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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 11, 2026 has been entered.
Status of Claims
This action is in reply to the RCE and amendment filed on August 11, 2026. Claims 1-6 and 8-20 are pending, of which claims 1 and 9 have been amended, claim 7 has been canceled and claims 13-18 and 20 have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-6, 8-12, and 19 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Specifically, the limitations:
1) classifying the reference content, using a first artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the first artificial intelligence model, the reference content into the one or more reference objects (Claims 1 and 9);
2) extracting, using second image analysis, a plurality of submission objects from the submission content, based on the intent of the first user, wherein the extracting further comprises: classifying the submission content, using a second artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the second artificial intelligence model, the submission content into the one or more submission objects (claims 1 and 9) include NEW MATTER.
With regard to limitations 1) and 2), a review of the Specification at paragraphs 87 and 88 describes: the image analysis module 1522 may identify an object, a place, a person, text, an image, and the like, from an image through image recognition. Alternatively, or additionally, the image analysis module 1522 may classify an image and detect an object based on a result of the image recognition. In some embodiments, the image analysis module 1522 may classify an image using a classification model. The classification model may use an Al technology that may include a discriminative model and/or a generative model. The generative model may calculate a probability of deriving a result from the input data based on distributions of classes.
While the specification mentions AI technology as a classification model (See, e.g., ¶88) the only mention of the model’s use is directed to classifying. The specification is silent with regard to the AI technology comprising a bounding box and/or a class probability map. The only mention of these elements is in ¶89 in connection with the image analysis module 1522. In addition, there is no mention of using the AI model with regard to segmenting, which is mentioned in ¶91. Therefore, the specification does not provide a written description supporting these limitations.
As a result, the amended claims 1 and 9 contain subject matter which lacks adequate written description, and for at least these reasons, claims 1 and 9 are found to fail the written description requirement.
Claims 2-6, 8, 10-12, and 19 depend from a rejected base claim, and therefore also lack written description based on their dependency.
As a result, claims 1-6, 8-12, and 19 contain subject matter which lacks adequate written description, and for at least these reasons, claims 1-6, 8-12, and 19 are found to fail the written description requirement.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-6, 8-12, and 19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
A patent may be obtained for “any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof.” 35 U.S.C. § 101. The Supreme Court has held that this provision contains an important implicit exception: laws of nature, natural phenomena, and abstract ideas are not patentable. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014); Gottschalk v. Benson, 409 U.S. 63, 67 (1972) (“Phenomena of nature, though just discovered, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.”). Notwithstanding that a law of nature or an abstract idea, by itself, is not patentable, the application of these concepts may be deserving of patent protection. Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1293-94 (2012). In Mayo, the Court stated that “to transform an unpatentable law of nature into a patent eligible application of such a law, one must do more than simply state the law of nature while adding the words ‘apply it.” Mayo, 132 S. Ct. at 1294 (citation omitted).
In Alice, the Supreme Court reaffirmed the framework set forth previously in Mayo “for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of these concepts.” Alice, 134 S. Ct. at 2355. The first step in the analysis is to “determine whether the claims at issue are directed to one of those patent-ineligible concepts.” Id. If the claims are directed to a patent-ineligible concept, then the second step in the analysis is to consider the elements of the claims “individually and ‘as an ordered combination” to determine whether there are additional elements that “transform the nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo, 132 S. Ct. at 1298, 1297). In other words, the second step is to “search for an ‘inventive concept’-i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Id. (brackets in original) (quoting Mayo, 132 S. Ct. at 1294). The prohibition against patenting an abstract idea “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment or adding insignificant post-solution activity.” Bilski v. Kappos, 561 U.S. 593, 610-11 (2010) (citation and internal quotation marks omitted). The Court in Alice noted that “[s]imply appending conventional steps, specified at a high level of generality,’ was not ‘enough’ [in Mayo] to supply an ‘inventive concept.” Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1300, 1297, 1294).
Examiners must perform a Two-Part Analysis for Judicial Exceptions. In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter.
Claims 1, 9, and 19 are directed to method, a system, and computer readable medium (CRM). As such, the claimed invention falls into the broad categories of invention. However, even claims that fall within one of the four subject matter categories may nevertheless be ineligible if they encompass laws of nature, physical phenomena, or abstract ideas. See Diamond v. Chakrabarty, 447 U.S. at 309.
