DETAILED ACTION
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 .
Status of Claims
This action is in reply to the claims filed on 13 May 025. Claims 1-20 are currently pending and have been examined.
Claim Objections
Claim 6 objected to because of the following informalities: the claim does not end with the use of punctuation, such as a period. This appears to by a typographical error. Appropriate correction is required.
Claim 16 objected to because of the following informalities: the claim does not end with the use of punctuation, such as a period. This appears to by a typographical error. Appropriate correction is required.
Claim 17 objected to because of the following informalities: the claim finishes with two periods as punctuation but should only have one period. This appears to by a typographical error. Appropriate correction is required
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-20 fall within one or more statutory categories. Claims 1-10 fall within the category of a machine. Claims 11-20 fall within the category of a process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-20 recite an abstract idea. Representative claim 1 recites:
receive dental images of a patient;
… identify dental features associated with one or more non-dental medical conditions;
for each of the one or more non-dental medical conditions:
determine a risk score based on the identified dental features; and
compare the risk score to a threshold value associated with the non-dental medical condition.
Therefore, the claim as a whole is directed to “treating a patient,” which is an abstract idea because it is a method of organizing human activity. “Treating a patient” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the present claims include the interaction between a healthcare provider and a patient.
Alternatively, the claims recite a mental process because it includes concepts capable of being performed in the human mind. The proudest reasonable interpretation of the claims include observation, evaluation, judgment, and opinion.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
at least a processor; and
at least a memory containing instructions that instruct the processor to:
process the dental images using a trained machine learning model….
The additional elements individually or in combination do not integrate the exception into a practical application. These additional element merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
the at least a memory contains further instructions that instruct the at least a processor to generate an alert containing information about the non-dental medical condition and the risk score when the risk score exceeds the threshold value, and transmit the generated alert to at least one of: the patient, a dental professional, and a medical professional.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 2 is ineligible.
Dependent claim 3 recites the method of claim 1, wherein:
the threshold value is dynamically determined based on at least one of a patient's age, a patient's medical history, and statistical data from a population of patients.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible.
Dependent claim 4 recites the method of claim 1, wherein:
determining the risk score comprises assigning numerical weights to each of the identified dental features based on a strength of correlation between the dental feature and the non-dental medical condition,
calculating a weighted sum of the identified dental features, and
normalizing the weighted sum to generate the risk score on a predefined scale.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 4 is considered to be ineligible.
Dependent claim 5 recites the method of claim 1, wherein:
the one or more non-dental medical conditions comprise at least one of diabetes, cardiovascular disease, stroke, Alzheimer's disease, respiratory disease, rheumatoid arthritis, and pregnancy complications.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 5 is considered to be ineligible.
Dependent claim 6 recites the method of claim 1, wherein:
the trained machine learning model has been trained with training data comprising dental data correlated to medical data, wherein:
the dental data comprises a plurality of dental images; and the medical data is representative of a plurality of non-dental medical conditions.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible.
Dependent claim 7 recites the method of claim 6, wherein:
the medical data is derived from an electronic health record (EHR) from a hospital and at least a dental image of the plurality of dental images is representative of at least a surface of dental tissue.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 7 is considered to be ineligible.
Dependent claim 8 recites the method of claim 1, wherein:
the at least a memory contains further instructions that instruct the at least a processor to monitor changes in the patient's dental images over time, identify trends in the patient's risk scores for the one or more non-dental medical conditions, and generate trend alerts when a pattern of increasing risk is detected over a predetermined time period.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 8 is considered to be ineligible.
Dependent claim 9 recites the method of claim 1, wherein:
the at least a memory contains further instructions that instruct the at least a processor to analyze potential interactions between different conditions when the patient has multiple risk scores exceeding respective threshold values for different non-dental medical conditions and identify compounding risk factors where multiple conditions may exacerbate each other.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 9 is considered to be ineligible.
Dependent claim 10 recites the method of claim 2, wherein:
the at least a memory contains further instructions that instruct the at least a processor to implement different threshold values for generating alerts based on a recipient type,
wherein alerts transmitted to medical professionals use a lower threshold value,
alerts transmitted to dental professionals use a threshold value focused on conditions with established oral-systemic connections, and
alerts transmitted to patients use a higher threshold value.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 10 is ineligible.
