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 .
Response to Amendment
This Office Action is in response to the amendment filed on 6/29/26
The applicant’s remarks and amendments to the claims were considered and results as
follow: THIS ACTION IS MADE FINAL.
3. Claims 35, 68 and 69 have been amended. No claims have been cancelled. Claims 70-72 have been added. As a result, claims 35-72 now pending in this office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created
doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the
unjustified or improper timewise extension of the “right to exclude” granted by a patent
and to prevent possible harassment by multiple assignees. A nonstatutory double
patenting rejection is appropriate where the conflicting claims are not identical, but at
least one examined application claim is not patentably distinct from the reference
Claims 1-22 are present in this application. Claims 1-22 are pending in this office
claim(s) because the examined application claim is either anticipated by, or would have
been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46
USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed.
Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum,
686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619
(CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may
be used to overcome an actual or provisional rejection based on nonstatutory double
patenting provided the reference application or patent either is shown to be commonly
owned with the examined application, or claims an invention made as a result of
activities undertaken within the scope of a joint research agreement. See MPEP §
717.02 for applications subject to examination under the first inventor to file provisions
of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for
applications not subject to examination under the first inventor to file provisions of the
AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 35-72 of the instant application is rejected on the ground of nonstatutory
obviousness type double patenting as being unpatentable over claims 35-41 and 43-63 of Patent No. US 12, 277, 531. Although the claims at issue are not identical,
they are not patentably distinct from each other because claims 35-41 and 43-63 of Patent No. US 12, 277, 531 recite the elements of Claims 35-69 of the Instant
application No. 19/079,875.
Both claim features of the instant application No. 19/079,875 and Patent No. US 12, 277, 531 can be compared as follows:
Instant Application 19/079,875
Patent No. US 12, 277, 531
35. (New) A method for classifying food items, including: capturing one or more sensor data relating to a disposal event, wherein the disposal event includes a food item being placed into a waste receptacle by a user, and the disposal event is one of a plurality of disposal events relating to the waste receptacle such that food items from multiple disposal events are disposed of consecutively within the waste receptacle without the waste receptacle being emptied, wherein the one or more sensor data includes image data captured from an image sensor above the waste receptacle and wherein the image data includes image data of the food item placed, during the disposal event, within the waste receptacle including food items from one or more previous disposal events, and wherein one or more sensor data includes weight data obtained from the one or more sensor data; classifying the food item using the image data, at least in part, automatically using a model trained on, at least, image sensor data; wherein the image data is processed during the classification to isolate new objects within the image data; and associating the food item with the weight data after the classification of the food item.
35. (Currently Amended) A method for classifying food items, including:
capturing one or more sensor data relating to a disposal event, wherein the disposal event includes a food item being placed into a waste receptacle by a user, and the disposal event is one of a plurality of disposal events relating to the waste receptacle such that food items from multiple events are disposed of consecutively within the waste receptacle without the waste receptacle being emptied, wherein the one or more sensor data includes image data captured from an image sensor above the waste receptacle and wherein the image data includes image data captured while the food item is being placed within the waste receptacle or after the food item has been placed within the waste receptacle onto food items within one or more previous disposal events, and wherein the one or more sensor data includes weight data: processing the image data to isolate new objects within the image data compared to previously captured image data using segmenter; and classifying the food item using at least the processed image data, at least in part, automatically using a model trained on sensor data, and using the were data.
36. (New) The method as claimed in claim 35, wherein the image data is processed by a method including the step of: identifying new image data between an image of a previous disposal event and an image of the disposal event using a segmenter such that the food item is isolated from food items in previous disposal events.
36. (Currently Amended) The method as claimed in claim 35, wherein the image data is processed by a method including the step of: identifying new image data between an image of a previous disposal event and an image of the disposal event using the segmenter such that the food item is isolated from food items in previous disposal events.
37. (Previously Presented) The method as claimed in claim 36, wherein the image data is processed by a method including the step of: combining the identified new image data with the image of the disposal event to result in a combined data; wherein the food item is classified using at least the combined data.
37. (New) The method as claimed in claim 36, wherein the image data is processed by a method including the step of: combining the identified new image data with the image of the disposal event to result in a combined data; wherein the food item is classified using at least the combined data.
38. (New) The method as claimed in claim 36, wherein the image data is compared to earlier image data captured before the disposal event, and a difference between the two image data is used to classify the food item.
38. (Previously Presented) The method as claimed in claim 36, wherein the image data is compared to earlier image data captured before the disposal event, and a difference between the two image data is used to classify the food item.
39. (New) The method as claimed in claim 35, wherein the image sensor is a visible light camera.
39. (Previously Presented) The method as claimed in claim 36, wherein the image sensor is a visible light camera.
40. (New) The method as claimed in claim 35, further including detecting a waste receptacle within the image data to isolate potential food items.
40. (Previously Presented) The method as claimed in claim 36, further including detecting a waste receptacle within the image data to isolate potential food items.
