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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record on file.
Claim Objections
Claims 5, 9-14, and 16-20 are objected to because of the following informalities:
Claim 5, line 8, the reference “the identification results” is lack of proper antecedent support since there is no prior first instantiation of “identification results” to have this instance of antecedent referencing, the claim is suggested to be amended from “the identification results” to “[[the ]]identification results”. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 9, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 10, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 11, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 12, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 12, line 6, the reference “the identification results” is lack of proper antecedent support since there is no prior first instantiation of “identification results” to have this instance of antecedent referencing, the claim is suggested to be amended from “the identification results” to “[[the ]]identification results”. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 13, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 14, line 1, the reference, “the device” should be read as “the electronic device” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 16, line 1, the reference “the medium” should be read as “the non-transitory storage medium” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 17, line 1, the reference “the medium” should be read as “the non-transitory storage medium” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 18, line 1, the reference “the medium” should be read as “the non-transitory storage medium” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 19, line 1, the reference “the medium” should be read as “the non-transitory storage medium” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 19, line 9, the reference “the identification results” is lack of proper antecedent support since there is no prior first instantiation of “identification results” to have this instance of antecedent referencing, the claim is suggested to be amended from “the identification results” to “[[the ]]identification results”. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim 20, line 1, the reference “the medium” should be read as “the non-transitory storage medium” to follow proper antecedent basis. Appropriate correction is required to avoid 112(b) antecedent basis issue.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitation(s) that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Claims 2, 5, 8-9, 12, 16 and 19 , recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 2; recites the limitation, “inputting the multimedia content to be classified into a first identifying model…acquiring an identification result corresponding to the multimedia content to be classified output by the first identifying model” [Lines 3-7].
Claim 5; recites the limitation, “inputting the prompt and the first multimedia content into a second identifying model…acquiring an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 8; recites the limitation, “a storage means configured to store one or more programs” [Line 3].
Claim 9; recites the limitation, “inputting the multimedia content to be classified into a first identifying model…acquire an identification result corresponding to the multimedia content to be classified output by the first identifying model” [Lines 4-8].
Claim 12; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 16; recites the limitation, “inputting the multimedia content to be classified into a first identifying model…acquire an identification result corresponding to the multimedia content to be classified output by the first identifying model” [Lines 4-8].
Claim 19; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 4-8].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 2, 5, 8-9, 12, 16 and 19;
(i) “first identifying model” the instant specification, filed on December 06th, 2024, in Par. [0029] provides sufficient written support of a structure, material and/or act for the recited first identifying model to perform the recited function, wherein Par. [0029] discloses the first identifying model may be a neural network model include an encoder and a decoder, thus have sufficient structure or material wherein is a neural network model include an encoder and a decoder.).
(ii) “second identifying model” the instant specification, filed on December 06th, 2024, does not provide sufficient written of a structure, material and/or act for the recited second identifying model to perform the recited function, the closest support can be found in Par. [0038] of wherein the second identifying model may include a multimodal graphic model, however, doesn’t provide specific structure, material and/or act for the feature to sufficiently understood to perform the recited function, thus have no sufficient structure, material and/or act.
(iii) “storage means” the instant specification, filed on December 06th, 2024, in Par. [0091] provides sufficient written support of a structure, material and/or act for the recited first identifying model to perform the recited function, wherein Par. [0091] discloses the storage means such as, for example, a magnetic tape, hard disk, etc., thus have sufficient structure or material wherein is a magnetic tape, and/or a hard disk.).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 5, 12 and 19’s limitations:
Claim 5; recites the limitation, “inputting the prompt and the first multimedia content into a second identifying model…acquiring an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 12; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 19; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 4-8].
Claim 5, 12 and 19, each respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains 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, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation.
Claim 5; recites the limitation, “inputting the prompt and the first multimedia content into a second identifying model…acquiring an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 12; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 3-7].
Claim 19; recites the limitation, “input the prompt and the first multimedia content into a second identifying model…acquire an identification result corresponding to the first multimedia content output by the second identifying model” [Lines 4-8].
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
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, 4, 8, 11, 15 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more nor an integration of the judicial exceptions into a practical application. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion). The claimed invention merely perform classifying multimedia content through observable category and category confidence degree of a target object within the content. See analysis below for more details.
Regarding Independent Claim 1 and its dependent claim 4,
Step 1 Analysis: Claim 1 is directed to a method/process, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“determining a first multimedia content…within the first multimedia content; and
Identifying a category of the first multimedia content based on the prompt”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
“determining a first multimedia content…within the first multimedia content” is a step a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, multimedia content, etc.) and evaluate them to make a determination/identification of multimedia to be classified and a prompt based on observable information such as a category of an object according to certain conditioning.
Please see MPEP §2106.04. The limitations of:
“identifying a category of the first multimedia content based on the prompt” is a step a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, multimedia content, etc.) and evaluate them to make a determination/identification.
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“acquiring a category and an object category confidence degree of a target object within a multimedia content to be classifier”
The additional elements include steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc. [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc.] . example of overcoming: “generating data from using the output of the neural network to estimate what time the vehicle should stop to avoid hitting an obstacle”.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claim 4 do not provide elements that overcome the deficiencies of the independent claim 1.