Claims 11-20 are parallel in nature to claims 1-10. Accordingly claims 11-20 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
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.
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 1-8, 10-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci et al. (U.S. 2021/0350530), hereinafter “Ricci,” in view of Cronin et al. (U.S. 2017/0330297), hereinafter “Cronin.”
Regarding Claim 1, Ricci discloses a system for generating medical alerts based on dental imagery analysis, comprising:
at least a processor (See Ricci [0116] system can include the use of at least one processor unit.); and
at least a memory containing instructions (See Ricci [0117] system can include the use of computer storage media.) that instruct the processor to:
receive dental images of a patient (See Ricci [0002] system can receive dental images to process.);
process the dental images using (See Ricci [0002] system uses artificial intelligence to process a dental image for determining a relative health risk.) a trained machine learning model (See Ricci [0047] artificial intelligence model can be trained for identifying of at least one dental characteristic. [0113] artificial intelligence system can be used to correlate dental images with bioinformatics data sets. Bioinformatics datasets include diabetes diagnosis) to identify dental features associated with one or more non-dental medical conditions (See Ricci [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk. [0113] artificial intelligence system can be used to correlate dental images with bioinformatics data sets. Bioinformatics datasets include diabetes diagnosis);
for each of the one or more non-dental medical conditions: determine a risk score based on the identified dental features (See Ricci [0129] predict risk of dental pathology. [0133] system generates a relative health risk. [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.).
Ricci does not disclose:
compare the risk score to a threshold value associated with the non-dental medical condition.
Cronin teaches:
compare the risk score to a threshold value associated with the non-dental medical condition (See Cronin [0116] the system calculates a numerical score that is meant to be compared to a threshold.).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claim 2, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci does not further disclose a system, wherein:
the at least a memory contains further instructions that instruct the at least a processor to generate an alert containing information about the non-dental medical condition and the risk score when the risk score exceeds the threshold value, and transmit the generated alert to at least one of: the patient, a dental professional, and a medical professional.
Cronin teaches:
the at least a memory contains further instructions that instruct the at least a processor to generate an alert containing information about the non-dental medical condition and the risk score when the risk score exceeds the threshold value (See Cronin [0125] system uses a different family history score, which may calculate an obesity risk score associated with family members of the user. In this example, different thresholds are used to set different actions: in the candidate rule where score exceeds 20, the installed rule will contact the user's physician when the measured calories burned does not exceed a calorie amount prescribed by the doctor (thereby cross referencing other user data such as, for example, the user's electronic health record). Another candidate rule, however, will result in a rule that only informs the user that they are not meeting guidelines when this same applicability criteria.), and transmit the generated alert to at least one of: the patient, a dental professional, and a medical professional (See Cronin [0070] when a threshold is being exceeded, a determination is made that the user's doctor should be contacted. See also [0125].).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claim 3, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci does not further disclose a system, wherein:
the threshold value is dynamically determined based on at least one of a patient's age, a patient's medical history, and statistical data from a population of patients.
Cronin teaches:
the threshold value is dynamically determined based on at least one of a patient's age, a patient's medical history, and statistical data from a population of patients (See Cronin [0070] If the family history does show high blood pressure, a rule-action combination may be generated and applied based on this history. For example, if a user has a family history of high blood pressure, a rule that might otherwise provide a “high blood pressure” warning action at a specific threshold blood pressure could be adjusted to provide that “high blood pressure” warning action at a lower threshold blood pressure.).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claim 4, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci further discloses a system, wherein:
determining the risk score comprises assigning numerical weights to each of the identified dental features based on a strength of correlation between the dental feature and the non-dental medical condition (See Ricci [0123] the system assign weights between correlations of connected nodes (i.e. dental image landmarks and diabetes diagnosis). [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.),
calculating a weighted sum of the identified dental features (See Ricci [0123] the system assign weights between correlations of connected nodes (i.e. dental image landmarks and diabetes diagnosis). [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.), and
normalizing the weighted sum to generate the risk score on a predefined scale (See Ricci [0129] system can normalize genomic expression levels and correlate it to a dental image. [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.).
