DETAILED ACTION
This is a Final office action on the merits in application number 18/740,057. This action is in response to Applicant’s Amendments and Arguments dated 2/25/26. Claims 1, 10, 11, and 16 were amended and Claims 8, 9, 14, 15, 19 and 20 were cancelled. Claims 1-7, 10-13 and 16-18 are pending and have been examined on the merits.
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 Arguments
Applicant asserts on page 11 of the Remarks dated 2/25/26 that the art of record does not teach the amended claims. As necessitated by amendment, and as discussed in the 35 USC 103 rejection, infra, Examiner now holds that the amended claims are taught by the art of record in view of Prakash.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2019/0371458 (Gonzalez) in view of U.S. Patent 11,443,275 (Prakash).
Regarding Claims 1, 11 and 16:
Gonzalez teaches a system that uses computer vision to determine the location of items in a hospital and track their location and uses machine learning to identify the items and update an inventory database using this information. Gonzalez teaches : A computer-implemented method comprising: receiving, with at least one processor, image data from at least one imaging device, the image data associated with at least one image of at least one item in at least one room of a hospital; ([0018] “An imaging and tracking device as disclosed herein is capable of capturing information indicative of a current inventory quantity within or on a furniture unit in real-time through one or more sensors (e.g., an image sensor, a motion sensor, or another sensor, or a combination thereof). For example, the sensors may be used to recognize inventory items stored within or on the furniture unit based on detected appearances of the inventory items (e.g., item shapes, text content, graphic content, item sizes, or the like) locations of those inventory items within or on the furniture unit.”).
determining, with at least one processor, at least one location of the at least one item based at least partly on at least one position of the at least one imaging device; ([0018] “locations of those inventory items within or on the furniture unit”).
inputting, with at least one processor, at least a portion of the image data to at least one image classification machine-learning model, the at least one image classification machine-learning model being trained at least partly on a set of images of items associated with an inventory of the hospital; ([0054] “the software application can use the submitted information to train a machine learning model (e.g., a neural network or other intelligence approach) to recognize the retrieved inventory item” and [0033] “hospital”).
determining, with at least one processor, at least one item identifier of the at least one item based on at least one output of the at least one image classification machine-learning model; ([0054] “the software application can use the submitted information to train a machine learning model (e.g., a neural network or other intelligence approach) to recognize the retrieved inventory item”).
determining, with at least one processor, at least one item record associated with the at least one item in at least one database based on the at least one item identifier; ([0028] “The database 110 can store records relating to inventory supplies (e.g., the inventory 112) which are or may be monitored using the imaging and tracking device 102 or by a different imaging and tracking device within the furniture unit 104 or within a different furniture unit”).
and updating, with at least one processor, the at least one item record in the at least one database, wherein updating the at least one item record comprises: updating at least one last known location in the at least one item record based on the at least one location of the at least one item. ([0005] “The software application uses the signal to automatically update a database record associated with the retrieved inventory item within a database” and [0057] “a shelf location (e.g., indicating the furniture unit of the health care clinic from which the inventory item was retrieved and/or the location at which that inventory item was stored within or on that furniture unit)”).
While Gonzalez teaches maintaining an object inventory using image sensors to obtain image data of objects in a hospital (see at least [0018]) and teaches using machine learning to identify the objects (see at least [0054]), Gonzales does not specifically teach: wherein the image data comprises a stream of images; Prakash, also in the inventory arts, teaches this: ([Column 3, line 20] “video”).
Prakash also teaches: tracking, with at least one processor, the at least one item throughout the at least one room based on the stream of images; ([Column 4, lines 33-39] “After the user has been identified, the inventory management system may locate the user throughout the facility, with the user's knowledge and consent. By maintaining location data of the user, the inventory management system is able to accurately associate events that take place within the facility with the user. The inventory management system may identify these events using image data of the user”).
Prakash also teaches: determining, with at least one processor, at least one identification of at least one human in the at least one room based on the image data; ([Column 4, line 28-29] “may be identified using facial-recognition techniques applied to image data of the user”).
Prakash also teaches: associating the at least one item record with at least one patient identifier or at least one clinician identifier based on the at least one identification of the at least one human. ([Column 2, line 66 – Column 3, line 11] “artificial neural networks, classifiers, and so forth, may be used to process the image data of an event and identify the item that was removed from the inventory location, identify the users after they move apart, disambiguate if the user picked or placed an item from an inventory location, and so forth. The inventory management system may be automated to provide the output data during operation. For example, the inventory management system may automatically identify an item removed from an inventory location as well as a user that removed the item. In response, the inventory management system may automatically update a virtual shopping cart of that particular user”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to use the computer vision based inventory tracking techniques to associate a person with an item of interest, as taught by Prakash, in the medical inventory system taught by Gonzalez, due to predictably improved clinician accountability and records accuracy. Examiner notes that Prakash specifically teaches that the system can be used for pharmaceuticals (see at least [Column 14, line 11]) and teaches in at least [Column 17, lines 54-67] that the inventory tracking system is not specific to any type of inventory.