Moreover, claim 4 recites, in part,
“comparing the object category…meets the first preset condition; and
acquiring a prompt template, and generating…content with the prompt template”,
The step of comparing is a mental process which a human mind can also perform through observation and evaluation to make a comparison between observable data/information.
The step of acquiring a prompt template and generating… is a step of insignificant extra-solution activities including acquiring data/information and generating data/information of data gathering process.
Accordingly, the dependent claim 4 is not patent eligible under 101.
Regarding the independent claim 8 and its dependent claim 11:
The independent claim 8 recites analogous limitations to the independent claim 1, therefore, is 101 ineligible for these analogous limitations under the same analysis. Moreover, claim 8 recites further additional elements of an electronic device, one or more processors, and a storage means configured to store one or more programs, which are generic computer and computer components recited at high level of generality to perform generic well-known corresponding functions. The same for the dependent claim 11 which recited analogous limitations to the dependent claim 4, hence, is 101 ineligible under the same approach.
Regarding the independent claim 15 and its dependent claim 18:
The independent claim 15 recites analogous limitations to the independent claim 1, therefore, is 101 ineligible for these analogous limitations under the same analysis. Moreover, claim 15 recites further additional elements of a non-transitory storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, causing the processor to, which are generic computer and computer components recited at high level of generality to perform generic well-known corresponding functions. The same for the dependent claim 18 which recited analogous limitations to the dependent claim 4, hence, is 101 ineligible under the same approach.
These claims below are 101 eligible:
Regarding claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20:
Claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 are eligible under 35 U.S.C. 101.
Particularly, claim 2 reflects a practical application, such as, claim 2 recites additional elements of “acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to at target category; acquiring an identification result corresponding….multimedia content to be classified”, these steps are considered to be additional elements since they perform extra-solution activities of data gathering, gathering of information and data, moreover, these additional elements are recited to be an integration of the additional elements into a practical application by using these acquired data/information, which are output by a machine learning model, wherein the machine learning model is trained using a particular training sample generated based on identification data corresponding to a target category, moreover, the acquired data is used to classify a target based on multiple identification information such as, position, category, object category confidence degree to classify the target object within the multimedia content. Therefore, claim 2 is 101 eligible under Step 2A Prong 2 of the 101 requirements.
Therefore, the dependent claims 3, 5-7 are 101 eligible under dependency to claim 2.
Similarly, for claim 9 which reflects a practical application, such as, claim 9 recites additional elements that are analogous to the limitations of the claim 2 as discussed above, of “acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to at target category; acquiring an identification result corresponding….multimedia content to be classified”, these steps are considered to be additional elements since they perform extra-solution activities of data gathering, gathering of information and data, moreover, these additional elements are recited to be an integration of the additional elements into a practical application by using these acquired data/information, which are output by a machine learning model, wherein the machine learning model is trained using a particular training sample generated based on identification data corresponding to a target category, moreover, the acquired data is used to classify a target based on multiple identification information such as, position, category, object category confidence degree to classify the target object within the multimedia content. Therefore, claim 9 is 101 eligible under Step 2A Prong 2 of the 101 requirements.
Therefore, the dependent claims 10, 12-14 are 101 eligible under dependency to claim 9.
Similarly, for claim 16 which reflects a practical application, such as, claim 16 recites additional elements that are analogous to the limitations of the claim 2 as discussed above, of “acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to at target category; acquiring an identification result corresponding….multimedia content to be classified”, these steps are considered to be additional elements since they perform extra-solution activities of data gathering, gathering of information and data, moreover, these additional elements are recited to be an integration of the additional elements into a practical application by using these acquired data/information, which are output by a machine learning model, wherein the machine learning model is trained using a particular training sample generated based on identification data corresponding to a target category, moreover, the acquired data is used to classify a target based on multiple identification information such as, position, category, object category confidence degree to classify the target object within the multimedia content. Therefore, claim 16 is 101 eligible under Step 2A Prong 2 of the 101 requirements.
Therefore, the dependent claims 17 and 19-20 are 101 eligible under dependency to claim 16.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 8, 11, 15 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kerwell Liao et. al. (“US 2015/0153906 A1” hereinafter as “Liao”).
Regarding claim 1, Liao teaches a method for classifying multimedia content (Abstract discloses “methods and apparatus related to identifying a video for completing a task and determining a plurality of video segments”; Par. [0060] discloses “in some implementations the video segmentation system may identify how-to videos based on an existing classification”), comprising: acquiring a category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and an object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of a target object within a multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified); determining a first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determining a prompt based on the category of the target object within the first multimedia content (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined); and identifying a category of the first multimedia content based on the prompt (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
Regarding claim 4, Liao teaches the method according to claim 1, wherein determining the first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determining the prompt based on the category of the target object within the first multimedia content (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined) comprises: comparing the object category confidence degree of the multimedia content to be classified with a first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”; Par. [0085] discloses “these sources may be ranked based on one or more criteria, including criteria similar to that utilized to determine the confidence measure…sources C and D may be identified as unreliable sources based on a ranking that fails to satisfy a threshold value” wherein, the top ranking requirement is analogous to the recited first preset condition), to obtain the first multimedia content that meets the first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”); and acquiring a prompt template (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task”), and generating the prompt by combining the category of the target object within the first multimedia content with the prompt template (Par. [0094] discloses “the template may be in the form of a relational database with one or more structural elements related to the task…the template may be additionally and/or alternatively associated with tags and/or metadata”).