Regarding claim 5, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci further discloses a system, wherein:
the one or more non-dental medical conditions comprise at least one of diabetes, cardiovascular disease, stroke, Alzheimer's disease, respiratory disease, rheumatoid arthritis, and pregnancy complications (See Ricci [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.).
Regarding claim 6, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci further discloses a system, wherein:
the trained machine learning model has been trained with training data comprising dental data correlated to medical data (See Ricci [0047] at least one first artificial intelligence model may be trained for identifying of at least one dental characteristic. [0049] artificial intelligence model may be trained based on at least one of: a dental image dataset, a dental image treatment dataset, a dental product dataset, a dental image landmark dataset and an artificial intelligence dataset. [0121] trained to correlate dental images and bioinformatics datasets. [0123] trained to correlate dental images and genetic datasets.), wherein:
the dental data comprises a plurality of dental images (See Ricci [0045] At least one dental dataset may include at least one of a classified dental image anatomy dataset and a classified dental image pathology dataset.); and
the medical data is representative of a plurality of non-dental medical conditions (See Ricci [0045] bioinformatics dataset including at least one of: a gene identifier, a gene sequence, a single nucleotide polymorphism, a nucleic acid sequence, a protein sequence, an annotating genome, a shotgun sequence, a periodontal disease, a caries susceptibility, an impacted tooth, a tooth loss, an angle's classification of malocclusion, a diabetes diagnosis.).
Regarding claim 7, Ricci in view of Cronin discloses the system of claim 6 as discussed above. Ricci further discloses a system, wherein:
… at least a dental image of the plurality of dental images is representative of at least a surface of dental tissue (See Ricci [0002] the he dental image or image is received from a source such as an x-ray, a camera, or an image capturing device. It is understood that this includes at least a surface of dental tissue.).
Ricci does not disclose
the medical data is derived from an electronic health record (EHR) from a hospital.
Cronin teaches:
the medical data is derived from an electronic health record (EHR) from a hospital (See Cronin [0105] system can retrieve medical data from an electronic health record.).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claim 8, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci further discloses a system, wherein:
[analyze patient medical data such as] dental images… (See Ricci [0002] system can receive dental images to process.).
Ricci does not disclose
the at least a memory contains further instructions that instruct the at least a processor to monitor changes in the patient's [medical data] over time,
identify trends in the patient's risk scores for the one or more non-dental medical conditions, and generate trend alerts when a pattern of increasing risk is detected over a predetermined time period.
Cronin teaches:
the at least a memory contains further instructions that instruct the at least a processor to monitor changes in the patient's [medical data] over time (See Cronin [0041] system can monitor health data about a patient over time.),
identify trends in the patient's risk scores for the one or more non-dental medical conditions, and generate trend alerts when a pattern of increasing risk is detected over a predetermined time period (See Cronin [0125] system uses a different family history score, which may calculate an obesity risk score associated with family members of the user. In this example, different thresholds are used to set different actions: in the candidate rule where score exceeds 20, the installed rule will contact the user's physician when the measured calories burned does not exceed a calorie amount prescribed by the doctor (thereby cross referencing other user data such as, for example, the user's electronic health record). Another candidate rule, however, will result in a rule that only informs the user that they are not meeting guidelines when this same applicability criteria.).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claim 10, Ricci in view of Cronin discloses the system of claim 2 as discussed above. Ricci further discloses a system, wherein:
the at least a memory contains further instructions that instruct the at least a processor to implement … alerts transmitted to dental professionals use a threshold value focused on conditions with established oral-systemic connections … (See Ricci [0049] system can make a referral to a medical professional based on the outcome of the analysis of the dental image. This includes a referral specifically to a dental professional.).
Ricci does not disclose
the at least a memory contains further instructions that instruct the at least a processor to implement different threshold values for generating alerts based on a recipient type,
wherein alerts transmitted to medical professionals use a lower threshold value,
alerts transmitted to patients use a higher threshold value.
