DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements submitted on April 5, 2024 and on February 10, 2026 have been considered by the Examiner.
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 limitations 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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder (e.g. “unit”) that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the “creation unit” recited in claims 1 and 7, the “training unit” recited in claims 1 and 3, the “acquisition unit” recited in claim 2, the “prediction unit” recited in claims 2 and 4, the “presentation unit” recited in claim 3, and the “allocation unit” recited in claim 4.
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 (i.e. a suitably programmed processor) as performing the claimed function, and equivalents thereof.
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 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 1-7 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 and enablement requirements. The claims 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. These claim(s) also contain subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
In particular, claim 1 recites “a creation unit” and “a training unit,” which as noted above, have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, and thereby cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof. As demonstrated below, these limitations are indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function (e.g., for the creation unit, “create a plurality question sets…”). Such a limitation also lacks an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See MPEP § 2163.03, subsection VI. Under a similar rationale, the specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. Claims 2-7, which depend from claim 1 and thereby include all of the limitations of claim 1, are rejected for the same reasons as claim 1.
Moreover, a similar rationale exists with respect to the “acquisition unit” and the “prediction unit” first introduced in claim 2, the “presentation unit” introduced in claim 3, and the “allocation unit” introduced in claim 4. These claim limitations have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, as is noted above, but are indefinite under 35 U.S.C. 112(b), as is noted below. Accordingly, these limitations also lack an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. In addition, the specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims.
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 1-7 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.
In particular, claim 1 recites “a creation unit” and “a training unit,” which as noted above, have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, and thereby cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof. The “creation unit” is configured to perform a specialized function (i.e. “to create a plurality of question sets…”), as is the “training unit” (i.e. “to train a plurality of learning models…”). In computational contexts, a specialized function must be supported in the specification by the computer and the algorithm that the computer uses to perform the claimed specialized function. See, e.g., In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316, 97 USPQ2d 1737, 1747 (Fed. Cir. 2011). In this case, the specification discloses the computer, i.e. a CPU executing a program (see e.g. paragraphs 0013-0014 and 0023 of Applicant’s specification as originally filed) but does not disclose the algorithm that the computer uses to perform the claimed specialized functions. Accordingly, the scope of the recited limitations is indefinite, and claim 1 thereby fails 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 2-7, which depend from claim 1 and thereby include all of the limitations of claim 1, are rejected for the same reasons.
Moreover, a similar rationale exists with respect to the “acquisition unit” and the “prediction unit” first introduced in claim 2, the “presentation unit” introduced in claim 3, and the “allocation unit” introduced in claim 4. These claim limitations have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, as is noted above, and thereby cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof. Each of these “units” performs a specialized function (e.g. the “acquisition unit” is configured to “acquire a data set that includes a plurality of items target for labeling”). Like noted above, a specialized function must be supported in the specification by the computer and the algorithm that the computer uses to perform the claimed specialized function. In this case, the specification discloses the computer, i.e. a CPU executing a program (see e.g. paragraphs 0013-0014 and 0023 of Applicant’s specification as originally filed) but does not disclose the algorithm that the computer uses to perform the claimed specialized functions. Accordingly, the scope of each of the noted limitations is indefinite.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a mental process) without significantly more.
Claim 1
Claim 1 is to a statutory category, as claim 1 is directed to an apparatus, which is considered a machine or manufacture.
However, claim 1 recites a mental process. “[C]reat[ing] a plurality of question sets, the plurality of questions sets each being composed of a question and a plurality of labels indicating answers that are selectable for the question,” as is recited in claim 1, can practically be performed in the human mind and is thus considered indicative a mental process.
Other than this mental process, claim 1 recites that “a creation unit” is configured to create the plurality of question sets. Claim 1 also recites that the apparatus comprises “a training unit configured to train a plurality of learning models using the plurality of question sets.” However, such recitations of the “creation unit” and “training unit” represent no more than mere instructions to apply the mental process on a computer, and thus do not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f). Particularly, the claim omits any details as to how the learning models are trained; the learning models appear to be invoked merely as a tool for performing the judicial exception.
Consequently, claim 1 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 1 is rejected as being patent ineligible under 35 U.S.C. § 101.
Claim 2
The recitation in claim 2 of “predict labels for the plurality of question sets” is considered a mental process. Such prediction can practically be performed in the human mind.
