DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is responsive to the above identified application filed February 16, 2024
Claims 1-15 are pending, all examined and rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-028840, filed on February, 27, 2023.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show estimating unit, extracting unit, controlling unit, storing unit, holding unit, inputting unit, outputting unit and image capturing unit as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
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 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 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:
estimating unit configured to estimate in claim 4,
searching unit configured to perform in claim 5,
extracting unit configured to extract in claim 6,
controlling unit configured to perform and storing unit configured to store in claim 7,
training unit configured to train and evaluating unit configured to evaluate in claim 8,
holding unit configured to hold in claim 9,
inputting unit configured to input and outputting unit configured to output in claim 10.
The claim limitation storing unit in claim 7 is not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 because it is interpreted as the storage device which is a hard disk drive (HDD), a solid-state drive (SDD) or an SD card as described in [0067] in the specification.
For the rest of the claim limitations, 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.
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 4-10 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 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.
Regarding claim 4, the claim recites “estimating unit configured to estimate… “. The disclosure does not provide adequate details of how the “estimating unit” performs its claimed function of estimating one of the plurality of tasks. 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 (FP 7.31.01)
Regarding claim 5, the claim recites “searching unit configured to perform…“ .The disclosure does not provide adequate details of how the “searching unit” performs its claimed function of searching for the image. 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 (FP 7.31.01)
Regarding claim 6, the claim recites “extracting unit configured to extract...”. The disclosure does not provide adequate details of how the “extracting unit” performs its claimed function of extracting the image from the training data. 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 (FP 7.31.01)
Regarding claim 7, the claims recites “controlling unit configured to perform …”. The disclosure does not provide adequate details of how the “controlling unit” performs its claimed function of rewriting the network structure of the recognition models. 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 (FP 7.31.01)
Regarding claim 8, the claim recites “training unit configured to train …”. The disclosure does not provide adequate details of how the “training unit” and “evaluating unit” performs their claimed functions of training the models and evaluating the inference accuracies. 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 (FP 7.31.01)
Regarding claim 9, the claim recites “holding unit configured to hold...”. The disclosure does not provide adequate details of how the “holding unit” performs its claimed function of holding the evaluation data, detection frame, and a partial image. 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 (FP 7.31.01)
Regarding claim 10, the claim recites “inputting unit configured to input ...”. The disclosure does not provide adequate details of how the “inputting unit” and “outputting unit” perform their claimed functions of inputting and outputting the evaluation data to the models. 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 (FP 7.31.01)
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 4-10 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 4, the claim recites “estimating unit configured to estimate…” 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 structure by which the “estimating unit” performs its recited function is not made clear from the teachings of the specification. There is no disclosure of any particular structure, either explicitly or inherently, to perform estimating one of the plurality of tasks. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which estimate structure or structures perform(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.
As to claim 5, the claim recites “searching unit configured to perform…” 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 function. In particular, the specification states the claimed function of performing searching for an image is performed by “searching.” There is no disclosure of any particular structure, either explicitly or inherently, to perform the searching for an image. The use of the term “searching” is not adequate structure for performing searching for image because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “searching” refers to looking for data, or a file and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which searching structure or structures perform(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.
As to claim 6, the claim recites “extracting unit configured to extract…” 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 function. In particular, the specification states the claimed function of performing an extraction an image from training data is performed by “extracting.” There is no disclosure of any particular structure, either explicitly or inherently, to perform a searching for an image. The use of the term “extracting” is not adequate structure for performing extracting an image because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “extracting” refers to getting a feature from plurality of features and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which extracting structure or structures perform(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.
As to claim 7, the claim recites “controlling unit configured to perform…” 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 function. In particular, the specification states the claimed function of controlling for modifying a network structure is performed by “controlling.” There is no disclosure of any particular structure, either explicitly or inherently, to perform control for modifying the network structure. The use of the term “controlling” is not adequate structure for performing controlling for modifying a network because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “controlling” refers to directing the system to do tasks and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which controlling structure or structures perform(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.
As to claim 8, the claim recites “training unit configured to train…” 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 function. In particular, the specification states the claimed function of training plurality of trained models is performed by “training.” There is no disclosure of any particular structure, either explicitly or inherently, to perform training the plurality of trained models. The use of the term “training” is not adequate structure for performing training plurality of trained models because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “training” refers to teaching the system to recognize patterns to make predictions and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which training structure or structures perform(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.
