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 .
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5, 6, 8-14, 16-23 and 25 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Patel 2022/0101192.
With respect to claim 1, Patel teaches a method for characterizing an object such as a sample container, comprising: capturing an image by means of infrared sensors, see para. 95 or by I/O components 818 for receiving input information, see para. 94. Patel teaches characterizing the object by using a first machine learning model, see para. 104, lines 8-10, operable by a controller (processor 804), see para. 103, especially lines 1-13.
Patel teaches determining a confidence level by comparing a first cumulative value to a first threshold probability value (see the bottom of page 104).
Patel teaches a refining component 210 which triggers retraining of the first AI model by evaluation and fine tuning the machine learning model, (see the bottom of para. 48). The retraining data which was not part of the original set, but rather data identifying additional features to be considered when evaluating fraud. This information includes non-image data that includes features that are obtained in a certain geographic region within a specific time. See para. 49, beginning at line 8. Hence the information is relevant to the current location as claimed.
With respect to claim 5, Patel teaches storing captured images in data storage 212 (see para. 35) when it has been determined that the output probability score goes below a threshold level, indicative that fraud has occurred. See the middle of para. 34).
With respect to claim 6, Patel teaches a sample in which other training feature are being used which were not at all part of the original training set. For example, at para. 16, Patel teaches evaluating additional features considered when evaluating fraud. Para. 48 also teaches the use of additional features which are now being used, not previously used, to retrain or update the models.
With respect to claim 8, Patel teaches a system controller (computing system 106) which causes annotation data, within the feedback signal, to determine which of the models will be retrained with new features, not original part of the training, see para. 48 and 49, see para. 21.
With respect to claim 9, Patel teaches a user interface, such as client devices, for inputting data to the computing system 106. The client devices, include but are not limited to devices 102 and 104, such as desktop computer, mobile computers, smart phones, tablets and smart TVs.
With respect to claim 10, Patel teaches a first AI algorithm which produces a second one with features not previously considered. The second algorithm or machine model is retrained using features generated from feedback signals, as set forth at the bottom of para. 49. See para. 45 regarding the second algorithm via second machine model. See also paras. 50 and 51 regarding retraining of the feature models.
With respect to claim 11, Patel teaches that the first algorithm is retrained to produce a second algorithm or machine model as set forth in para. 45, wherein such information is made available to a user, via client devices 102 and 104 through computing system 106 (automated diagnostic analysis system, as claimed). The results of the second machine learning are shared by interface described at the bottom of para. 22. The user interfaces the system 106 through client devices 102 and 104.
With respect to claim 12, Patel teaches wherein the user, via client devices 102 and 104, (see para. 21) may interface with system 106 (see the top of para. 22) and to determine if fraudulent behavior has occurred (see para. 24).
Patel teaches that the first algorithm produces a second algorithm by means of feedback signal, see the middle of para. 49, for example.
The system controller 106 replaces the first algorithm with the second in view of input information received by a user through the client interface, described at the bottom of para. 22.
With respect to claim 13, Patel teaches that the second retrained algorithm replaces the first one because the first one is nullified in view of the second one wherein the second one uses additional features, not contemplated in the first algorithm. The additional features are used in the second algorithm for the purpose of determining of fraud is or has occurred, see para. 24. The second algorithm is determined by the refinement component 210made available to the user see para. 53.
With respect to claim 14, Patel teaches an automated diagnostic system, comprising: infrared sensors, see para. 95 or by I/O components 818 for receiving input information, see para. 94.
Patel teaches a system controller via system 106 coupled to the image capture elements such as biometric component or other illumination sensing components, as set forth in para. 95. Patel teaches the system controller 106 coupled to an imaging I/O component (818 of which the image capture unit is a subpart) or infrared sensor for detecting object.
Patel teaches a first AI algorithm is used to characterize the object by using a first machine learning model, see para. 104, lines 8-10, operable by a controller (processor 804), see para. 103, especially lines 1-13.
Patel teaches determining a confidence level by comparing a first cumulative value to a first threshold probability value (see the bottom of page 104).
Patel teaches that when a probability score is below a threshold value, a further determination is made using second machine learning algorithm. A refining component 210 which triggers retraining of the first AI model by evaluation and fine tuning the machine learning model, (see the bottom of para. 48). The retraining data which was not part of the original set, but rather data identifying additional features to be considered when evaluating fraud. This information includes non-image data that includes features that are obtained in a certain geographic region within a specific time. See para. 49, beginning at line 8. Hence the information is relevant to the current location as claimed.
With respect to claim 16, Patel teaches storing captured images in data storage 212 (see para. 35) when it has been determined that the output probability score goes below a threshold level, indicative that fraud has occurred. See the middle of para. 34).
With respect to claim 17, Patel teaches a sample in which other training feature are being used which were not at all part of the original training set. For example, at para. 16, Patel teaches evaluating additional features considered when evaluating fraud. Para. 48 also teaches the use of additional features which are now being used, not previously used, to retrain or update the models.
With respect to claim 18, Patel teaches a first AI algorithm which produces a second one with features not previously considered. The second algorithm or machine model is retrained using features generated from feedback signals, as set forth at the bottom of para. 49. See para. 45 regarding the second algorithm via second machine model. See also paras. 50 and 51 regarding retraining of the feature models.
With respect to claim 19, Patel teaches that the first algorithm is retrained to produce a second algorithm or machine model as set forth in para. 45, wherein such information is made available to a user, via client devices 102 and 104 through computing system 106 (automated diagnostic analysis system, as claimed). The results of the second machine learning are shared by interface described at the bottom of para. 22. The user interfaces the system 106 through client devices 102 and 104.