Claim 2 further recites that a “prediction unit” is configured to predict such labels, particularly “by applying the plurality of learning models to the plurality of items.” However, this recitation of the “prediction unit” represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f).
Claim 2 also recites that the apparatus further comprises “an acquisition unit configured to acquire a data set that includes a plurality of items targeted for labeling.” However, this recitation is indicative of insignificant extra-solution activity, i.e. mere data gathering, and is therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the recitation of the “acquisition unit” in claim 2 also does not amount to significantly more than the judicial exception.
Claim 2 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 2 is also patent ineligible under 35 U.S.C. § 101.
Claim 3
The recitation in claim 3 of “a presentation unit configured to present the labels predicted by the prediction unit, to a user” is indicative of insignificant extra-solution activity, i.e. mere data gathering, and is therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Like noted above, such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the recitation of the “presentation unit” in claim 3 also does not amount to significantly more than the judicial exception.
Moreover, the recitation in claim 3 of “the training unit trains the plurality of learning models based on a result of the user inspecting the labels” represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f).
Claim 3 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 3 is also patent ineligible under 35 U.S.C. § 101.
Claim 4
The recitation in claim 4 of “predicts labels for the plurality of question sets” is considered a mental process. Such prediction can practically be performed in the human mind.
Claim 4 further recites that the “prediction unit” is configured to predict such labels, particularly “using the allocated learning model,” and that “an allocation unit [is] configured to allocate one of the plurality of learning models to each of the plurality of question sets.” However, this recitation of the “prediction unit” and “allocation unit” represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f).
Claim 4 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 4 is also patent ineligible under 35 U.S.C. § 101.
Claims 5 and 6
The recitation in claim 5 that “the plurality of learning models include a learning model trained in advance using a data set that is different from the data set” represents no more than mere instructions to apply the mental process on a computer (i.e. using the learning models), and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f). Similarly, the recitation in claim 6 that “the plurality of learning models include one or more labelling rules set based on the data set” is also considered indicative of mere instructions to apply the mental process on a computer.
Claims 5 and 6 thus fail to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claims 5 and 6 are also patent ineligible under 35 U.S.C. § 101.
Claim 7
The recitation in claim 7 of “creates the plurality of question sets that have different question types, and the question types include a first type that takes three or more values and a second type that takes two values” is considered indicative of an abstract idea, i.e. a mental process. Like noted above, the additional recitation that a “creation unit” creates such question sets is indicative of mere instructions to apply the mental process on a computer.
Claim 7 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 7 is also patent ineligible under 35 U.S.C. § 101.
Claim 8
Claim 8 is to a statutory category, as claim 8 is directed to a method, i.e. a process. However, claim 8 recites a mental process. “[C]reating a plurality of question sets, the plurality of questions sets each being composed of a question and a plurality of labels indicating answers that are selectable for the question,” as is recited in claim 8, can practically be performed in the human mind and is thus considered indicative a mental process.
Other than this mental process, claim 8 recites “training a plurality of learning models using the plurality of question sets.” However, such a recitation of training represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP § 2106.05(f). Particularly, the claim omits any details as to how the learning models are trained; the learning models appear to be invoked merely as a tool for performing the judicial exception.
Consequently, claim 8 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 8 is rejected as being patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0342926 to Zeiler et al. (“Zeiler”), and also over U.S. Patent Application Publication No. 2021/0279470 to Zadeh et al. (“Zadeh”).
Regarding claim 1, Zeiler generally describes a system for associating context information, e.g. labels, with individual multimedia items such as images (see e.g. paragraph 0004). Like claimed, Zeiler particularly teaches that such a system comprises:
a creation unit configured to create a plurality of question sets, the plurality of question sets each being composed of a question and a plurality of labels indicating answers that are selectable for the question (see e.g. paragraphs 0015, 0036 and 0046: Zeiler teaches that the system comprises a task component configured to generate one or more tasks and/or sets of tasks to be performed by one or more users via a user interface. The task component selects one or more labels or sets of labels to correspond with individual tasks, and the tasks may entail presenting options to users for adding, removing, changing and/or confirming predicted label associations – see e.g. paragraphs 0006, 0015 and 0046. Zeiler particularly demonstrates that presenting a task to the user can entail presenting a question to the user and a plurality of labels indicating answers that are selectable for the question – see e.g. paragraph 0064 and FIG. 4. Such a question and associated options is considered a “question set” like claimed. Accordingly, the task component taught by Zeiler is considered a creation unit like claimed, which is configured to create a plurality of questions sets, and wherein each question set is composed of a question and a plurality of labels indicating answers that are selectable for the question.); and
a training unit configured to train a learning model using the plurality of question sets (see e.g. paragraphs 0020, 0038 and 0070: Zeiler discloses that the system also comprises a learning component configured to generate a new machine learning prediction model and/or update an existing machine learning prediction model based on the user input for the tasks, i.e. based on the user input to the plurality of questions sets, inter alia. The learning component is consequently considered a training unit like claimed, which is configured to train a learning model using the plurality of question sets.).
