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
In response to the Office Action mailed on February 13, 2026, the applicant has submitted an amendment/request for reconsideration filed on May 7, 2026; arguing to traverse the 35 U.S.C. 102 rejection of independent claims 1, 14, and 25.
An amendment to the specification filed on May 7, 2026 is noted and made of record.
Response to Arguments
Applicant’s arguments, see pages 11-15 of the remarks, filed on May 7, 2026, with respect to the rejection(s) of claims 1, 3-14, 16-23, and 25 under 35 U.S.C. 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made (rejecting claims 1-25) in view of Narasimhamurthy, et al. (WO 2021/086720 A1) and Heilig, et al. (2020/ 0395121 A1) which will be discussed in the rejection below.
Notice re prior art available under both pre-AIA and AIA
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 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.
Examiner's Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 1-20 and 23-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Narasimhamurthy, et al. (WO 2021/086720 A1).
With regard to claim 1, Narasimhamurthy, et al. disclose a method of characterizing a sample container or a sample, i.e., specimen/s, in an automated diagnostic analysis system (See for example, paragraph 0030: specimen containers 102, specimens 212, and automated diagnostic analysis system 100; and See Figs. 1 and 2 ), comprising: capturing an image of a sample container containing a sample by using an imaging device, i.e., image capturing devices (See for example, paragraph 0051; and Fig. 4); characterizing the image using a first artificial intelligence (AI) algorithm, i.e., HILN network, which may be or includes, e.g., a segmentation convolutional neural network (SCNN), executing on a system controller, i.e., computer 143 of Fig. 1, of the automated diagnostic analysis system (See for example, paragraph 0021 and 0057-0059); determining a characterization confidence level of the image using the system controller (See for example, paragraphs and 0062); and triggering a retraining, i.e., training updates and/or additional training, of the first AI algorithm with retraining data in response to a characterization confidence level determined to be below a pre-selected threshold, the triggering initiated by the system controller, wherein: the retraining data includes image data captured by the imaging device or non-image data, i.e., characterization of images determined to be incorrect or low confidence and other related data or user input and/or annotation, that includes features prevalent at a current location, i.e., geographic location, location, address and/or region, of the automated diagnostic analysis system that were not sufficiently or at all included in training data used to initially train the first AI algorithm (See for example, paragraphs 0024, 0027, 0040-0041 and 0063-0064). Thus, each of the requirements of claim 1 is met.
With regard to claim 2, the method of claim 1, wherein the triggering further comprises: notifying a user via a user interface of the automated diagnostic analysis system in response to a characterization confidence level determined to be below a pre-selected threshold, wherein the notification indicates that the first AI algorithm is to be retrained with the retraining data, the triggering initiated by the system controller; and delaying retraining of the first AI algorithm with the retraining data in response to receiving user input to delay the retraining (See for example, paragraph 0027: “. . . characterizations performed by the HILN network that are determined to be incorrect or low confidence may not be automatically forwarded to an analyzer of the automated diagnostic analysis system. . . . a report or prompt of the availability of one or more training updates may be provided to a user to allow the user to decide when and if to receive and/or incorporate the one or more training updates in the HILN network. . .”).
With regard to claim 3, the method of claim 1, wherein the characterizing comprises determining a presence of hemolysis, icterus, or lipemia in the sample contained in the sample container imaged by the imaging device (See for example, paragraphs 0020-0021).
With regard to claim 4, the method of claim 1, wherein the characterizing comprises determining whether a cap is present on a sample container imaged by the imaging device (See for example, paragraph 0023).
With regard to claim 5, the method of claim 1, further comprising storing captured images that have a determined characterization confidence level below the pre-selected threshold (See for example, paragraph 0027 and 0062).
With regard to claim 6, the method of claim 1 wherein the features prevalent at the current location of the automated diagnostic analysis system include sample container configurations or types not sufficiently or at all included in the training data used to initially train the first AI algorithm (See for example paragraph 0065).
With regard to claim 7, the method of claim 1 wherein the features prevalent at the current location of the automated diagnostic analysis system include sample HILN sub-classes not sufficiently or at all included in the training data used to initially train the first AI algorithm (See for example, paragraph 0059 and 0065).
With regard to claim 8, the method of claim 1 wherein the retraining data has annotations automatically generated by the system controller or is manually annotated by a user (See for example, paragraph 0027).
With regard to claim 9, the method of claim 1 wherein the retraining data additionally includes data provided by a user via a user interface of the automated diagnostic analysis system (See for example, paragraph 0055: “ The training updates 537 may be forwarded to HILN network 535 (e.g., via the Internet or a physical media) for incorporation therein. The incorporation of the training updates 537 into HILN network 535 may be automatic under the control of computer 143 or optionally at a prompting at a user’s discretion via CIM 145 (of FIG.1 1)”; and paragraph 0064) .
With regard to claim 10, the method of claim 1 wherein retraining the first AI algorithm produces a second AI algorithm, the method further comprising validating the second AI algorithm with a validation dataset (See for example, paragraph 0055).
With regard to claim 11, the method of claim 1, wherein retraining the first AI algorithm produces a second AI algorithm, the method further comprising reporting availability of the second AI algorithm to a user via a user interface of the automated diagnostic analysis system (See for example, paragraph 0055).
With regard to claim 12, the method of claim 1, wherein retraining the first AI algorithm produces a second AI algorithm, the method further comprising replacing the first AI algorithm with the second AI algorithm in response to user input received via a user interface of the automated diagnostic analysis system (See for example, paragraph 0055 “. . . Based on this report, the user can approve for an update or the HILN network 535 can get updated automatically. These training updates can occur without interrupting the existing workflow. Here the update can simply replace the old model with the new model trained on the new data . . .” ).