Regarding claim 8, Liao teaches an electronic device, comprising (Par. [0174] discloses “an example computer system”): one or more processors (Par. [0174] discloses “computer system typically includes at least one processor); and a storage means configured to store one or more programs (Par. [0174] discloses “…include a storage subsystem…a file storage subsystem…”); wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to (Par. [0174] discloses “computer system typically includes at least one processor which communicates with a number of peripheral devices via bus subsystem…include a storage subsystem…a file storage subsystem…”): acquire a category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and an object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of a target object within a multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified); determine a first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determining a prompt based on the category of the target object within the first multimedia content (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined); and identify a category of the first multimedia content based on the prompt (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
Regarding claim 11, Liao teaches the device according to claim 8, wherein the programs causing the device to determine the first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determine the prompt based on the category of the target object within the first multimedia content (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined) comprise the programs causing the device to: compare the object category confidence degree of the multimedia content to be classified with a first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”; Par. [0085] discloses “these sources may be ranked based on one or more criteria, including criteria similar to that utilized to determine the confidence measure…sources C and D may be identified as unreliable sources based on a ranking that fails to satisfy a threshold value” wherein, the top ranking requirement is analogous to the recited first preset condition), to obtain the first multimedia content that meets the first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”); and acquire a prompt template (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task”), and generate the prompt by combining the category of the target object within the first multimedia content with the prompt template (Par. [0094] discloses “the template may be in the form of a relational database with one or more structural elements related to the task…the template may be additionally and/or alternatively associated with tags and/or metadata”).
Regarding claim 15, Liao teaches a non-transitory storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, causing the processor (Par. [0174] discloses “computer system typically includes at least one processor which communicates with a number of peripheral devices via bus subsystem…include a storage subsystem…a file storage subsystem…” indicating a computer system which has a ROM or RAM being a non-transitory storage medium) to: acquire a category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and an object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of a target object within a multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified); determine a first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determining a prompt based on the category of the target object within the first multimedia content (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined); and identify a category of the first multimedia content based on the prompt (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
Regarding claim 18, Liao teaches the medium according to claim 15, wherein the instructions causing the processor to determine the first multimedia content within the multimedia content to be classified (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content) based on the object category confidence degree (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task”), and determine the prompt based on the category of the target object within the first multimedia content comprise (Par. [0083] discloses “select the video with the highest confidence measure as a source for the task attributes related to performing the task” and Par. [0084] discloses “identify a plurality of sources and extract one or more task attributes from such sources”; moreover, Par. [0083] discloses “task attributes…may include one or more of a title, introduction, skill level to perform the task, average time required to perform the task…a set of steps to perform the task, and one or more sources of information for the task attributes” wherein, the task attributes is analogous to the recited prompt determined) the instructions causing the processor to: compare the object category confidence degree of the multimedia content to be classified with a first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”; Par. [0085] discloses “these sources may be ranked based on one or more criteria, including criteria similar to that utilized to determine the confidence measure…sources C and D may be identified as unreliable sources based on a ranking that fails to satisfy a threshold value” wherein, the top ranking requirement is analogous to the recited first preset condition), to obtain the first multimedia content that meets the first preset condition (Par. [0103] discloses “the selection may be based on the confidence measure. In some implementations the how-to videos may be ranked based on the confidence measure and the top ranked how-to video may be selected”); and acquire a prompt template (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task”), and generate the prompt by combining the category of the target object within the first multimedia content with the prompt template (Par. [0094] discloses “the template may be in the form of a relational database with one or more structural elements related to the task…the template may be additionally and/or alternatively associated with tags and/or metadata”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 6, 9, 13, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kerwell Liao et. al. (“US 2015/0153906 A1” hereinafter as “Liao”)in view of Hari Kalva et. al. (“US 2024/0137543 A1” hereinafter as “Kalva”).
Regarding claim 2, Liao teaches the method according to claim 1, wherein acquiring the category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and the object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of the target object within the multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified) comprises: acquiring the multimedia content to be classified (Par. [0060] discloses “in some implementations the video segmentation system may identify how-to videos based on an existing classification”), and inputting the multimedia content to be classified into a model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness” wherein, the analysis is analogous to the recited model); and acquiring an identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…” wherein the result includes the confidence measure/object category confidence degree; moreover, Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments” wherein, the identifier being flagged a portion of the video segment indicating a position; Par. [0084] discloses “the plurality of sources may be associated with confidence measures based on one or more task attributes” indicating the category).
However, Liao does not explicitly teach the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”), wherein a training sample set of the first identifying model comprises a multimedia content sample (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) generated based on identification data corresponding to a target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a method, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Wherein having Liao’s method having the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Regarding claim 6, Liao in view of Kalva teaches the method according to claim 2, Liao teaches wherein the multimedia content to be classified comprises a video (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content); and after acquiring the identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), the method further comprises: for each video frame included in the video (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise” wherein, determining frames including noise concerning going through all frames in the video), determining a target confidence degree based on the object category confidence degree (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the indication of confidence here is analogous to the recited target confidence degree) corresponding to the target object at each position in the video frame (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task” wherein, the portion of the video segment indicating a position being concerned; Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments”); and determining a second video frame whose target confidence degree meets a second preset condition (Par. [0077] discloses “determine how many of the frames in the given how-to video included noise…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the frame having no noise [a second video frame] indicating a higher information-to-noise ratio [a second preset condition] hence, meet the confidence indicative of the true confidence), and determining a category of the second video frame based on the category of the target object in the second video frame (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model.