Claims 2-5, 12-13 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2019/0371458 (Gonzalez) in view of U.S. Patent 11,443,275 (Prakash) in view of U.S. Patent publication 2011/0173028 (Bond)
Regarding Claims 2, 12 and 17:
Gonzalez in view of Prakash teaches all of the elements of Claims 1, 11 and 16. Gonzalez also teaches: (Original) The computer-implemented method of claim 1, wherein the at least one item is a plurality of items in the at least one room of the hospital, ([0006] “The image sensor captures images of inventory items stored within a furniture unit to which at least a portion of the imaging and tracking device is coupled” and [0033] “hospital”).
and wherein the at least one item record is a plurality of item records, the method further comprising: determining, with at least one processor, a plurality of locations of the plurality of items based on the plurality of item records; ([0019] “The real-time detection of changes to inventory items stored within or on a furniture item represents an improvement in inventory tracking computing technology, for example, based on the sensors included within the imaging and tracking device, the processing capabilities of the imaging and tracking device and/or of the server which runs the software application (e.g., in enumerating inventory items using real-time image data and detecting changes to inventory items based on such enumerations), and based on other improvements demonstrated throughout this disclosure. The updating of database records associated with inventory items detected to have changed (e.g., by a physical retrieval thereof from a furniture unit)”).
While Gonzalez teaches creating and using a database, Gonzalez does not specifically teach an inventory report. Gonzalez does not specifically teach: and generating, with at least one processor, an inventory report of the hospital based on the plurality of items and the plurality of locations. Bond teaches an inventory system for a medical facility. Bond teaches: ([0155] “inventory reports” and [0156] “storage location” and [0161] “item name”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez to output an inventory report, as taught by Bond, because Gonzalez already teaches an inventory database that contains the same information and it would be predictable to output this information in the form of a report.
Regarding Claims 3, 13 and 18:
Gonzalez in view of Prakash teaches all of the elements of Claims 1, 11 and 16. Gonzalez in view of Prakash and Bond teaches all of the elements of Claims 2, 12 and 17. While Gonzalez also teaches a plurality of inventory records, Gonzalez does not specifically teach: (Original) The computer-implemented method of claim 2, wherein determining the at least one item record in the at least one database comprises: determining that the at least one item record does not yet exist in the at least one database based on the at least one item identifier; and generating the at least one item record associated with the at least one item. Bond teaches this: ([0062] “new inventory department” and [0064] “ “inventory add” screen”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez to output an inventory report, as taught by Bond, because Gonzalez already teaches an inventory database that contains the same information and it would be predictable to output this information in the form of a report. It further would have been obvious to be able to add a new record so the report could be updated to match changes in physical inventory.
Regarding Claim 4:
Gonzalez in view of Prakash teaches all of the elements of Claim 1. Gonzalez in view of Prakash and Bond teaches all of the elements of Claims 2-3. While Gonzalez further teaches the database containing expiration date of an item (see at least [0057] “expiration date”), Gonzalez does not specifically teach: (Original) The computer-implemented method of claim 3, wherein generating the at least one item record comprises: determining at least one expiration date associated with the at least one item; and updating at least one expiration date field of the at least one item record based on the at least one expiration date. Bond teaches this: ([0073] “If the item is expirable, the user will be prompted to enter the expiration date”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez to output an inventory report, as taught by Bond, because Gonzalez already teaches an inventory database that contains the same information and it would be predictable to output this information in the form of a report. It further would have been obvious to be able to add an expiration date to the report so the report could be updated to match changes in physical inventory.
Regarding Claim 5:
Gonzalez in view of Prakash teaches all of the elements of Claim 1. Gonzalez in view of Prakash and Bond teaches all of the elements of Claims 2-4. Gonzalez further teaches (Original) The computer-implemented method of claim 4, wherein the method further comprises: determining, with at least one processor, a plurality of expiration dates based on the plurality of item records; ([0057] “expiration date”).
While Gonzalez teaches the value of an item (see at least [0061] “purchase prices”), Gonzalez does not specifically teach: and determining, with at least one processor, a total value of the plurality of items based on an individual value of each item of the plurality of items; Bond teaches this: ([0071] “Users with rights can optionally view the total cost of the inventory”).
Gonzalez does not teach but Bond teaches: and wherein generating the inventory report of the hospital comprises: generating the inventory report of the hospital based on the plurality of items, the plurality of locations, the plurality of expiration dates, and the total value. ([0155] “inventory reports” and [0156] “storage location” and [0161] “item name” and [0057] “expiration date” and [0071] “Users with rights can optionally view the total cost of the inventory”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez to output an inventory report, as taught by Bond, because Gonzalez already teaches an inventory database and it would be predictable to output this information in the form of a report. It further would have been obvious to be able to add item specific information to the report so the report could be updated to match changes in physical inventory.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2019/0371458 (Gonzalez) in view of U.S. Patent 11,443,275 (Prakash) in view of U.S. Patent publication 2011/0173028 (Bond) in view of U.S. Patent Publication 2023/0075566 (Wolf).