As to claim 8, the claim recites “evaluating unit configured to evaluate…” 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 function. In particular, the specification states the claimed function of evaluating inference accuracies of the plurality of trained models is performed by “evaluating.” There is no disclosure of any particular structure, either explicitly or inherently, to perform evaluating inference accuracies of the plurality of trained models. The use of the term “evaluating” is not adequate structure for performing evaluating inference accuracies of the plurality of trained models because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “evaluating” refers to comparing accuracies to a specific number and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which evaluating structure or structures perform(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.
As to claim 9, the claim recites “holding unit configured to hold…” 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 function. In particular, the specification states the claimed function of holding evaluation data, detection frames and a partial image is performed by “holding.” There is no disclosure of any particular structure, either explicitly or inherently, to perform holding evaluation data, detection frames and a partial image. The use of the term “holding” is not adequate structure for performing holding evaluation data, detection frames and a partial image because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “holding” refers to temporary storing data, files of the system and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which holding structure or structures perform(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.
As to claim 10, the claim recites “inputting unit configured to input…” 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 function. In particular, the specification states the claimed function of inputting evaluation data is performed by “inputting.” There is no disclosure of any particular structure, either explicitly or inherently, to perform inputting evaluation data. The use of the term “inputting” is not adequate structure for performing inputting evaluation data because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “inputting” refers to putting data to the computer and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which inputting structure or structures perform(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.
As to claim 10, the claim recites “outputting unit configured to output…” 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 function. In particular, the specification states the claimed function of outputting the result obtained by the plurality of trained models is performed by “outputting.” There is no disclosure of any particular structure, either explicitly or inherently, to perform outputting a result obtained by the plurality of trained models. The use of the term “outputting” is not adequate structure for performing outputting a result obtained by plurality of trained models because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term “outputting” refers to making predictions by the trained models and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which outputting structure or structures perform(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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. because the claimed invention is directed to an abstract idea without significantly more.
With regard to Claim 1,
Step 1, MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
An information processing apparatus comprising:
at least one processor; and
at least one memory coupled to the at least one processor, the memory storing instructions that, when executed by the processor, cause the processor to act as:
first determining unit configured to determine a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data; and
second determining unit configured to determine a second task to be combined with the first task based on a result of inference in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
Step 2A, Prong 1:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
An information processing apparatus comprising:
at least one processor; and
at least one memory coupled to the at least one processor, the memory storing instructions that, when executed by the processor, cause the processor to act as:
first determining unit configured to determine a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data; and
second determining unit configured to determine a second task to be combined with the first task based on a result of inference in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human mind can determine a task from among a plurality of tasks based on a result of inference in plurality of tasks mentally or with pen and paper. As such, the claim recites at least one abstract idea.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
Claim 1 recites:
An information processing apparatus comprising:
at least one processor; and
at least one memory coupled to the at least one processor, the memory storing instructions that, when executed by the processor, cause the processor to act as:
first determining unit configured to determine a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data; and
second determining unit configured to determine a second task to be combined with the first task based on a result of inference in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
This part of the eligibility analysis evaluates whether the claim as a whole integrates the
recited judicial exception into a practical application of the exception or whether the
claim is "directed to" the judicial exception. This evaluation is performed by (1)
identifying whether there are any additional elements recited in the claim beyond the
judicial exception, and (2) evaluating those additional elements individually and in
combination to determine whether the claim as a whole integrates the exception into a
practical application. See MPEP 2106.04(d).
The additional elements in this claim are “at least one processor”, “at least one memory coupled to the at least one processor [..]”, “first determining unit”, “second determining unit”. These elements are recited at a high level of generality and thus are generic computer components performing computer functions. See MPEP 2106.05(f).
Additionally, the additional elements of “a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data” and “a trained model executing the first task among the plurality of trained model” amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.5(g)
The claim recites the abstract ideas.
Step 2A, Prong 2 (No).
Step 2B
Claim 1 recites:
An information processing apparatus comprising:
at least one processor; and
at least one memory coupled to the at least one processor, the memory storing instructions that, when executed by the processor, cause the processor to act as:
first determining unit configured to determine a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data; and
second determining unit configured to determine a second task to be combined with the first task based on a result of inference in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
This part of the eligibility analysis evaluates whether the claim as a whole amount to
significantly more than the recited exception i.e., whether any additional element, or
combination of additional elements, adds an inventive concept to the claim. See M PEP
2106.05.