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a method, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s method having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Regarding claim 9, Liao teaches the device according to claim 8, wherein the programs causing the device to acquire the category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and the object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of the target object within the multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified) comprise the programs causing the device to: acquire the multimedia content to be classified (Par. [0060] discloses “in some implementations the video segmentation system may identify how-to videos based on an existing classification”), and inputting the multimedia content to be classified into a model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness” wherein, the analysis is analogous to the recited model); and acquire an identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…” wherein the result includes the confidence measure/object category confidence degree; moreover, Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments” wherein, the identifier being flagged a portion of the video segment indicating a position; Par. [0084] discloses “the plurality of sources may be associated with confidence measures based on one or more task attributes” indicating the category).
However, Liao does not explicitly teach the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”), wherein a training sample set of the first identifying model comprises a multimedia content sample (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) generated based on identification data corresponding to a target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a device to perform wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Wherein, having Liao’s device having the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Regarding claim 13, Liao in view of Kalva teaches the device according to claim 9, Liao teaches wherein the multimedia content to be classified comprises a video (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content); and after acquiring the identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), the method further comprises: for each video frame included in the video (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise” wherein, determining frames including noise concerning going through all frames in the video), determining a target confidence degree based on the object category confidence degree (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the indication of confidence here is analogous to the recited target confidence degree) corresponding to the target object at each position in the video frame (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task” wherein, the portion of the video segment indicating a position being concerned; Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments”); and determining a second video frame whose target confidence degree meets a second preset condition (Par. [0077] discloses “determine how many of the frames in the given how-to video included noise…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the frame having no noise [a second video frame] indicating a higher information-to-noise ratio [a second preset condition] hence, meet the confidence indicative of the true confidence), and determining a category of the second video frame based on the category of the target object in the second video frame (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model.
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a device, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s device having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Regarding claim 16, Liao teaches the medium according to claim 15, wherein the instructions causing the processor to acquire the category (Par. [0033] discloses “identifying a video for completing a task and determining a plurality of video segments…based on one or more attributes of the task” wherein, a task is analogous to a category) and the object category confidence degree (Par. [0068] discloses “a confidence measure may be associated with one or more videos. The confidence measure for a given how-to video may be indicative of the effectiveness of the given video in providing correct and comprehensive completion steps for the task”) of the target object within the multimedia content to be classified (Par. [0076] discloses “the confidence measure for a given how-to video may be based on how closely the given how-to video relates to the how-to query…based on anchor-text evidence…the similarities between the textual snippet in an anchor-text for a given source and the task identified by the how-to query may be utilized as a measure of confidence” wherein, the textual snippet is analogous to a target object, the video being analogous to the multimedia content to be classified) comprise the instructions causing the processor to: acquire the multimedia content to be classified (Par. [0060] discloses “in some implementations the video segmentation system may identify how-to videos based on an existing classification”), and inputting the multimedia content to be classified into a model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness” wherein, the analysis is analogous to the recited model); and acquire an identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…” wherein the result includes the confidence measure/object category confidence degree; moreover, Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments” wherein, the identifier being flagged a portion of the video segment indicating a position; Par. [0084] discloses “the plurality of sources may be associated with confidence measures based on one or more task attributes” indicating the category).
However, Liao does not explicitly teach the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”), wherein a training sample set of the first identifying model comprises a multimedia content sample (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) generated based on identification data corresponding to a target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a system, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
Wherein, having Liao’s system having the model being a first identifying model, wherein a training sample set of the first identifying model comprises a multimedia content sample generated based on identification data corresponding to a target category.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Regarding claim 20, Liao in view of Kalva teaches the medium according to claim 16, Liao teaches wherein the multimedia content to be classified comprises a video (Par. [0083] discloses “the video segmentation system may utilize the confidence measure to rank the one or more videos and select the video with the highest confidence measure as a source for the task attributes related to performing the task” wherein, the selected video here is analogous to the recited first multimedia content); and after acquiring the identification result corresponding to the multimedia content to be classified output by the model (Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), the method further comprises: for each video frame included in the video (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise” wherein, determining frames including noise concerning going through all frames in the video), determining a target confidence degree based on the object category confidence degree (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the indication of confidence here is analogous to the recited target confidence degree) corresponding to the target object at each position in the video frame (Par. [0077] discloses “an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise…other portions of the video not related to the task” wherein, the portion of the video segment indicating a position being concerned; Par. [0082] disclose “”video segmentation…each of the video segments may be an identifier of a certain portion of the given video and the video segmentation system may flag the given video based on the video segments”); and determining a second video frame whose target confidence degree meets a second preset condition (Par. [0077] discloses “determine how many of the frames in the given how-to video included noise…a higher information-to-noise ratio may be associated with a confidence measure more indicative of confidence whereas a lower information-to-noise ratio may be associated with a confidence measure less indicative of confidence” wherein, the frame having no noise [a second video frame] indicating a higher information-to-noise ratio [a second preset condition] hence, meet the confidence indicative of the true confidence), and determining a category of the second video frame based on the category of the target object in the second video frame (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model.