In Step 2A, it must be determined whether the claimed invention is ‘directed to’ a judicially recognized exception. According to the specification, the invention is directed to teaching. For example, the user 100 of the display device 1000 may be a teacher, and external users of the at least one external device 200 may be students. In such an example, to conduct the class, the teacher may display content related to the class in the display device 1000. Alternatively, or additionally, while the teacher conducts the class by displaying content related to the class in the display device 1000, the teacher may ask a question associated with the content displayed on the display device 1000 and/or gives a quiz. For example, the teacher may ask a question associated with the content displayed on the display device 1000 and/or utter speech asking a quiz. In such an example, after obtaining the speech of the teacher (e.g., user 100), the display device 1000 may transmit current content to the at least one external device 200. The at least one external device 200 may display the content transmitted from the display device 1000. Alternatively, or additionally, the at least one external device 200 may transmit (e.g., submit), to the display device 1000, content for submission (e.g., submission content) in response to obtaining input from the student. See, Specification ¶¶45-47.
Independent claim 1 recites the following (with emphasis):
A method of analyzing and evaluating content at a display device, comprising:
obtaining a speech input of a first user of the display device;
extracting, using first image analysis, one or more reference objects from reference content being displayed on the display device, the reference content comprising a plurality of objects wherein the extracting further comprises: classifying the reference content, using a first artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the first artificial intelligence model, the reference content into the one or more reference objects;
determining an intent of the first user indicating an operation to be performed by the display device, based on a result of interpreting the speech input and the one or more reference objects;
determining, based on the intent of the first user, reference data from the one or more reference objects;
obtaining submission content from an external device, the external device being connected to the display device;
extracting, using second image analysis, a plurality of submission objects from the submission content, based on the intent of the first user, wherein the extracting further comprises: classifying the submission content, using a second artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the second artificial intelligence model, the submission content into the one or more submission objects;
determining at least one target object to be compared with the reference data by selecting the at least one target object from among the plurality of submission objects;
evaluating the submission content by comparing the at least one target object with the reference data;
displaying, on the display device, information indicating a result of the evaluating of the submission content; and
sending, to the external device, the information indicating the result of the evaluating of the submission content, based on the result of the evaluating of the submission content being less than or equal to a predetermined threshold.
The underlined portions of claim 1 generally encompass the abstract idea, with substantially identical features in claims 9 and 19. Claims 2-6, 8 and 10-12 further define the abstract idea such as by defining the reference content, inputs, and/or extra solution activity. Under prong 2, the claimed invention encompasses an abstract idea in the form of organizing human activity and/or mental processes. The claims recite a means of organizing human activity because they are drawn to managing personal behavior of students and teacher during learning activities, such as evaluation of student responses in a classroom (i.e., teaching). Furthermore, the method can be performed in the mind of a human and/or with the aid of pencil and paper.
Selecting and providing questions to students, soliciting answers, and comparing the answers to a reference determine correctness and to evaluate the student as a means of teaching, training, and imparting knowledge is basic to the learning process. The method, system, and CRM in the instant application simply seek to automate this well-known activity using generic computers recited at a high level of generality, and, therefore, the claims are directed to the abstract concept sub-grouping of "managing personal behavior or relationships or interactions between people" including teaching and following rules or instructions, for example, an instructor conducting training of students by administering questions for the student to complete or solve, and evaluating the student’s answers.