As explained with respect to Step 2A, the additional elements are “at least one processor”, “at least one memory coupled to the at least one processor [..]”, “first determining unit”, “second determining unit” which at best are insignificant extra-solution activity as recited at a high level of generality. See MPEP 2106.05(d).
Additionally, the additional elements of “a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data” and “a trained model executing the first task among the plurality of trained model” which at best are mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No)
Claim 1 is ineligible.
With respect to Claims 14 and 15,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale.
Dependent Claims:
Claims 2, 3, 4, 5, 6: These claims only recite further abstract ideas (mental process) and thus are ineligible.
Claims 10: These claims recite further generic computer component (“searching unit”, “extracting unit”, “inputting unit”, “outputting unit”) but also recite “perform a search [..] for an image of an object [..]”, “extract [..] from training data [..] an image [..]”, “input evaluation data [..]”, and “results obtained by [..] trained models [..]”.
With respect to Step 2A, Prong 2, this is mere data gathering recited at a high level of generality and thus in significant extra-solution activity. See MPEP 2106.05(g).
With respect to Step 2B, searching for an image from training data, extracting an image from data and inputting training data/outputting the result from training data have been found by the courts to be well-understood, routine and conventional activity. See MPEP 2106.05(d), subsection II.
Thus, these claims are ineligible.
Claims 7, 8, 9, 13: These claims recite further generic computer component (“controlling unit”, storing unit”, “training unit”, “evaluating unit”, “holding unit”, “image capturing unit”, “information processing apparatus”) and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
Claims 11, 12: These claims recite further mere instruction to apply the abstract ideas (trained models, plurality of tasks) and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-4, 6-15 are rejected under 35 U.S.C as being unpatentable over Standley, Trevor, et al. "Which tasks should be learned together in multi-task learning?." International conference on machine learning. PMLR, 2020, in view of Piao, U.S. PG Pub 20240428551, filed October 26, 2021.
With regard to claim 1,
Standley teaches at least one processor; and at least one memory coupled to the at least one processor, the memory storing instruction that, when executed by the processor, cause the processor to act as:
first determining unit configured to determine a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data. (See Page 4, Table 2 “Pairwise multi-task relationships in Setting 1” .See page 7, column 1, Section 5.3.2. PREDICT HIGHER-ORDER FROM LOWER-ORDER “Using this strategy, we can predict the performance of all networks with three or more tasks using the performance of all of the fully trained two task networks. First, simply train all networks with two or fewer tasks to convergence. Then predict the performance of higher-order networks, run network selection on both the trained and the predicted networks, then train the higher order networks from scratch.” – The two or fewer tasks used for training is the first determined task and it is chosen based on the performance of the networks(s))
second determining unit configured to determine a second task to be combined with the first task based on a result of inference. (See page 7, column 1, Section 5.3.2. PREDICT HIGHER-ORDER FROM LOWER-ORDER “Using this strategy, we can predict the performance of all networks with three or more tasks using the performance of all of the fully trained two task networks. First, simply train all networks with two or fewer tasks to convergence. Then predict the performance of higher-order networks, run network selection on both the trained and the predicted networks, then train the higher order networks from scratch.”. See Page 3, column 2, 3. Experimental Setup, Task Set “Task Set 2 includes Auto Encoder, Surface Normal Prediction again, Occlusion Edges, Reshading, and Principal Curvature.”. See Page 8, column 1, figure 3 “The task groups picked by each of our techniques for integer budgets between 1 and 5.” and paragraph 2 “This shows that the networks learned through multi-task learning were found to be best for three of our tasks (s, d, and e), whereas two of our tasks (n and k) are best solved individually.” – The “high order” combination is the second determinate task. The Surface normal prediction tasks is being used as part of the multi-task dataset but later, in conclusion, it is said to be best for individual training, so it was discarded/erroneous in the multi-task learning)
Standley does not explicitly disclose in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
Piao teaches in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models. (See [0036] – The first image/frame includes all the feature/object image of all the objects; means it also include an erroneously detected object by the first task)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine determining units based on the result of inference of Standley with the ability to detect object that is not the detection target as taught by Piao. One would be motivated to do so to extract features used as the outputs and used for the next tasks. [Piao – 0036].
With regard to Claim 2,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Standley further teaches first determining unit determines the first task from among the different tasks based on an inference accuracy of each of the plurality of trained models as a result of inference being performed on the evaluation data. (See Page 5, column 2, Section 5. Task Grouping Framework “Our goal is to find a set of networks, each of which is trained on a subset of the tasks, that results in the best overall loss within a given computational budget. We do this by considering the space of all possible task subsets, training a network for each subset, and then using each network’s performance to choose the best networks that fit within the budget.”.– The networks are chosen based on the performances/inference accuracy performed on data.)