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a system, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s system having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kerwell Liao et. al. (“US 2015/0153906 A1” hereinafter as “Liao”) in view of Hari Kalva et. al. (“US 2024/0137543 A1” hereinafter as “Kalva”) and Haozhi Huang et. al. (“US 2021/0243383 A1” hereinafter as “Huang”).
Regarding claim 3, Liao in view of Kalva teaches the method according to claim 2.
However, Liao does not explicitly teach wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Kalva teaches wherein generating the multimedia content sample based on identification data (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) corresponding to the target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”) comprises: acquiring identification data corresponding to the target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements” wherein the correlations being analogous to the identification data); generating simulated identification data based on the identification data (Par. [0036] discloses “video encoder…may generate video and signal regions to be synthesizes at a receiver”, and Par. [0056] discloses “decoder may generate synthesizable content model by training synthesizable content model using training samples correlating frame sequences” wherein the synthesized video being analogous to the recited simulated identification data), wherein the identification data represents an identification in a first form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein the parameter is analogous to the first form as claimed), and the simulated identification data represents an identification in a second form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein, the first region is analogous to the recited second form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a method, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein having Liao method having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Huang teaches fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample (Par. [0023] discloses “the video synthesis model being used for fusing the source image, the source motion key point, and the target motion key point, the first output image…”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a method according, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Huang of having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein having Liao method having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video synthesis based on feature extraction and fusion to generate a more accurate image. Since both Liao and Huang perform video generation based on object similarity determination. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Huang’s system improves video synthesis based on feature extraction and fusion to generate a more accurate image, (see Huang’s Par. [0138]).
Regarding claim 10, Liao in view of Kalva teaches the device according to claim 9.
However, Liao does not explicitly teach wherein the programs causing the device to generate the multimedia content sample based on identification data corresponding to the target category comprise the programs causing the device to: acquire identification data corresponding to the target category; generate simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Kalva teaches wherein the programs causing the device to generate the multimedia content sample based on identification data (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) corresponding to the target category comprise the programs causing the device to (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”): acquire identification data corresponding to the target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements” wherein the correlations being analogous to the identification data); generate simulated identification data based on the identification data (Par. [0036] discloses “video encoder…may generate video and signal regions to be synthesizes at a receiver”, and Par. [0056] discloses “decoder may generate synthesizable content model by training synthesizable content model using training samples correlating frame sequences” wherein the synthesized video being analogous to the recited simulated identification data), wherein the identification data represents an identification in a first form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein the parameter is analogous to the first form as claimed), and the simulated identification data represents an identification in a second form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein, the first region is analogous to the recited second form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a device, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein having Liao’s device having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Huang teaches fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample (Par. [0023] discloses “the video synthesis model being used for fusing the source image, the source motion key point, and the target motion key point, the first output image…”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a device, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Huang of having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein, having Liao’s device having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video synthesis based on feature extraction and fusion to generate a more accurate image. Since both Liao and Huang perform video generation based on object similarity determination. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Huang’s system improves video synthesis based on feature extraction and fusion to generate a more accurate image, (see Huang’s Par. [0138]).
Regarding claim 17, Liao in view of Kalva teaches the medium according to claim 16.
However, Liao does not explicitly teach wherein the instructions causing the processor to generate the multimedia content sample based on identification data corresponding to the target category comprise the instructions causing the processor to: acquire identification data corresponding to the target category; generate simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Kalva teaches wherein the instructions causing the processor to generate the multimedia content sample based on identification data (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements…training data may be linked to descriptors of categories by tags, tokens, or other data elements”; Par. [0035] discloses “video encoders using predictive coding” wherein, video indicating multimedia content to be the training data) corresponding to the target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”) comprise the instructions causing the processor to: acquire identification data corresponding to the target category (Par. [0038] discloses “training data as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements” wherein the correlations being analogous to the identification data); generate simulated identification data based on the identification data (Par. [0036] discloses “video encoder…may generate video and signal regions to be synthesizes at a receiver”, and Par. [0056] discloses “decoder may generate synthesizable content model by training synthesizable content model using training samples correlating frame sequences” wherein the synthesized video being analogous to the recited simulated identification data), wherein the identification data represents an identification in a first form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein the parameter is analogous to the first form as claimed), and the simulated identification data represents an identification in a second form (Par. [0056] discloses “wherein the decoding the video includes generating a synthesizable content model using at least a parameter from the bitstream and animating a first region using the synthesizable content model” wherein, the first region is analogous to the recited second form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a system, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein having Liao’s system having wherein generating the multimedia content sample based on identification data corresponding to the target category comprises: acquiring identification data corresponding to the target category; generating simulated identification data based on the identification data, wherein the identification data represents an identification in a first form, and the simulated identification data represents an identification in a second form; and fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Huang teaches fuse the simulated identification data with a preset multimedia content to obtain the multimedia content sample (Par. [0023] discloses “the video synthesis model being used for fusing the source image, the source motion key point, and the target motion key point, the first output image…”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a system, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Huang of having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
Wherein, having Liao’s system having fusing the simulated identification data with a preset multimedia content to obtain the multimedia content sample.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video synthesis based on feature extraction and fusion to generate a more accurate image. Since both Liao and Huang perform video generation based on object similarity determination. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Huang’s system improves video synthesis based on feature extraction and fusion to generate a more accurate image, (see Huang’s Par. [0138]).
