artificial intelligence model based on context.
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/HANH B THAI/Primary Examiner, Art Unit 2163
March 19, 2026