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 .
DETAILED ACTION
The response received on 1/28/2026 has been placed in the file and was considered by the examiner. An action on the merit follows.
Response to Amendment
The amendments filed on 2026 January 28 have been fully considered. Response to these amendments is provided below.
Summary of Amendment/ Arguments and Examiner’s Response:
The applicant has amended the claims and has argued that the prior art reference does not teach the amended limitations.
The examiner agrees. The claims are newly considered, below.
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 1-20 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. Claims 1, 8 and 15 state that the object identification rule is based on the input “that encodes the indicated relationship…” and further that the generating of the rule updates at least one of the claimed grouping of images or relations between clusters. The examiner cannot find any support in the specification for such limitations. The specification briefly states that there is an autoencoder that may be used as the self-supervised model (paragraph 27 of the PGPub), and that the sensed data is encoded (paragraph 81 of the PGPub). Furthermore, no reference to the “object type identification rule” updating the claimed data can be found in the specification. If the applicant can point to where in the specification the amended limitations are specifically taught, this rejection will be withdrawn.
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the clusters” in line 15, twice,18 and 19. It is unclear as to which clusters the applicant is referring to, because the applicant previously claims many sets of different clusters.
Claim 8 recites the limitation “the clusters” in line 19, 20, 22-23 and 23. It is unclear as to which clusters the applicant is referring to, because the applicant previously claims many sets of different clusters.
Claim 15 recites the limitation “the clusters” in line 23, 24, 26-27 and 27. It is unclear as to which clusters the applicant is referring to, because the applicant previously claims many sets of different clusters.
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.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over U.S. Patent Application Publication No. 20190034557 (Alsallakh et al) in view of U.S. Patent No.11544505 (Beach et al).
Regarding claim 1, Alsallakh et al discloses a method for training a machine learning model (fig. 4), the method comprising: receiving a training dataset that includes a plurality of images, sample images (fig. 1, item 32, fig. 4, item 310); identifying, by a machine learning model/ image classification model (fig. 4, item 320), a first subset of images of the plurality of images, wherein the first subset of images includes images associated with a first object type, i.e. the subset of images that receive the same classification output from the image classification model (fig. 4, item 320); grouping the first subset of images into a first image group associated with the first object type, either by grouping the subset as the same class (fig.4, item 320), or by grouping the images of similar classes (fig. 4, item 330); generating, for display, a first user interface (fig. 4, item 320, fig. 10) that includes a rank matrix (fig. 10) including a first aspect that represents images of the first image group, i.e. any aspect of fig. 10 including the color of the image, the placement of the image group or the number of images of the image group represented by item 426 of fig. 10, wherein the rank matrix further includes a plurality of respective clusters of object types, i.e. each of the clusters of fig. 10, item 426, and a value indicating a number of images associated with each respective cluster (fig. 10, item 430); receiving, at the first user interface, input indicating user feedback associated with the rank matrix, i.e. user inputs defining hierarchy (page 4, paragraph 42, page 5, paragraph 47), filtering of the confusion matrix (page 6, paragraph 55, page 7, paragraphs 62, 65), or reordering neurons (page 7, paragraph 67), and that clusters of object types are represented in the rank matrix (fig. 10), generating an object type identification rule based on the input, i.e. the rules being trained through epochs in the adapted training process (page 10, paragraph 92) or the architecture rules of page 8, paragraph 71, page 10, paragraph 93; and training the machine learning model based on the object type identification rule (page 8, paragraph 71, pages 10, paragraphs 92-96).
Alsallakh et al does not disclose expressly the user feedback indicates a relationship between at least two clusters of object types, the relationship indicating at least one of (i) the clusters are to be merged, (ii) a cluster is to be split into multiple clusters, or (iii) the clusters are to be more closely associated or not associated, the generated identification rule is based on the input indicating user feedback that encodes the indicated relationship and updates at least one of (i) the groupings of images among the clusters of object types, or (ii) relations between the clusters of object types; and training is by using the updated grouping or relations as weak supervision signals to update parameters of the machine learning model.
Beach et al discloses the user feedback/ user input (col. 2, lines 12-13, col. 1, line 43) indicates a relationship between at least two clusters of object types, the relationship indicating at least one of (i) the clusters are to be merged (col. 1, lines 41-42), (ii) a cluster is to be split into multiple clusters, or (iii) the clusters are to be more closely associated or not associated, the generated identification rule, i.e. the rule that the clusters belong to the same cluster(col. 1, lines 41-42) is based on the input indicating user feedback that encodes the indicated relationship, i.e. into the encoded image group described in col. 1, lines 45-49, or even the encoded relationship of the clusters should be combined of col. 1, lines 42-43/ positive feedback of col. 2 lines 22-24, and updates at least one of (i) the groupings of images among the clusters of object types (col. 1, lines 42-43, col. 2, lines 19-20), or (ii) relations between the clusters of object types; and training is by using the updated grouping or relations (col. 1, lines 49-51, col. 2, lines 16-20) as weak supervision signals, because the signals are weakly supervised by only considering parts of the clusters/ representative images and not every image (fig. 2, item 204) to update parameters of the machine learning model, any of the parameters that are updated in the updating of the object recognition model of col. 2, lines 16-17, col. 1, liens 49-52, fig. 2, item 216.