Regarding Claim 6:
Gonzalez in view of Prakash teaches all of the elements of Claim 1. Gonzalez in view of Prakash and Bond teaches all of the elements of Claims 2-4. Gonzalez further teaches the database containing expiration date of an item (see at least [0057] “expiration date”), but Gonzalez does not specifically teach: (Original) The computer-implemented method of claim 4, further comprising: determining, with at least one processor, at least one expired item based on a current date and the at least one expiration date field of the at least one item record; and transmitting, with at least one processor, at least one alert to at least one computing device associated with at least one inventory personnel based on the at least one expired item. Wolf, in the same field of art teaches this: ([0071] “the system can provide the user with information including, but not limited to, which items the system reports to be present in the kit, which items are missing, which items are subject to recall, and which items are expiring or soon to be expiring” and [Abstract] “Notifications about expirations and recalled items are provided to users” and [0086] “users may access the system not only from computers but from their mobile devices”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to supplement the inventory system taught by Gonzalez to send an alert to a user if an item was expired or about to expire, as taught by Wolf, due to the predictable importance of avoiding the use of expired medications or medical supplies to avoid illness.
Regarding Claim 7:
Gonzalez in view of Prakash teaches all of the elements of Claim 1. Gonzalez does not specifically teach: The computer-implemented method of claim 1, further comprising: receiving, with at least one processor, a recall notice associated with the at least one item; determining, with at least one processor, at least one current location of the at least one item based on the at least one item record; and transmitting, with at least one processor, at least one message to at least one computing device associated with at least one inventory personnel, the at least one message comprising the at least one current location of the at least one item and at least a portion of the recall notice. Wolf, in the same field of art teaches this: ([0071] “the system can provide the user with information including, but not limited to, which items the system reports to be present in the kit, which items are missing, which items are subject to recall, and which items are expiring or soon to be expiring” and [Abstract] “Notifications about expirations and recalled items are provided to users” and [0086] “users may access the system not only from computers but from their mobile devices”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to supplement the inventory system taught by Gonzalez to send an alert to a user if an item was under recall, as taught by Wolf, due to the predictable importance of avoiding the use of expired medications or medical supplies to avoid illness.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2019/0371458 (Gonzalez) in view of U.S. Patent 11,443,275 (Prakash) in view of in view of U.S. Patent publication 2011/0173028 (Bond) in view of U.S. Patent Publication 2024/0206984 (Woolford).
Regarding Claim 10:
Gonzalez in view of Prakash teaches all of the elements of Claim 1. Gonzalez does not specifically teach but Bond teaches: (Currently Amended) The computer-implemented method of claim 1, wherein the at least one human in the at least one room is at least one patient undergoing at least one operation and at least one clinician performing the at least one operation, ([0163] “The banner "Encounter Costing Report" identifies the type of report. Underneath the banner, the… attending physician, the patient name”).
Bond further teaches: wherein updating the at least one item record in the at least one database further comprises: associating the at least one item record with the at least one patient identifier, the at least one clinician identifier, at least one identifier of the at least one action, and at least one time of the at least one action. [0163] By clicking "Show Report," the user is directed to a screen listing encounters that can be included in the Encounter Costing Report. The entries are listed by encounter date, complaint, patient name, provider and amount. The user can select the checkboxes next to any or a combination of these entries to be included in the report…. Underneath the banner, the date of service, encounter type, attending physician, the patient name, patient's date of birth, and insurance carrier are shown”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez to output an inventory report, as taught by Bond, because Gonzalez already teaches an inventory database and it would be predictable to output this information in the form of a report. It further would have been obvious to be able to add as much information to the report as is available so the report could more fully document the actual medical event to document the disposition of inventory items.
Gonzalez does not specifically teach: the method further comprising: inputting, with at least one processor, at least a portion of the stream of images into the at least one image classification machine-learning model, the at least one image classification machine-learning model being trained at least partly on a set of images of actions taken in a plurality of patient operations; and determining, with at least one processor, at least one action of the at least one operation based on at least one second output of the at least one image classification machine-learning model; Woolford, in a related field of art, teaches this: ([0055] “machine learning classifiers” and [0012] “Optionally, the one or more classifiers comprise at least one of: a tool identification classifier; a tool location classifier; an anatomical identification classifier; an anatomical position classifier; a surgery type classifier; a surgery stage classifier, a tool identification classifier, and/or a tool location classifier”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the inventory system taught by Gonzalez that uses machine learning classification tools to identify objects could also train these tools to identify specific actions, as taught by Woolford with the predictable advantage of adding more information to the database to more fully document the medical event and the disposition of inventory items.
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.
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/KIMBERLY S. BURSUM/Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627
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