With regard to claim 13, the method of claim 12, further comprising replacing the second AI algorithm with the first AI algorithm in response to further user input received via the user interface, i.e., based on user’s judgment/discretion via CIM 145 (See for example, paragraph 0055).
Claim 14 is rejected the same as claim 1 except claim 14 is an apparatus claim. Thus, argument similar to that presented above for claim 1 is applicable to claim 14. With regard to an automated diagnostic analysis system, comprising: an imaging device configured to capture an image of a sample container containing a sample; and a system controller coupled to the imaging device, applicant’s attention is invited to Figs. 1 and 5.
With regard to claim 15, wherein the system controller is further configured to: notify a user via a user interface of the automated diagnostic analysis system that the first AI algorithm is to be retrained with the retraining data in response to the trigger; and delay the retraining of the first AI algorithm in response to receiving user input within a pre-determined time period to delay the retraining (See for example, paragraph 0027: “. . . the training updates may be forwarded to the HILN network for incorporation therein via retraining. In some embodiments, a report or prompt of the availability of one or more training updates may be provided to a user to allow the user to decide when and if to receive and/or incorporate the one or more training updates in the HILN network, what this means is that the user may decide when one or more training updates might occur, and as a result it may delay the process.”)
With regard to claim 16, the automated diagnostic analysis system of claim 14, wherein the system controller is further configured to store in a storage device of the automated diagnostic analysis system captured images that have a determined characterization confidence level below the pre-selected threshold (See for example, paragraphs 0027 and 0062).
With regard to claim 17, the automated diagnostic analysis system of claim 14, wherein the features prevalent at the current location of the automated diagnostic analysis system include: sample container configurations or types not sufficiently or at all included in the training data used to initially train the first AI algorithm; or sample HILN sub-classes not sufficiently or at all included in the training data used to initially train the first AI algorithm (See for example, paragraph 0059 and 0065).
With regard to claim 18, the automated diagnostic analysis system of claim 14, wherein the retraining of the first AI algorithm produces a second AI algorithm, and the system controller is further configured to validate the second AI algorithm with a validation dataset (See for example, paragraph 0055).
With regard to claim 19, the automated diagnostic analysis system of claim 14, wherein the retraining of the first AI algorithm produces a second AI algorithm, and the system controller is further configured to report availability of the second AI algorithm to a user via a user interface of the automated diagnostic analysis system(See for example, paragraph 0055).
With regard to claim 20, the automated diagnostic analysis system of claim 14, wherein the retraining of the first AI algorithm produces a second AI algorithm, and the system controller is further configured to replace the first AI algorithm with the second AI algorithm in response to user input received via a user interface of the automated diagnostic analysis system (See for example, paragraph 0055 “. . . Based on this report, the user can approve for an update or the HILN network 535 can get updated automatically. These training updates can occur without interrupting the existing workflow. Here the update can simply replace the old model with the new model trained on the new data . . .” ).
With regard to claim 23, the automated diagnostic analysis system of claim 14, wherein the non-image data that includes the features prevalent at the current location is text data (See for example, paragraphs 0024 and 0063).
With regard to claim 24, the automated diagnostic analysis system of claim 23, wherein the text data is self-evaluation and analysis reports of the characterization performed by the first AI algorithm, data related to tests being performed, or patient information (See for example, paragraphs 0021, 0027, and 0032).
.Claim 25 is rejected the same as claim 1. Thus, argument similar to that presented above for claim 1 is applicable to claim 25. Claim 25 distinguishes from claim 1 only in that it recites capturing data representing a sample container containing a sample by using one or more of an optical, acoustic, humidity, liquid volume, vibration, weight, photometric, thermal, temperature, current, or voltage sensing device. Fortunately, Narasimhamurthy (See for example, paragraph 0051 “image capturing devices 440A-440C: optical devices; and paragraphs 0004: photometric) teach one or more of these features.
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 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Narasimhamurthy, et al. in view of Heilig, et al. (2020/ 0395121 A1).
With regard to claim 21 Narasimhamurthy, et al.(hereinafter “Narasimhamurthy”) discloses all of the claimed subject matter as set forth above in paragraph 9, and incorporated herein by reference. While Narasimhamurthy discloses non-imagining data, i.e., other related data of incorrect or low confidence characterization determination and/or user input/annotation (See for example, paragraph 0027 or 0062 ), Narasimhamurthy does not expressly call for wherein the non-image data is received from one or more measurement sensors at the current location. However, Heilig, et al. (See for example, paragraphs 0021, 0037, and 0040) teach this feature. Narasimhamurthy and Heilig, et al. are combinable because they are from the same field of endeavor, i.e., performing testing on a specimen (See for example, paragraph 0021). Before the effective filing date of the claimed
invention, it would have been obvious to incorporate the teaching as taught by Heilig, et al. into the system of Narasimhamurthy, and to do so would at least allow analyzing/testing measurement data (e.g., temperature) produced by measurement (temperature) sensor/s (See for example, paragraph 0044). Therefore, it would have been obvious to combine Narasimhamurthy with Heilig, et al.to obtain the invention as specified in claim 21.
With regard to claim 22, the automated diagnostic analysis system of claim 21, wherein the one or more measurement sensors are one or more, temperature sensors, acoustic sensors, humidity sensors, liquid volumes sensors, weight sensors, vibration sensors, current sensors or voltage sensors (See for example, paragraph 0044 of Heilig, et al.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL G MARIAM whose telephone number is (571)272-7394. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mathew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL G MARIAM/ Primary Examiner, Art Unit 2675
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