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 .
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to claim 9, several of the features of this claim were known in the art as evidenced by Genno et al (Japanese Pub. No. JP 2021157497 A), which anticipates the limitations of parent claims 1 and 8, as discussed below. In particular, Genno discloses a data set that is a set of annotation data including classification target data (e.g., “central part of the image”) and a class (“initial class:”; e.g., “growth rate”; “class 0 group”) corresponding to the classification target data at pp. 4-5 of the English translation; to wit: “Apple fruit images were collected by taking regular photographs from the time the fruit began to grow until just before harvest. Each image was automatically annotated with the growth rate… The picture was taken so that the leaves and branches around the fruit were included in the picture. In the present embodiment, as shown in FIG. 1 (b ), the central portion of the image is cut out and used as a learning image… [T]he images taken on the first shooting date are set in the class 0 group, and the images taken after the first shooting date are set in the group in which the class is increased by one for each shooting date. Hereinafter, the class set by this method is referred to as an initial class. In this embodiment, the initial class is an annotation of each image before data cleansing.” And, at p. 7: “[A]ll the images of the image set for learning are tentatively classified into the initial class, and the annotation of each image is set.” However, Genno does not disclose an operation processing unit that executes deep learning processing by inputting image data of goods displayed on a display shelf from image data obtained by capturing the display shelf to the second model and identifies goods identification information of the goods
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: “learning processing unit”, “cleansing processing unit” in claims 1-11, “operation processing unit” in claim 4, “initial annotation processing unit” in claim 5.
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.
The aforesaid units correspond to a general purpose computer programmed to perform the functions ascribed to each unit. See, specification-as-filed at ¶ [0030].
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 10-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 10-11, taken as a whole, are directed to the description of a computer program. 35 U.S.C. 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these "four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of § 101 even if the subject matter is otherwise new and useful." In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Non-limiting examples of claims that are not directed to any of the statutory categories include products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. Software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8 and 10-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Genno et al (Japanese Pub. No. JP 2021157497 A).
With regards to claim 1, Genno discloses an information processing system that executes cleansing processing of annotation data in a data set at pp. 6-10.
Genno discloses the data set is a set of annotation data including classification target data (e.g., “central part of the image”) and a class (“initial class:”; e.g., “growth rate”; “class 0 group”) corresponding to the classification target data at pp. 4-5 of the English translation; to wit: “Apple fruit images were collected by taking regular photographs from the time the fruit began to grow until just before harvest. Each image was automatically annotated with the growth rate… The picture was taken so that the leaves and branches around the fruit were included in the picture. In the present embodiment, as shown in FIG. 1 (b ), the central portion of the image is cut out and used as a learning image… [T]he images taken on the first shooting date are set in the class 0 group, and the images taken after the first shooting date are set in the group in which the class is increased by one for each shooting date. Hereinafter, the class set by this method is referred to as an initial class. In this embodiment, the initial class is an annotation of each image before data cleansing.” And, at p. 7: “[A]ll the images of the image set for learning are tentatively classified into the initial class, and the annotation of each image is set.”
Genno discloses the information processing system comprising a learning processing unit that generates a model (e.g., “CNN”) by executing learning processing of deep learning using the data set at pp. 6-7 of the English translation; to wit: “[D]ata cleansing of an image set for learning is performed by using deep learning by a convolutional neural network (CNN)… [T]he images of the classified image set are divided into k groups… [O]ne of the k groups is set as a test image, and the other groups are collectively set as a learning image. In steps ST4 to ST5, the CNN is trained with the learning image. At this time, the CNN intentionally overfits the learning image.”
Genno discloses the information processing system comprising a cleansing processing unit that executes cleansing processing of annotation data in the data set at pp. 6-10 of the English translation. In particular:
Genno discloses the cleansing processing unit inputs the data to be classified (e.g., “current image set”) in the first annotation data in the first data set to a first model (e.g., “over-learned CNN”) generated by the learning processing unit using the first data set to output a second class (e.g., “class estimation”) at pp. 8-9 of the English translation; to wit: “[T]he over-learned CNN is used to perform class estimation by the over-learned CNN in order from the current image set (learning image+ test image) one by one.”
Genno discloses the cleansing processing unit performs the cleansing process of the first annotation data in the first data set by comparing the first class (e.g., “initial class” or “the currently belonging class”) with the second class (e.g., “class estimation”) at pp. 8-9; to wit: “[T]he over-learned CNN is used to perform class estimation by the over-learned CNN in order from the current image set (learning image+ test image) one by one. Then, when the class estimation result does not match the class to which the current affiliation belongs, it is determined that the class to which the present affiliation belongs is not the correct answer, and …, or the image is removed... [I]t is determined whether or not the difference (class estimation error) between the currently belonging class and the class estimation result by the over-learned CNN is within± y for the image whose class estimation result is incorrect. If the class estimation error exceeds± y (step ST7: No), the process proceeds to step ST8, and the erroneously estimated image is deleted from the current image set.”
With regards to claim 2, Genno discloses the cleansing processing unit inputs the data to be classified (e.g., “current image set”) in the first annotation data in the first data set to the first model (e.g., “over-learned CNN”) generated by the learning processing unit using the first data set to output a second class at pp. 8-9 of the English translation; to wit: “[T]he over-learned CNN is used to perform class estimation by the over-learned CNN in order from the current image set (learning image+ test image) one by one.”