41. (New) The method as claimed in claim 35, wherein the image data includes a plurality of concurrently captured images of the food item captured over time during the plurality of disposal events and wherein the food item is classified using an image selected from the plurality of images using a good image selection module.
41. | (Currently Amended) The method as claimed in claim 36, wherein the image data includes a plurality of concurrently captured images of the food item captured over time during the
plurality of disposal events and wherein the food item is classified using an image selected from the plurality of images using a good image selection module.
48. (New) The method as claimed in claim 35, wherein the model is a neural network.
43. (Previously Presented) The method as claimed in claim 35, wherein the model is a neural network.
49. (New) The method as claimed in claim 35, wherein the food item is classified, at least in part, by an inference engine using the model.
44. (Previously Presented) The method as claimed in claim 35, wherein the food item is classified, at least in part, by an inference engine using the model.
50. (New) The method as claimed in claim 49, wherein the inference engine also uses historical pattern data to classify the food item.
45. (Previously Presented) The method as claimed in claim 44, wherein the inference engine also uses historical pattern data to classify the food item.
51. (New) The method as claimed in claim 50, wherein the inference engine also uses one or more selected from a set of time, location, and immediate historical data to classify the food item.
46. (Previously Presented) The method as claimed in claim 45, wherein the inference engine also uses one or more selected from the set of time, location, and immediate historical data to classify the food item.
52. (New) The method as claimed in claim 50, wherein the inference engine determines a plurality of possible classifications for the food items.
47. (Previously Presented) The method as claimed in claim 45, wherein the inference engine determines a plurality of possible classifications for the food items.
53. (New) The method as claimed in claim 52, wherein a number of possible classifications is based upon a probability for each possible classification exceeding a threshold.
48. (Previously Presented) The method as claimed in claim 47, wherein a number of possible classifications is based upon a probability for each possible classification exceeding a threshold.
54. (New) The method as claimed in claim 52, wherein the plurality of possible classifications is displayed to a user on a user interface.
49. (Previously Presented) The method as claimed in claim 47, wherein the plurality of possible classifications is displayed to a user on a user interface.
55. (New) The method as claimed in claim 54, wherein an input is received by the user to classify the food item.
50. (Previously Presented) The method as claimed in claim 49, wherein an input is received by the user to classify the food item.
56. (New) The method as claimed in claim 52, wherein the inference engine classifies the food item in accordance with the possible classification with the highest probability.
51. (Previously Presented) The method as claimed in claim 47, wherein the inference engine classifies the food item in accordance with the possible classification with the highest probability.
57. (New) The method as claimed in claim 35, wherein the model is trained by capturing sensor data relating to historical food item events and users classifying the food items during the historical food item events.
52. (Previously Presented) The method as claimed in claim 35, wherein the model is trained by capturing sensor data relating to historical food item events and users classifying the food items during the historical food item events.
58. (New) The method as claimed in claim 57, wherein the sensor data relating to historical food item events are captured and the users classify the food items during the historical food item events at a plurality of local food waste devices.
53. (Previously Presented) The method as claimed in claim 52, wherein the sensor data relating to historical food item events are captured and the users classify the food items during the historical food item events at a plurality of local food waste devices.
59. (New) The method as claimed in claim 35, wherein the sensor data is captured at a local device within a commercial kitchen and the disposal event is a commercial kitchen event.
54. (Previously Presented) The method as claimed in claim 35, wherein the sensor data is captured at a local device within a commercial kitchen and the disposal event is a commercial kitchen event.
60. (New) The method as claimed in claim 59, wherein a dynamic decision is made to classify the food item at the local device or a server.
55. (Previously Presented) The method as claimed in claim 54, wherein a dynamic decision is made to classify the food item at the local device or a server.
61. (New) The method as claimed in claim 35, wherein the weight data is obtained from a weight sensor.
56. (Currently Amended) The method as claimed in claim 35, wherein weight data is captured from a weight sensor.
63. (New) The method as claimed in claim 35, wherein the food item is classified, at least in part, automatically using a plurality of models trained on sensor data.
57. (Previously Presented) The method as claimed in claim 35, wherein the food item is classified, at least in part, automatically using a plurality of models trained on sensor data.
64. (New) The method as claimed in claim 61, wherein a first model of the plurality of models is trained on sensor data from global food item events.
58. (Previously Presented) The method as claimed in claim 57, wherein a first model of the plurality of models is trained on sensor data from global food item events.
65. (New) The method as claimed in claim 64, wherein a second model of the plurality of models is trained on sensor data from local food item events.
59. (Previously Presented) The method as claimed in claim 58, wherein a second model of the plurality of models is trained on sensor data from local food item events.
66. (New) The method as claimed in claim 35, wherein the sensor data includes sensor data captured over, at least part of, a duration of the plurality of disposal events.
60. (Previously Presented) The method as claimed in claim 35, wherein the sensor data includes sensor data captured over, at least part, a duration of the plurality of disposal events.
Conclusions/Points of Contacts
THIS ACTION IS MADE FINAL. 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 MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 5712724078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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