Accordingly, Zeiler teaches an information processing apparatus similar to that of claim 1, but does not explicitly teach training a plurality of learning models using the question sets, as is required by claim 1.
Zadeh nevertheless generally teaches using a plurality of machine learning models (i.e. “detectors”) to identify and label objects within media content items, wherein each model is used to identify and label particular objects (see e.g. paragraphs 0003, 0036 and 0038). Zadeh further suggests that specific question sets can be associated with each machine learning model, e.g. to enable a user to confirm the particular labels provided by the model, whereby the model can then be updated based on answers provided by users to the question sets (see e.g. paragraphs 0043, 0048, and 0078-0080, and FIG. 4D).
It would have been obvious to one of ordinary skill in the art, having the teachings of Zeiler and Zadeh before the effective filing date of the claimed invention, to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets, and whereby a plurality of such learning models are trained using a plurality of the question sets. It would have been advantageous to one of ordinary skill to utilize such a combination because it would enable a user to create and/or configure specific object detectors, as is evident from Zadeh (see e.g. paragraphs 0068-0069 and 0078). Accordingly, Zeiler and Zadeh are considered to teach, to one of ordinary skill in the art, an information processing apparatus like in claim 1.
As per claim 2, Zeiler further teaches that the information processing apparatus comprises (i) an acquisition unit (i.e. a preprocessing component) configured to acquire a data set (e.g. one or more multimedia items from a repository) that includes a plurality of items targeted for labelling, and (ii) a prediction unit (i.e. the preprocessing component) configured to predict labels for the plurality of question sets, by applying a learning model to the plurality of items (see e.g. paragraphs 0005, 0015, 0019, 0028-0030, 0033, 0036, 0039, 0043 and 0064). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Zadeh similarly suggests acquiring a data set that includes a plurality of items (e.g. video frames) targeted for labelling, and predicting labels by applying the plurality of machine learning models (i.e. detectors) to the plurality of items (see e.g. paragraphs 0003, 0036, 0038-0039 and 0043). Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 2.
As per claim 3, Zeiler further teaches that the information processing apparatus comprises a presentation unit (i.e. a user interface component) configured to present the labels predicted by the prediction unit, to a user, wherein the training unit trains the learning model based on a result of the user inspecting the presented labels (see e.g. paragraphs 0015, 0019, 0020, 0036-0038, 0054-0057 and 0064, and FIG. 4). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Zadeh similarly suggests presenting predicted labels to a user, and training a plurality of learning models (i.e. detectors) based on a result of the user inspecting the presented labels (see e.g. paragraphs 0036, 0038, 0039, 0043, 0048 and 0078-0080, and FIG. 4D). Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 3.
As per claim 4, Zeiler further suggests that the apparatus further comprises an allocation unit configured to allocate a learning model to a question set (i.e. to select labels predicted by the learning model to correspond to an individual task), and wherein the prediction unit predicts labels for the question set using the allocated learning model (see e.g. paragraphs 0015, 0019, 0036-0038, 0046, 0047 and 0064, and FIG. 4). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Zadeh similarly suggests allocating one of a plurality of learning models (i.e. detectors) to each of a plurality of question sets, and predicting labels for the plurality of question sets using the allocated learning models (see e.g. paragraphs 0036, 0038, 0039, 0043, 0048 and 0078-0080, and FIG. 4D). Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 4.
As per claim 5, Zeiler suggests that the learning model can be trained in advance using a data set (i.e. an auxiliary training corpus) that is different from the data set (see e.g. paragraphs 0033-0034). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 5.
As per claim 6, Zeiler demonstrates that the learning model can include one or more labeling rules, e.g. confidence thresholds for labeling (e.g. paragraphs 0059-0060). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Zadeh similarly teaches associating the learning models with such labeling rules (see e.g. paragraph 0059). Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 6.