In addition, the claims also recite a mental process directed to observations, evaluations, judgments, and opinions. But for the recitation of the recitation of display device, external device, processor, memory and computer readable medium storing instructions, nothing in the claimed method or operations precludes the recitations from practically being performed in the mind. For example, obtaining a speech input of a first user (e.g., a teacher asking a question); extracting, using first image analysis, one or more reference objects from reference content being displayed on the display device, the reference content comprising a plurality of objects, wherein the extracting further comprises: classifying the reference content, using a first artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the first artificial intelligence model, the reference content into the one or more reference objects (e.g., a teacher viewing a plurality of objects on a sheet of paper/screen and drawing a box around the objects to indicate which ones are desired); determining an intent of the first user indicating an operation to be performed by the display device, based on a result of interpreting the speech input and the one or more reference objects (e.g., a teaching thinking which group of answers correspond to the question); determining, based on the intent of the first user, reference data from the one or more reference objects (e.g., a teacher thinking which answers respond to the question); obtaining submission content from an external device, the external device being connected to the display device (e.g., a teacher receiving a student response); extracting, using second image analysis, a plurality of submission objects from the submission content, based on the intent of the first user, wherein the extracting further comprises: classifying the submission content, using a second artificial intelligence model comprising at least one of: a bounding box, or a class probability map; and segmenting, using the second artificial intelligence model, the submission content into the one or more submission objects (e.g., a teacher reading the student response and circling the answers corresponding to the question); determining at least one target object to be compared with the reference data by selecting the at least one target object from among the plurality of submission objects (e.g., a teacher thinking which circled answers are correct); evaluating the submission content by comparing the at least one target object with the reference data (e.g., a teacher thinking did the student answer match the correct answer); displaying information indicating a result of the evaluating of the submission content (e.g., a teacher writing down whether the student answer was correct); and sending the information indicating the result of the evaluating of the submission content, based on the result of the evaluating of the submission content being less than or equal to a predetermined threshold (a teacher providing the written result to the student) can be performed or formulated in the minds of a teacher, a tutor, or an instructor and student. If a claim, under its broadest reasonable interpretation, covers performance of recitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Therefore, under prong 2, the claimed invention encompasses an abstract idea in the form of mental processes and/or certain methods of organizing human activity.
Under prong 2, the instant claims do not integrate the abstract idea into a practical application. In other words, the claims do not (1) improve the functioning of a computer or other technology, (2) effect a particular treatment or prophylaxis for a disease or medical condition (3) are not applied with any particular machine, (4) do not effect a transformation of a particular article to a different state, and (5) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim, as a whole, is more than a drafting effort designed to monopolize the exception, the claims are directed to the judicially recognized exception of an abstract idea. See MPEP §§ 2106.05(a)-(c), (e)-(h).
While certain physical elements (i.e., elements that are not an abstract idea) are present in the claims, such features do not affect an improvement in any technology or technical field and are recited in generic (i.e., not particular) ways. Similarly, the abstract idea does not improve the functioning of these physical elements. In recent cases, the CAFC has made it clear that the term “practical application” means providing a technical solution to a technical problem in computers or networks per se. To be patent-eligible, the claimed invention must improve the computer as a computer or network as a network. Applicant’s invention does not meet these requirements. Applicant’s invention uses computers to process data to evaluate the user’s response. This does not improve the computer qua computer. Instead, Applicant’s invention uses generic computers and networks as a tool to implement the abstract idea. Mere automation of an abstract process is not a technical improvement to the system. As such, the claims are not eligible under Section 101.
Step 2B requires that if the claim encompasses a judicially recognized exception, it must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception. The additional elements or combination of elements other than the abstract idea per se amounts to no more than: a system having a computer and memory configured to perform the abstract idea. Applicant’s specification, for example, ¶¶39, 69, 309-313, recite off the shelf general purpose computing components such a display (e.g., a television, a monitor, and/or an electronic bulletin board), a microphone, a processor (e.g., central processing units (CPUs), microprocessors, graphic processing units (GPUs), application specific integrated circuits (ASIC), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), and the like, without being limited thereto.), and a memory (e.g., a flash memory, a hard disk, a multimedia card micro type memory, a card type memory (e.g., a secure digital (SD) memory card, an extreme digital (XD) memory card), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read-Only Memory (ROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Programmable Read-Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like, without being limited thereto) for storing instructions that can be executed by a computer. As a result, nothing in Applicant’s specification indicates the computer system performs anything other than well understood, routine, and conventional functions, such as receiving, storing, and processing. See, Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (ed. Cir. 2016) (“Nothing in the claims, understood in light of the [S]pecification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”); see also Alice, 573 US. at 224—26 (receiving, storing, sending information over networks insufficient to add an inventive concept); buySAFE, Inc. v. Google, Inc., 765 F.3d 1340, 1355 (ed. Cir, 2014) (That a computer receives and sends the information over a network-—with no further specification—is not even arguably inventive.”). At best, Applicant’s claimed subject matter simply uses generic processing circuitry to perform the abstract idea of converting input data from one form to another (e.g., student answers to evaluated results). As noted above, the use of a generic computer system does not alone transform an otherwise abstract idea into patent-eligible subject matter. As our reviewing court has observed, “after Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” DDR Holdings, 773 F.3d at 1256 (citing Alice, 573 U.S. at 223).