With respect to claim 20, Patel teaches a retraining of the first algorithm when the probability score is less than a predetermined amount. Patel teaches that a feedback signal is used to determined which modules to use for the retraining so as to generate the features to be used in retraining, see the middle of para. 16 and the bottom of para. 48. See also steps 508 and 510.
Patel teaches that the first algorithm produces a second algorithm by means of feedback signal, see the middle of para. 49, for example.
The system controller 106 replaces the first algorithm with the second in view of input information received by a user through the client interface, described at the bottom of para. 22.
With respect to claim 21, Patel teaches non-image data, which includes location information, as set forth in para. 49, wherein feedback information evaluates new feature information which regards the specified geographic region associated with the transaction. It is inherent that within the client devices are GPS sensors which indicate the location of the transaction being discriminated as being fraudulent or not. Note that at the bottom of para. 49, it states that the geographic location is determined. Hence, the location sensor is implied as part of the client devices.
With respect to claim 22, Patel teaches the use of different sensors for measuring different functions. For example, at the bottom of para. 94, beginning at line 21, Patel uses haptic components, which is a vibration sensor as claimed. In the same section, Patel teaches acoustic sensors, also claimed. At para. 95, beginning at line 9, Patel also teaches the use of temperature sensors, which are also claimed.
With respect to claim 23, Patel teaches wherein the user may enter the location of the transaction. For example, at para. 94, lines 1-14, Patel describes I/O components. The I/O components, beginning at line 22, indicate that the input components maybe alphanumeric input components from a keyboard, a touch screen, a photo-optical keypad or other alphanumeric input components. The Patel reference further teaches toward the bottom of the paragraph that : “… tactile input components (e.g., a physical button, a touch screen that provides location…” Hence, Patel contemplates current location information which can be textual.
With respect to claim 25, Patel teaches a method for characterizing an object such as a sample container, comprising: capturing an image by means of infrared sensors, see para. 95 or by I/O components 818 for receiving input information, see para. 94. Patel teaches characterizing the object by using a first machine learning model, see para. 104, lines 8-10, operable by a controller (processor 804), see para. 103, especially lines 1-13.
Patel teaches the use of different sensors for measuring different functions. For example, at the bottom of para. 94, beginning at line 21, Patel uses haptic components, which is a vibration sensor as claimed. In the same section, Patel teaches acoustic sensors, also claimed. At para. 95, beginning at line 9, Patel also teaches the use of temperature sensors, which are also claimed. Patel teaches wherein the user may enter the location of the transaction. For example, at para. 94, lines 1-14, Patel describes I/O components. The I/O components, beginning at line 22, indicate that the input components maybe alphanumeric input components from a keyboard, a touch screen, a photo-optical keypad or other alphanumeric input components. The Patel reference further teaches toward the bottom of the paragraph that : “… tactile input components (e.g., a physical button, a touch screen that provides location…” Hence, Patel contemplates current location information which can be textual.
Patel teaches determining a confidence level by comparing a first cumulative value to a first threshold probability value (see the bottom of page 104).
Patel teaches a refining component 210 which triggers retraining of the first AI model by evaluation and fine tuning the machine learning model, (see the bottom of para. 48). The retraining data which was not part of the original set, but rather data identifying additional features to be considered when evaluating fraud. This information includes non-image data that includes features that are obtained in a certain geographic region within a specific time. See para. 49, beginning at line 8. Hence the information is relevant to the current location as claimed.
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.
Claim(s) 3, 4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel (2022/0101192) in view of Narasimhamurthy WO2021086720 .
With respect to claim 3, Patel teaches all of the subject matter upon which the claims depend except for the sample, taken by the image capture device, comprised of hemolysis, icterus or lipemia.
However, Narasimhamurthy, teaches obtaining a sample, by image capture, of hemolysis, icterus and lipemia, see para. 7 and para. 19.
Therefore, since Narasihamurthy provides the motivation for taking samples of these medical abnormalities, it would have been recognized by Patel to sample such medical abnormalities.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the image capture device, as taught by Patel, for the purpose of sampling medical abnormalities such as hemolysis, icterus and lipemia as set forth by the prior art.
With respect to claim 4, Patel teaches all of the subject matter upon which the claim depends except for determining if a top is present on the sample.
Narasihamurthy, teaches determining the height, diameter, type and color of the cap on the sample, see para. 21.
Since Narasihamurthy clearly teaches determining characteristics of the top or cap on a sample, the determination of these characteristics determines that cap is present.
Hence, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of Narasihamurthy, so that the sample discriminated by Patel, may determine the type of sample and whether there was a top present thereon.
With respect to claim 7, Patel teaches a sample in which other training feature are being used which were not at all part of the original training set. For example, at para. 16, Patel teaches evaluating additional features considered when evaluating fraud. Para. 48 also teaches the use of additional features which are now being used, not previously used, to retrain or update the models.
What Patel does not teach is a sample HILN sub-class which is data not original present in the sample.
Narsimhamurthy teaches a sample that is HILN sub-class.
Since, Narsimhamurthy teaches a specific sample, for image capture, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to substitute one type of sample, as taught by Patel, for that taught by Narsimhamurthy as a matter of choice.
Allowable Subject Matter
With respect to claims 2 and 15, the prior art does not teach the combination wherein delaying training of the first AI algorithm with retraining data in response to a user input.
Claim 24 is objected to as containing allowable matter for at least the reason the prior art does not show in claimed combination, wherein textual data is self-evaluated and analysis reports of characterization performed by the first AI algorithm, performed tests or patient information.
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/JEROME GRANT II/Primary Examiner, Art Unit 2664