With regard to Claim 3,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Standley further teaches the second determining unit determines the second task from among the different tasks based on the inference result of the first task and a result obtained by trained models executing tasks other than the first task each performing inference on the evaluation data corresponding to the first task. (See Page 7, column 1, 5.3.2 PREDICT HIGHER-ORDER FROM LOWER-ORDER “Do the performances of a network trained with tasks A and B, another trained with tasks A and C, and a third trained with tasks B and C tell us anything about the performance of a network trained on tasks A, B, and C? As it turns out, the answer is yes. Although this ignores complex task interactions and nonlinearities, a simple average of the first-order networks’ accuracies was a good indicator of the accuracy of a higher-order network. For example, if you have networks, a&b with losses 0.1&0.2, b&c with 0.3&0.4, and a&c with 0.5&0.6, the per-task loss estimate for a network with a&b&c would be a = (0:1 + 0:6)=2 = 0:35, b = (0:2 + 0:3)=2 = 0:25 and c = (0:4 + 0:6)=2 = 0:5. Using this strategy, we can predict the performance of all networks with three or more tasks using the performance of all of the fully trained two task networks. First, simply train all networks with two or fewer tasks to convergence. Then predict the performance of higher-order networks, run network selection on both the trained and the predicted networks, then train the higher order networks from scratch.” -The first determination is the pair A, B, the second determination is C, choosing C based on the performance/inference result of the pair A, B, and the inference results obtained by the pair A, C and pair B, C different from the pair A, B.)
With respect to claim 4,
As discussed with regard to Claim 1, Standley and Piao teach all of the limitations.
Piao further teaches estimating unit configured to estimate one of the plurality of tasks to which the inference result of the first task belongs using a classification model that has learned ground truth data corresponding to the plurality of tasks, wherein the second determining unit determines the second task from among the different tasks based on a result of the estimating by the estimating unit. (See [0030-0033]– the estimating unit estimates which class the object in the image belongs to using the results of the 3 inference tasks.)
Claim 5 is rejected under 35 U.S.C as being unpatentable over Standley, and Piao in view of Gu, U.S. PG Pub 20220147838, filed November 09,2020.
With respect to claim 5,
As discussed with regard to Claim 1, Standley and Piao teach all of the limitations.
Standley and Piao do not explicitly disclose searching unit configured to perform a search in training data corresponding to the different tasks for an image of an object that is similar to the object included in the inference result of the first task, wherein the second determining unit determines, as the second task, a task created based on a result of the searching by the searching unit and ground truth data corresponding to the result of the search.
Gu teaches searching unit configured to perform a search in training data corresponding to the different tasks for an image of an object that is similar to the object included in the inference result of the first task, wherein the second determining unit determines, as the second task, a task created based on a result of the searching by the searching unit and ground truth data corresponding to the result of the search. (See [0026] – the search engine/searching unit performs image-based searching for images that have similar relationships and dependencies between image objects of the query image (based on image of object that is similar to the object in the inference result). The searching results then are being used for another tasks.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the processing apparatus of Standley and Piao with the search engine as taught by Gu. One would be motivated to do so to retrieve more accurate images that have similar objects to the target object. [Gu - 0026].
With respect to claim 6,
As discussed with regard to claim 4, Standley and Piao teach all the limitations.
Piao further discloses extracting unit configured to extract, from training data corresponding to a task that is based on the result of the estimating by the estimating unit, an image including a second object of the task that has a predetermined size, wherein the second determining unit determines, as the second task, a task created based on a result of the extracting by the extracting unit and ground truth data corresponding to the result of the extracting. (See [0036], [0051] – the training unit uses the results of the 3 inferences tasks/estimating unit to extract the result/image as the first feature that includes the result/image that is common to the 3 inferences tasks. Before doing that, the resizing unit matches all the sizes of the values so they can have same/predetermined sizes.)