As a result, these additional elements amount to generic, well-understood and conventional computer components. As demonstrated by Berkheimer v. HP, such computer functions cannot save an otherwise ineligible claim under §101. In short, each step does no more than require a generic computer to perform generic computer functions. The claimed features relating receiving an input, displaying information, and sending information represent extra-solution activities are not particular and are recited at a high level of generality.
Considered as an ordered combination, only generic computer components are present. Viewed as a whole, the claims simply recite the concept of making judgments by a generic computer. The claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea using some unspecified, generic computer. Under relevant court precedents, that is not enough to transform an abstract idea into a patent-eligible invention.
As a result, claims 1-6, 8-12 and 19 are not patent eligible
Response to Arguments
Applicant's arguments filed August 11, 2026 have been fully considered.
The rejection of claims 1-6, 8-12, and 19 as directed to an abstract idea without significantly more is maintained for the reasons given above. The rejection has been updated in view of the amendments to the claims.
Applicant argues the claims limitations cannot be performed in the human mind and therefore are not directed to mental processes. In particular, Applicant argues “The human mind is not equipped to perform image analysis, such as classification and segmentation of objects using bounding boxes, or class probability maps. These are computational image processing operations that require specific algorithmic techniques and cannot practically be performed in the human mind or with pencil and paper.” The examiner respectfully disagrees. The human mind is very adept at performing image analysis using a human vision to observe and classify object. Humans are adapted at identifying boundaries, concentrations, and segment larger images into smaller ones. While Applicant argues the operations require “specific algorithmic techniques” no examples are given, and none are described in Applicant’s specification or recited in the claims.
With regard to the citation of SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010) the case was decided before Alice. The court found that the claims recited a specific GPS receiver that is a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices. This includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result) and integral to the claims. There is not such corresponding technology in Applicant’s claim.
With regard to the citation of SRIInt'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) the court found that the claims were directed to using a specific technique—using a plurality of network monitors that each analyze specific types of data, such as recited network packet data transfer commands, network packet data transfer errors, network packet data volume, network connection requests, network connection denials, error codes included in a network packet, network connection acknowledgements, on the network and integrating reports from the monitors—to solve a technological problem arising in computer networks: identifying hackers or potential intruders into the network. Here there is no recited specific technique rooted in the technology.
Applicant further argues “Regarding organizing human activity, in Ex Parte Vulcano, the PTAB held that an element is considered to be a certain method of organizing human activity only if it is a longstanding human practice. See Ex Parte Vulcano, Appeal 2018-009113 (PTAB 2018).” This citation is not understood for this point. First the opinion is non-precedential. Second, the decision did not hold a certain method of organizing human activity only if it is a longstanding human practice. And third the Board found in its 2A prong 1 analysis at pp. 8-9 of the opinion:
In this case, for example, claim 110 recites “determining, . . ., a current location of the mobile device” “while the mobile device is connected to a vehicle;” “predicting a plurality of likely destinations for the vehicle based on data from a plurality of different sources, each likely destination predicted to be a likely next destination of the vehicle based on the current location;” “generating, for a display . . . that displays at least a portion of different, separate informational display areas for each of the plurality of likely destinations, the informational display areas for each of the plurality of likely destinations” including “a portion of a map including a route from the current location to the likely destination;” and “outputting . . . for display.” Appeal Br. 14 (Claims App.). Standing alone, the act of predicting destinations based on different sources and a current location and displaying displays of the likely destinations, is an abstract idea of nontechnical human activity such as providing different maps that predict military actions, a longstanding practice. Thus, these limitations, under the broadest reasonable interpretation, are steps that recite a method of organizing human activity that manages personal behavior including following rules or instructions.
We note that claims 4, 7, and 27 recite similar limitations, we conclude that claims 1, 4, 7, and 27 recite a method of organizing human activity, and thus, a judicial exception, i.e., an abstract idea.
The examiner agrees with applicant's citation....just not with what the citation actually says; therefore, much like Vulcano there is an abstract idea recited in Applicant’s claims.