Alsallakh et al and Beach et al are combinable because they are from the same field of endeavor, i.e. image clustering.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to allow the user to modify clusters.
The suggestion/motivation for doing so would have been to provide a more robust recognition by allowing corrections.
Therefore, it would have been obvious to combine the method of Alsallakh et al with the user input of Beach et al to obtain the invention as specified in claim 1.
Regarding claim 2, Alsallakh et al discloses displaying, at the first user interface, a second user interface that includes a visualization of the first image group, i.e. the additional interfacing in fig. 2, items 210, 260, 230, which includes the visualization of item 250 which includes the first image group.
Regarding claim 3, Alsallakh et al discloses determining, based on a number of object types associated with the first image group, a size of the visualization of the first image group (page 6, paragraph 49).
Regarding claim 4, Alsallakh et al discloses determining a similarity factor based on the first image group and at least one other image group (page 6, paragraph 51, page 4, paragraph 42).
Regarding claim 5, Alsallakh et al discloses generating a graphical representation of the first image group (fig. 6) based on, at least, a size of the first image group and the similarity factor (page 6, paragraph 49-50).
Regarding claim 6, Alsallakh et al discloses receiving, via the first user interface, a selection of a region of the rank matrix (pages 9-10, paragraph 89).
Regarding claim 7, Alsallakh et al discloses generating an output that includes a representative image from an image group associated with the selected region, by filtering thumbnails to those that are selected, or sorting the thumbnails (page 9, paragraph 89).
Claim 8 is rejected for the same reasons as claim 1. Thus, the arguments analogous to that presented above for claim 1 are equally applicable to claim 8. Claim 8 distinguishes from claim 1 only in that claim 8 is a system claim for identifying at least one object type in at least one image using a machine learning model, the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to carry out the method of claim 1, with the additional step of identifying, using the machine learning model, at least one aspect of at least one image that corresponds to at least one of the first object type and another object type or a plurality of object types, wherein the at least one image is provided to the machine learning model as an input. Alsallakh et al discloses a system (fig. 1) for identifying at least one object type (fig. 4)in at least one image using a machine learning model (fig. 1, item 24), the system comprising: a processor (fig. 1, item 14); and a memory (fig. 1, item 16) including instructions (fig. 1, item 24) that, when executed by the processor, cause the processor to carry out the method of claim 1, with the additional step of identifying, using the machine learning model, at least one aspect of at least one image that corresponds to at least one of the first object type and another object type or a plurality of object types, because the images are analyzed for features that correspond to multiple object types (page 4, paragraph 39), wherein the at least one image is provided to the machine learning model as an input (fig. 4, item 310).
Claims 9-14 are rejected for the same reasons as claims 2-7 respectively. Thus, the arguments analogous to that presented above for claims 2- 7 are equally applicable to claims 9- 14. Claims 2- 7 distinguishes from claims 9- 14 only in that they have different dependencies, both of which have been previously rejected, and that claims 13 and 14 recite a selection of a row. Since Alsallakh et al discloses images are selected from the rank matrix (pages 9-10, paragraph 89) that are comprised of rows (fig. 10), Alsallakh et al discloses selection of a row.
Claim 15 is rejected for the same reasons as claim 5. Thus, the arguments analogous to that presented above for claim 5 are equally applicable to claim 15. Claim 15 distinguishes from claim 5 only in that claim 15 is an apparatus claim for training a machine learning model, the apparatus comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to carry out the method of claim 5. Alsallakh et al teaches further this feature, i.e. an apparatus (fig. 1) for training a machine learning model, the apparatus comprising: a processor (fig. 1, item 14); and a memory including instructions (fig. 1, item 16, 24) that, when executed by the processor, cause the processor to carry out the method (fig. 4).
Claims 16-18 are rejected for the same reasons as claims 9-11, respectively. Thus, the arguments analogous to that presented above for claims 9-11 are equally applicable to claims 16-18. Claims 16-18 distinguish from claims 9-11 only in that they have different dependencies, both of which have been previously rejected. Therefore, prior art applies.
Regarding claim 19, Alsallakh et al discloses to identifying, using the machine learning model, at least one aspect of at least one image that corresponds to at least one of the first object type and another object type of a plurality of object types, because the images are analyzed for features that correspond to multiple object types (page 4, paragraph 39).
Regarding claim 20, Beach et al discloses an image capturing device of the system (fig. 1, item 106), and that the at least one image capturing device is associated with at least one of a manufacturing machine, a power tool, an automated personal assistant, a domestic appliance, surveillance system (fig. 1, item 106, “Surveillance Devices”), and a medical imaging system.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN YUAN DULANEY whose telephone number is (571)272-2902. The examiner can normally be reached M1:9am-5pm, th1:9am-1pm, fri1 9am-3pm, m2: 9am-5pm, t2:9-5 th2:9am-5pm, f2: 9am-5pm.
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/KATHLEEN Y DULANEY/Primary Examiner, Art Unit 2666 2/9/2026