With respect to claim 7,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Standley further teaches controlling unit configured to perform control for modifying a network structure of the trained model executing the first task based on the first task and the second task, and adding second training data corresponding to the second task to first training data corresponding to the first task; (See page 1, column 1, Figure. 1 “Given five example tasks to solve, there are many ways that they can be split into task groups for multi-task learning. How do we find the best one?”. See Page 3, column 2, Architectures “In all experiments, we used a standard encoder-decoder architecture with a modified Xception (Chollet, 2017)) encoder.”. See Page 4, column 1, Comparison “Another five single task networks were trained, each having a half-size (1=2-SNT) encoder and a standard decoder” – after grouping the second task to the first task, the decoder of the second task is added to the encoder of the first task. Thus, the network is modified by the processor.)
Piao further teaches storing unit configured to store a learning parameter and the network structure of the trained model executing the first task. (See [0035], [0027] – the storing unit is a storage device with hard disk, stores main processing information/network structure and image information/learning parameters used for training).
With regard to claim 8,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Standley further teaches training unit configured to train the plurality of trained models using a training dataset corresponding to the plurality of tasks; (See page 3, column 1, 3. Experimental Setup, Dataset: “We perform our study using the Taskonomy dataset (Zamir et al., 2018), which is currently the largest multi-task dataset for computer vision with diverse tasks”. See page 3, column 2, Training Details “All training was done using PyTorch (Paszke et al., 2017) with Apex for fp16 acceleration”- The models are being trained using Pytorch with Apex, Taskonomy is the dataset for training.)
and evaluating unit configured to evaluate inference accuracies of the plurality of trained models based on a result obtained by performing inference on the evaluation data and ground truth data corresponding to the evaluation data. (See Page 4, table 1, 2, 3 and See Page 7, column 2, section 6. Task grouping evaluation, Figure 2 “Performance/inference time trade-off for various methods in Setting 1”. See page 8, Figure 4 “Performance/inference time trade-off in Setting 2.”, Figure. 5 “Performance/inference time trade-off in Setting 3.”, Figure. 6 “Performance/inference time trade-off in Setting 4.”.- performance/inference results are evaluated and displayed under performance tables).
With respect to claim 9,
As discussed with regard to claim 5, Standley, Piao, and Gu teach all of the limitations.
Piao further teaches holding unit configured to hold: the evaluation data corresponding to the first task; a detection frame of at least one of the object erroneously inferred in the evaluation data corresponding to the first task by the trained model executing the first task and a correct detection object correctly inferred in the evaluation data corresponding to the first task by the trained model executing the first task; and a partial image of at least one of the object and the correct detection object. (See [0089-0091] – the first feature value includes common frames of the inference tasks, which includes the detection frame of at least one of the objects of a specific task/first task and at least one object that does not belong to the mentioned task/erroneous object. The first value also can be an image/partial image that contains at least one of the other task’s objects and the object of the first task. The training unit also generates trained models to train based on image/evaluation data; therefore the training unit also hold evaluation data.)
With regard to claim 10,
As discussed with regard to claim 7, Standley and Piao teach all of the limitations.
Piao further teaches inputting unit configured to input evaluation data corresponding to the different tasks to the plurality of trained models based on a result of the storing by the storing unit; (See [0039] – the training unit causes the model to take images (stored in the storing unit) as input to perform machine learning.)
outputting unit configured to output a result obtained by the plurality of trained models performing inference on the evaluation data corresponding to the different tasks. (See [0039] – the models take the estimation results of the 3 inference tasks as output.)
With regard to claim 11,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Standley further teaches the plurality of trained models each comprise a shared layer that executes same processing for the different tasks. (See page 3, column 2, Architecture “In all experiments, we used a standard encoder-decoder architecture with a modified Xception (Chollet, 2017)) encoder. Our choice of architecture is not critical and was chosen for its reasonably low inference time. All max-pooling layers were replaced by 2 x 2 convolution layers with a stride of 2, similar to (Chen et al., 2018a).” – All the networks use the same/share encoder, with 2x2 convolution layers with a stride of 2 that executes the same processing for different tasks)
With respect to claim 12,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Piao further teaches the plurality of tasks include a head detection task, an animal detection task, a ball detection task, an upper body detection task, and a vehicle detection task. (See [0030-0031] – the object detects a person, an animal/dog, a thing/vehicle, and skeletal information includes body of a person/head/upper body.)
With respect to claim 13,
As discussed with regard to claim 1, Standley and Piao teach all of the limitations.
Piao further teaches an image capturing apparatus comprising: an image capturing unit that captures an image of a subject. (See [0025], [0027] – the camera captures the images at different shooting times).
With regard to claims 14 and 15,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale.
Conclusion
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/MICHAEL HOANG LE/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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