As per claim 7, Zeiler suggests that the creation unit can create a plurality of question sets that have different question types, and that the question types include a first type that takes three or more values and a second type that takes two values (see e.g. paragraph 0006: Zeiler discloses that different tasks can entail providing one label per item, multiple labels per item, etc. Zeiler further discloses that the tasks can provide different interaction elements including a type, e.g. multiple choice, that takes three or more values and a type, e.g. for binary filtering, that takes two values – see e.g. paragraph 0056. Also, like noted above, Zeiler demonstrates that the tasks can include a question – see e.g. paragraph 0064 and FIG. 4. Accordingly, it is apparent that that a plurality of questions sets having different question types can be created, including a first type that takes three or more values and a second type that takes two values.). As described above, it would have been obvious to modify the apparatus taught by Zeiler so as to comprise a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets. Accordingly, the above-described combination of Zeiler and Zadeh is further considered to teach an information processing apparatus like that of claim 7.
Regarding claim 8, and like noted above, Zeiler generally describes a system for associating context information, e.g. labels, with individual multimedia items such as images (see e.g. paragraph 0004). Like claimed, Zeiler particularly teaches:
creating a plurality of question sets, the plurality of question sets each being composed of a question and a plurality of labels indicating answers that are selectable for the question (see e.g. paragraphs 0015, 0036 and 0046: like noted above, Zeiler teaches that the system comprises a task component configured to generate one or more tasks and/or sets of tasks to be performed by one or more users via a user interface. The task component selects one or more labels or sets of labels to correspond with individual tasks, and the tasks may entail presenting options to users for adding, removing, changing and/or confirming predicted label associations – see e.g. paragraphs 0006, 0015 and 0046. Zeiler particularly demonstrates that presenting a task to the user can entail presenting a question to the user and a plurality of labels indicating answers that are selectable for the question – see e.g. paragraph 0064 and FIG. 4. Such a question and associated options is considered a “question set” like claimed. Accordingly, the task component taught by Zeiler creates a plurality of questions sets, and wherein each question set is composed of a question and a plurality of labels indicating answers that are selectable for the question.); and
training a learning model using the plurality of question sets (see e.g. paragraphs 0020, 0038 and 0070: like noted above, Zeiler discloses that the system also comprises a learning component configured to generate a new machine learning prediction model and/or update an existing machine learning prediction model based on the user input for the tasks, i.e. based on the user input to the plurality of questions sets, inter alia. The learning component thus trains a learning model using the plurality of question sets.).
Accordingly, Zeiler teaches an information processing method similar to that of claim 8, but does not explicitly teach training a plurality of learning models using the question sets, as is required by claim 8.
Like noted above, Zadeh nevertheless generally teaches using a plurality of machine learning models (i.e. “detectors”) to identify and label objects within media content items, wherein each model is used to identify and label particular objects (see e.g. paragraphs 0003, 0036 and 0038). Zadeh suggests that specific question sets can be associated with each machine learning model, e.g. to enable a user to confirm the particular labels provided by the model, whereby the model can then be updated based on answers provided by users to the question sets (see e.g. paragraphs 0043, 0048, and 0078-0080, and FIG. 4D).
It would have been obvious to one of ordinary skill in the art, having the teachings of Zeiler and Zadeh before the effective filing date of the claimed invention, to modify the method taught by Zeiler so as to use a plurality of learning models like taught by Zadeh, wherein each learning model is associated with a particular one or more question sets, and whereby a plurality of such learning models are trained using a plurality of the question sets. It would have been advantageous to one of ordinary skill to utilize such a combination because it would enable a user to create and/or configure specific object detectors, as is evident from Zadeh (see e.g. paragraphs 0068-0069 and 0078). Accordingly, Zeiler and Zadeh are considered to teach, to one of ordinary skill in the art, an information processing method like in claim 8.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant’s disclosure. The applicant is required under 37 C.F.R. §1.111(C) to consider these references fully when responding to this action. In particular, the U.S. Patent to Sharma et al. cited therein describes techniques for generating and utilizing machine learning based adaptive instructions for annotation. The U.S. Patent to Ratti et al. cited therein describes a system that provides annotation techniques on smartphones for crowdsourced data labeling for AI training. The U.S. Patent Application Publication to Dasgupta et al. cited therein describes a model development environment (MDE) that allows a team of users to develop machine learning (ML) media models, and which provides a media data management interface that allows users to annotate and manage training data for models.
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/BTB/
7/11/2026
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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