Genno discloses the cleansing processing unit excludes the first annotation data from the first data set when the first class and the second class are compared and a predetermined condition is not satisfied at pp. 8-9; to wit: “[T]he over-learned CNN is used to perform class estimation by the over-learned CNN in order from the current image set (learning image+ test image) one by one. Then, when the class estimation result does not match the class to which the current affiliation belongs, it is determined that the class to which the present affiliation belongs is not the correct answer, and …, or the image is removed... [I]t is determined whether or not the difference (class estimation error) between the currently belonging class and the class estimation result by the over-learned CNN is within± y for the image whose class estimation result is incorrect. If the class estimation error exceeds± y (step ST7: No), the process proceeds to step ST8, and the erroneously estimated image is deleted from the current image set.”
Genno discloses the cleansing processing unit outputs a second data set using annotation data that has not been excluded among the first annotation data in the first data set at p. 10 of the English translation (“The ‘current image set’ at the time of step ST14 is an image set in which the modification (image removal and reclassification) of the image set is repeated k times, and the content of the modified image set is different from that of the initial image set.”)
With regards to claim 3, Genno discloses the learning processing unit generates a second model using a data set of a set of annotation data on which the cleansing processing has been executed at p. 4 of the English translation; to wit: “Further, if the Al is trained using the image set after data cleansing by the method of the present invention, it is possible to obtain an Al that performs image classification with high accuracy. In the present invention, at the same time as performing data cleansing using Al, deep learning of the Al is performed. Therefore, at the end of data cleansing, it is possible to simultaneously obtain an Al that performs image classification with high accuracy.”
With regards to claim 4, Genno discloses an operation processing unit that executes deep learning processing by inputting analysis target data as a processing target to the second model at p. 4 of the English translation; to wit: “Further, if the Al is trained using the image set after data cleansing by the method of the present invention, it is possible to obtain an Al that performs image classification with high accuracy. In the present invention, at the same time as performing data cleansing using Al, deep learning of the Al is performed. Therefore, at the end of data cleansing, it is possible to simultaneously obtain an Al that performs image classification with high accuracy.” See, also, pp. 4-5 of the English translation; to wit: “Apple fruit images were collected…In the present embodiment, as shown in FIG. 1 (b ), the central portion of the image is cut out and used as a learning image…” And, at p. 7: “[A]ll the images of the image set for learning are tentatively classified into the initial class, and the annotation of each image is set.” See, also, pp. 6-10.
With regards to claim 5, Genno discloses an initial annotation processing unit that executes predetermined classification processing on the classification target data (e.g., “central part of the image”), outputs the first class (e.g., “initial class” or “the currently belonging class”) corresponding to the classification target data, and outputs the first data set that is a set of first annotation data in which the classification target data and the first class are associated with each other at pp. 4-5 of the English translation; to wit: “Apple fruit images were collected by taking regular photographs from the time the fruit began to grow until just before harvest. Each image was automatically annotated with the growth rate… The picture was taken so that the leaves and branches around the fruit were included in the picture. In the present embodiment, as shown in FIG. 1 (b ), the central portion of the image is cut out and used as a learning image… [T]he images taken on the first shooting date are set in the class 0 group, and the images taken after the first shooting date are set in the group in which the class is increased by one for each shooting date. Hereinafter, the class set by this method is referred to as an initial class. In this embodiment, the initial class is an annotation of each image before data cleansing.” And, at p. 7: “[A]ll the images of the image set for learning are tentatively classified into the initial class, and the annotation of each image is set.”
With regards to claim 6, Genno discloses the classification processing (i.e., classification by shooting date) in the initial annotation data processing unit and the classification processing in the cleansing processing unit (“CNN”) are different classification processing at p. 5 and pp. 7-10.
With regards to claim 7, Genno discloses the classification target data is data obtained by cutting out a part of original data at pp. 4-5 of the English translation; to wit: “Apple fruit images were collected by taking regular photographs from the time the fruit began to grow until just before harvest. Each image was automatically annotated with the growth rate… The picture was taken so that the leaves and branches around the fruit were included in the picture. In the present embodiment, as shown in FIG. 1 (b ), the central portion of the image is cut out and used as a learning image…”
With regards to claim 8, Genno discloses an information processing system that executes cleansing processing of annotation data in a data set at pp. 6-10.
Genno discloses the data set that is a set of annotation data including image data of goods (fruit) and goods identification information (identified growth of fruit; “initial class:”; e.g., “growth rate”; “class 0 group”), the information processing system comprising: a learning processing unit that generates a model by executing learning processing of deep learning using the data set at pp. 4-5 and p. 7.
Genno discloses a cleansing processing unit that executes cleansing processing of annotation data in the data set, wherein the cleansing processing unit inputs image data of goods (fruit) in the first annotation data in the first data set to the first model (e.g., “overlearned CNN”) generated by the learning processing unit using the first data set to output a second goods identification information (e.g., “class estimation”) at pp. 8-9 of the English translation.
Genno discloses the cleansing processing unit performs the cleansing process of the first annotation data in the first data set by comparing first goods (fruit) identification information (identified growth of fruit; “initial class:”; e.g., “growth rate”; “class 0 group”) with the second goods (fruit) identification information (e.g., “class estimation”) at pp. 8-9 of the English translation.
Genno discloses the learning processing unit generates a second model by using a data set of a set of annotation data on which the cleansing processing has been executed at p. 4 of the English translation.
With regards to claim 10, the steps performed by the apparatus of this claim are anticipated by Genno for the same reasons as were provided in the discussion of claim 1, which recites an apparatus configured to perform these same steps.
With regards to claim 11, the steps performed by the apparatus of this claim are anticipated by Genno for the same reasons as were provided in the discussion of claim 8, which recites an apparatus configured to perform these same steps.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID F DUNPHY whose telephone number is (571)270-1230. The examiner can normally be reached 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID F DUNPHY/ Primary Examiner, Art Unit 2673
Prosecution Insights