Claims 5, 7, 12, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kerwell Liao et. al. (“US 2015/0153906 A1” hereinafter as “Liao”) in view of Hari Kalva et. al. (“US 2024/0137543 A1” hereinafter as “Kalva”) and Seung Joon Lee et. al. (“US 2024/0362272 A1” hereinafter as “Lee”).
Regarding claim 5, Liao in view of Kalva teaches the method according to claim 2, Liao teaches wherein identifying the category of the first multimedia content based on the prompt (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified); inputting the prompt and the first multimedia content into a second model (Par. [0066] discloses “the video characteristics of the given video may include an audio and/or textual transcript of the given video…the video text may additionally and/or alternatively include text that is displayed on the video, text overlaid on the video”; Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments” wherein, the segmentation system that utilize textual transcript is analogous to the recited second model); acquiring an identification result corresponding to the first multimedia content output by the second model (Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments”); and combining the first multimedia content with the identification results (Par. [0090] discloses “anchor-text associated with a primary source from which the how-to video is selected…and the video segmentation system may identify”) corresponding to the model and the second model respectively ([0090] discloses “…the video segmentation system may identify” indicating the result of the second model, Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), to determine the category of the first multimedia content (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a method, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s method having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Lee teaches the second model being a second identifying model (Par. [0022] discloses “the video analysis system also generates a set of prompt embeddings representing at least a portion of the query in a latent space. The set of prompt embeddings and a set of input embeddings are combined to generate an input tensor” to be input into a machine-learned decoder [analogous to the recited second identifying model]), wherein a training sample set of the second identifying model comprises an image-text sample pair (Par. [0094] discloses “a training instance may recite “The video introduces three sugar types at….”” Indicating a training sample including image-text pair; Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”), and text information in the image-text sample pair (Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”) is determined based on an image description of a corresponding image (Par. [0021] discloses “the set of video embeddings are generated by extracting frame data, audio data, or text data from the video content”; moreover, Par. [0067] discloses “the text data may be, for example, a transcript or caption of a video”; Par. [0074] discloses “the decoder…to receive the set of video embeddings and the set of prompt embeddings and generate an output for responding to the user query” wherein, the output here is analogous to the recited identification result).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a method, wherein identifying the category of the first multimedia content based on the prompt; inputting the prompt and the first multimedia content into a second model; acquiring an identification result corresponding to the first multimedia content output by the second model; and combining the first multimedia content with the identification results corresponding to the first model and the second model respectively, with the teachings of Lee of having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Wherein, having Liao’s method having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video generation by incorporating multi-modal information effectively. Both Liao and Lee perform image-text alignment processing for video recognition. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Lee’s system improves video generation by incorporating multi-modal information effectively, (see Lee’s Par. [0071]).
Regarding claim 7, Liao in view of Kalva teaches the method according to claim 6, Liao teaches further comprising: determining the category of the video based on the category of the first video frame and the category of the second video frame (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task” wherein, any of the frame is analogous to the recited first video frame and any of the other is analogous to the recited second video frame, and the determining of the category of the video depend on the noise of each of these frames [categories of these frames]), wherein the first video frame is a video frame whose target confidence degree meets the first preset condition (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task”, wherein the confidence measure here with higher information-to-noise ratio in this instance is analogous to the first preset condition as claimed), and the category of the first video frame is determined by the second model based on the prompt Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task”, wherein the confidence measure here with lower information-to-noise ratio in this instance is analogous to the second preset condition as claimed.
However, Liao in view of Kalva does not explicitly teach the second model being a second identifying model.
Lee teaches the second model being a second identifying model (Par. [0022] discloses “the video analysis system also generates a set of prompt embeddings representing at least a portion of the query in a latent space. The set of prompt embeddings and a set of input embeddings are combined to generate an input tensor” to be input into a machine-learned decoder [analogous to the recited second identifying model]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a method wherein identifying the category of the first multimedia content based on the prompt; inputting the prompt and the first multimedia content into a second model; acquiring an identification result corresponding to the first multimedia content output by the second model; and combining the first multimedia content with the identification results corresponding to the first model and the second model respectively, with the teachings of Lee of having the second model being a second identifying model.
Wherein, having Liao’s method having the second model being a second identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video generation by incorporating multi-modal information effectively. Both Liao and Lee perform image-text alignment processing for video recognition. Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Lee’s system improves video generation by incorporating multi-modal information effectively, (see Lee’s Par. [0071]).