Applicant argues “The amended claims provide a specific technical improvement to automated content analysis systems with artificial intelligence. As such, the claims are similar to the claims at issue in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision). For example, in Ex Parte Desjardins the court found a method of training a machine learning model to be an improvement to computing technology. Specifically, in Ex Parte Desjardins the claimed invention was a method of training a machine learning model on a series of tasks. In Ex Parte Desjardins, the court determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks. Id. The court evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Id. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception. Id.” Applicant then alleges “the instant features of the claims recite improved machine learning models for performing content evaluation.” The examiner respectfully disagrees.
The court in Desjardins found there was a technical improvement to machine learning model itself through including the optimization and training of the model. No such improvements to AI are found in Applicant’s specification. In fact, the artificial intelligence models are broadly described in one sentence in the specification as an example. No specific improvement to the models themselves is disclosed. The Federal Circuit recently in Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025) that that the claims which included an iterative step of training the machine learning model (a neural network) not only recited an abstract concept, but stated “This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.” Recentive at p. 10. Much like Recentive (and unlike Desjardins) Applicant’s specification describes and claims use of well-established artificial intelligence used for its conventional purpose and recited in the claims at a high level of generality with no description or evidence of any improvement to the artificial intelligence.
In addition, the use of bounding boxes for object detection has been around for decades. The concept of bounding boxes in computer vision traces back to 1963 in Larry Roberts' foundational Ph.D. thesis at MIT, titled "Machine Perception of Three-Dimensional Solids," which analyzed 3D geometric polyhedra ("block world") using enclosing boundaries. However, the specific use of rectangular bounding boxes for modern data annotation and statistical object detection evolved gradually through digital image processing including key milestones in bounding box Evolution of: 1960s–1980s (Geometric & Block Models): Early computer vision used primitive 3D bounding volumes or edge tracking to fit simple geometric models. 2001 (Viola-Jones Framework): The pioneering Viola-Jones Face Detector used a fixed rectangular sub-window (detection window) sliding across images to spot faces, establishing the rectangular localization window. 2005–2008 (HOG and DPM): The Histogram of Oriented Gradients (2005) and Deformable Part-based Models (2008) utilized bounding windows and introduced explicit bounding box regression to fine-tune object boundaries. Mid-2000s to 2010s (Standardized Datasets): Large standardized visual datasets like PASCAL VOC (launched in 2005) formally adopted 2D rectangular bounding boxes as the definitive ground-truth annotation format for machine learning evaluation.1
Applicant points to paragraph 4 of specification as providing a technical problem of a moderator may experience difficulty when attempting to determine whether a student is using different content. In addition, the paragraph explains the checking and evaluating content of another participant may disturb processing flows of the meeting and/or class. However, this is not a technical problem but a teaching/discourse problem. For example, even without the use of computers a teacher may not understand if a student us looking at the correct page in a book. And disruption of flow again is a human problem not a technical one. Applicant argues that “the technical improvement lies in the integrated artificial intelligence pipeline recited by the claims as amended. Specifically, claim 1 recites using image analysis that employs bounding boxes, or class probability maps, combined with speech interpretation to determine user intent, and using that intent to guide both the extraction of reference objects and submission objects. This technical integration enables automatic evaluation of content received from external devices without user intervention.” However, the use of existing artificial intelligence for image analysis and use of bound boxes does not provide a technical improvement but merely uses existing technology for their intended suitable purposes to automate an abstract concept. The specific algorithm of an application of the AI to the data and/or operations of the image analysis are not claimed or disclosed and therefore do not represent a technical improvement of the system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed on the attached Notice of References Cited.
US 20200019628 describes a system that performs visual intent classification or visual intent detection or both on an image. Visual intent classification utilizes a trained machine learning model that classifies subjects in the image according to a classification taxonomy. The visual intent classification can be used as a pre-triggering mechanism to initiate further action in order to substantially save processing time. Example further actions include user scenarios, query formulation, user experience enhancement, and so forth. Visual intent detection utilizes a trained machine learning model to identify subjects in an image, place a bounding box around the image, and classify the subject according to the taxonomy. The trained machine learning model utilizes multiple feature detectors, multi-layer predictions, multilabel classifiers, and bounding box regression.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew Bodendorf whose telephone number is (571) 272-6152. The examiner can normally be reached M-F 9AM-5PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW BODENDORF/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
1 A brief history of Object Detection found at https://kanishkmunot.hashnode.dev/introduction-to-object-detection.
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