Regarding claim 12, Liao in view of Kalva teaches the device according to claim 9, Liao teaches wherein the programs causing the device to identify the category of the first multimedia content based on the prompt comprises the programs causing the device to (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified): input the prompt and the first multimedia content into a second model (Par. [0066] discloses “the video characteristics of the given video may include an audio and/or textual transcript of the given video…the video text may additionally and/or alternatively include text that is displayed on the video, text overlaid on the video”; Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments” wherein, the segmentation system that utilize textual transcript is analogous to the recited second model); acquire an identification result corresponding to the first multimedia content output by the second model (Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments”); and combine the first multimedia content with the identification results (Par. [0090] discloses “anchor-text associated with a primary source from which the how-to video is selected…and the video segmentation system may identify”) corresponding to the model and the second model respectively ([0090] discloses “…the video segmentation system may identify” indicating the result of the second model, Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), to determine the category of the first multimedia content (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a device, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s device having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Lee teaches the second model being a second identifying model (Par. [0022] discloses “the video analysis system also generates a set of prompt embeddings representing at least a portion of the query in a latent space. The set of prompt embeddings and a set of input embeddings are combined to generate an input tensor” to be input into a machine-learned decoder [analogous to the recited second identifying model]), wherein a training sample set of the second identifying model comprises an image-text sample pair (Par. [0094] discloses “a training instance may recite “The video introduces three sugar types at….”” Indicating a training sample including image-text pair; Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”), and text information in the image-text sample pair (Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”) is determined based on an image description of a corresponding image (Par. [0021] discloses “the set of video embeddings are generated by extracting frame data, audio data, or text data from the video content”; moreover, Par. [0067] discloses “the text data may be, for example, a transcript or caption of a video”; Par. [0074] discloses “the decoder…to receive the set of video embeddings and the set of prompt embeddings and generate an output for responding to the user query” wherein, the output here is analogous to the recited identification result).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a device, wherein identifying the category of the first multimedia content based on the prompt; inputting the prompt and the first multimedia content into a second model; acquiring an identification result corresponding to the first multimedia content output by the second model; and combining the first multimedia content with the identification results corresponding to the first model and the second model respectively, with the teachings of Lee of having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Wherein, having Liao’s device, having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video generation by incorporating multi-modal information effectively. Both Liao and Lee perform image-text alignment processing for video recognition. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Lee’s system improves video generation by incorporating multi-modal information effectively, (see Lee’s Par. [0071]).
Regarding claim 14, Liao in view of Kalva teaches the device according to claim 13, Liao teaches wherein the device is further caused to: determine the category of the video based on the category of the first video frame and the category of the second video frame (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task” wherein, any of the frame is analogous to the recited first video frame and any of the other is analogous to the recited second video frame, and the determining of the category of the video depend on the noise of each of these frames [categories of these frames]), wherein the first video frame is a video frame whose target confidence degree meets the first preset condition (Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task”, wherein the confidence measure here with higher information-to-noise ratio in this instance is analogous to the first preset condition as claimed), and the category of the first video frame is determined by the second model based on the prompt Par. [0077] discloses “the confidence measure of the given how-to video may be based on an analysis of the information-to-noise ratio of the given how-to video. For example, the scoring system may determine how many of the frames in the given how-to video included noise (e.g., commercials and/or other portions of the video not related to the task”, wherein the confidence measure here with lower information-to-noise ratio in this instance is analogous to the second preset condition as claimed.
However, Liao in view of Kalva does not explicitly teach the second model being a second identifying model.
Lee teaches the second model being a second identifying model (Par. [0022] discloses “the video analysis system also generates a set of prompt embeddings representing at least a portion of the query in a latent space. The set of prompt embeddings and a set of input embeddings are combined to generate an input tensor” to be input into a machine-learned decoder [analogous to the recited second identifying model]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a device wherein identifying the category of the first multimedia content based on the prompt; inputting the prompt and the first multimedia content into a second model; acquiring an identification result corresponding to the first multimedia content output by the second model; and combining the first multimedia content with the identification results corresponding to the first model and the second model respectively, with the teachings of Lee of having the second model being a second identifying model.
Wherein, having Liao’s device having the second model being a second identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video generation by incorporating multi-modal information effectively. Both Liao and Lee perform image-text alignment processing for video recognition. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Lee’s system improves video generation by incorporating multi-modal information effectively, (see Lee’s Par. [0071]).
Regarding claim 19, Liao in view of Kalva teaches the medium according to claim 16, Liao teaches wherein the instructions causing the processor to identify the category of the first multimedia content based on the prompt comprises the instructions causing the processor to (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified): input the prompt and the first multimedia content into a second model (Par. [0066] discloses “the video characteristics of the given video may include an audio and/or textual transcript of the given video…the video text may additionally and/or alternatively include text that is displayed on the video, text overlaid on the video”; Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments” wherein, the segmentation system that utilize textual transcript is analogous to the recited second model); acquire an identification result corresponding to the first multimedia content output by the second model (Par. [0079] discloses “the video segmentation system may determine a plurality of video segments of a given video. In some implementations an audio and/or textual transcript of the given video may be utilized to determine the video segments”); and combine the first multimedia content with the identification results (Par. [0090] discloses “anchor-text associated with a primary source from which the how-to video is selected…and the video segmentation system may identify”) corresponding to the model and the second model respectively ([0090] discloses “…the video segmentation system may identify” indicating the result of the second model, Par. [0075] discloses “the confidence measure for the given how-to video may be based on an analysis of the cohesiveness of the given how-to video…for example, elements in the given how-to video and/or transcript of the video may be classified based on their mutual similarities and dissimilarities…classifications may be more indicative of the how-to video’s cohesiveness”; Par. [0078] discloses “a linear combination of the one or more confidence measures discussed herein may be utilized to associate a confidence measure with the given how-to video” wherein, the associated how-to video being analogous to the recited identification result), to determine the category of the first multimedia content (Par. [0093] discloses “a task may…be associated with a template. The template may be indicative of desired contents of the how-to videos…may create a template based on the one or more task attributes…in some implementations the task attributes may be determined from the task template for the task” wherein, the task template is analogous to the recited category being identified).
However, Liao does not explicitly teach the model being a first identifying model.
Kalva teaches the model being a first identifying model (Par. [0025] discloses “use neural networks and/or deep learning methods to estimate or generate an animated flow of fluids based on a single static image…encoder and decoder” being analogous to the recited first identifying model, and analogous to Liao’s similarity analysis, Par. [0053] discloses “a decoder and/or encoder may be configured to generate a classifier using a K-nearest neighbors…utilizes feature similarity to analyze how closely out-of-sample-features resemble training data”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao of a system, wherein acquiring the category and the object category confidence degree of the target object within the multimedia content to be classified comprises: acquiring the multimedia content to be classified, and inputting the multimedia content to be classified into a model; and acquiring an identification result corresponding to the multimedia content to be classified output by the model, wherein the identification result comprises position, category, and object category confidence degree of the target object within the multimedia content to be classified, with the teachings of Kalva of having wherein the model being a first identifying model.
Wherein having Liao’s system having the model being a first identifying model.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve on video encoding/compression based on element similarity efficiently without losing important information. Since both Liao and Kalva perform video element similarity determination for video tagging. Wherein Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Kalva’s system improves on video encoding/compression based on element similarity efficiently without losing important information, (see Kalva’s Pars. [0003-0005]).
However, Liao in view of Kalva does not explicitly teach the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Lee teaches the second model being a second identifying model (Par. [0022] discloses “the video analysis system also generates a set of prompt embeddings representing at least a portion of the query in a latent space. The set of prompt embeddings and a set of input embeddings are combined to generate an input tensor” to be input into a machine-learned decoder [analogous to the recited second identifying model]), wherein a training sample set of the second identifying model comprises an image-text sample pair (Par. [0094] discloses “a training instance may recite “The video introduces three sugar types at….”” Indicating a training sample including image-text pair; Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”), and text information in the image-text sample pair (Par. [0114] discloses “the alignment model is coupled to receive the set of video embeddings and generate a set of video-language-aligned embeddings”) is determined based on an image description of a corresponding image (Par. [0021] discloses “the set of video embeddings are generated by extracting frame data, audio data, or text data from the video content”; moreover, Par. [0067] discloses “the text data may be, for example, a transcript or caption of a video”; Par. [0074] discloses “the decoder…to receive the set of video embeddings and the set of prompt embeddings and generate an output for responding to the user query” wherein, the output here is analogous to the recited identification result).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Liao in view of Kalva of a system wherein, identifying the category of the first multimedia content based on the prompt; inputting the prompt and the first multimedia content into a second model; acquiring an identification result corresponding to the first multimedia content output by the second model; and combining the first multimedia content with the identification results corresponding to the first model and the second model respectively, with the teachings of Lee of having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
Wherein, having Liao’s system, having the second model being a second identifying model, wherein a training sample set of the second identifying model comprises an image-text sample pair, and text information in the image-text sample pair is determined based on an image description of a corresponding image.
The motivation behind the modification would have been to improve identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, and further to improve video generation by incorporating multi-modal information effectively. Both Liao and Lee perform image-text alignment processing for video recognition. Wherein, Liao’s system improves identifying a video for a task enable the user to obtain useful video that assist them in in completing the task, (see Liao’s Par. [0033]) and Lee’s system improves video generation by incorporating multi-modal information effectively, (see Lee’s Par. [0071]).
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Saharia, Chitwan et. al., “US 2024/0249456 A1”, discloses Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating images. In one aspect, a method includes: receiving an input text prompt including a sequence of text tokens in a natural language; processing the input text prompt using a text encoder neural network to generate a set of contextual embeddings of the input text prompt; and processing the contextual embeddings through a sequence of generative neural networks to generate a final output image that depicts a scene that is described by the input text prompt.
Gupta, Manish et. al., “US 12651019 B1”, discloses an image search is performed based on a query image where the result includes images with semantic information that matches the query image. For example, image encoders are used to extract information from images that are stored in an index. Continuing this example, when a query is obtained the index is searched to determine potential matches, and a comparison is performed between the query image and the potential matches to determine images with semantic information matching the query image.
Bakunov, Mykyta et. al., “US 12488445 B2”, discloses an automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. The first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.
XUE, Le et. al., “ US 2024/0160917 A1”, discloses method of training a neural network based three-dimensional (3D) encoder is provided. A training dataset is generated using a plurality of 3D models of a 3D model dataset. To generate a first sample of the training dataset, an image generator with multi-view rendering is used to generate a plurality of image candidates of a first 3D model. A word is chosen from metadata associated with the first 3D model. A language model is used to generate one or more text descriptions using the selected word and a plurality of prompts. A point cloud is generated by randomly sampling points in the 3D model. The first sample is generated to include a first image randomly selected from the plurality of image candidates, one or more text descriptions, and the point cloud is generated. The 3D encoder is trained using the training dataset including the first sample.
Conclusion
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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