Cronin teaches:
the at least a memory contains further instructions that instruct the at least a processor to implement different threshold values for generating alerts based on a recipient type (See Cronin [0125] system uses a different family history score, which may calculate an obesity risk score associated with family members of the user. In this example, different thresholds are used to set different actions: in the candidate rule where score exceeds 20, the installed rule will contact the user's physician when the measured calories burned does not exceed a calorie amount prescribed by the doctor (thereby cross referencing other user data such as, for example, the user's electronic health record). Another candidate rule, however, will result in a rule that only informs the user that they are not meeting guidelines when this same applicability criteria.),
wherein alerts transmitted to medical professionals use a lower threshold value (See Cronin [0125] system uses a different family history score, which may calculate an obesity risk score associated with family members of the user. In this example, different thresholds are used to set different actions: in the candidate rule where score exceeds 20, the installed rule will contact the user's physician when the measured calories burned does not exceed a calorie amount prescribed by the doctor (thereby cross referencing other user data such as, for example, the user's electronic health record).),
alerts transmitted to patients use a higher threshold value (See Cronin [0125] system uses a different family history score, which may calculate an obesity risk score associated with family members of the user. Another candidate rule, however, will result in a rule that only informs the user that they are not meeting guidelines when this same applicability criteria.).
The system of Cronin is applicable to the disclosure of Ricci as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include threshold and notification elements as taught by Cronin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to account for factors that may ameliorate or deteriorate the potentially detrimental health behaviors [0012]).
Regarding claims 11-18 and 20, Ricci in view of Cronin discloses the system of claims 1-8 and 10 as discussed above. Claims 11-18 and 20 recite a method that is substantially similar to the method performed by the system of claims 1-8 and 10. Accordingly, claims 11-18 and 20 are rejected based on the same analysis.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci et al. (U.S. 2021/0350530), hereinafter “Ricci,” in view of Cronin et al. (U.S. 2017/0330297), hereinafter “Cronin,” and further in view of Moheb (U.S. 2021/0282645), hereinafter “Moheb.”
Regarding claim 9, Ricci in view of Cronin discloses the system of claim 1 as discussed above. Ricci further discloses a system, wherein:
the at least a memory contains further instructions that instruct the at least a processor to [perform an action] when the patient has multiple risk scores exceeding respective threshold values for different non-dental medical conditions (See Ricci [0129] predict risk of dental pathology. [0133] system generates a relative health risk. [0002] Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk.).
Ricci does not disclose:
the at least a memory contains further instructions that instruct the at least a processor to analyze potential interactions between different conditions … and identify compounding risk factors where multiple conditions may exacerbate each other.
Moheb teaches:
the at least a memory contains further instructions that instruct the at least a processor to analyze potential interactions between different conditions … and identify compounding risk factors where multiple conditions may exacerbate each other (See Moheb [0048] the system can change a classification to high risk when certain other diseases are present that cause oral complications. These multiple conditions include at least: diabetes, heart disease, cancer, obesity, immune deficiency, Alzheimer disease, anemia, cystic fibrosis, rheumatoid arthritis, stroke, mumps, Parkinson disease, surgical removal of salivary glands, and dehydration.).
The system of Moheb is applicable to the disclosure of Ricci in view of Cronin as they both share characteristics and capabilities, namely, they are directed to analyzing dental imagery. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ricci to include disease comorbidities as taught by Moheb. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Ricci in order to increase the success rate and reduce the time patients spend in a dentist chair for diagnosis and treatment plan as a result of accurate and efficient calculations (See Moheb [0003]).
Regarding claim 19, Ricci in view of Cronin and Moheb discloses the system of claim 9 as discussed above. Claim 19 recites a method that is substantially similar to the method performed by the system of claim 9. Accordingly, claim 19 is rejected based on the same analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ezhov et al. (U.S. 2024/0029901) teaches a system and method for generating a personalized medical summary from a practitioner-patient conversation, including dental and non-dental medical information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN L HANKS whose telephone number is (571)270-5080. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.L.H./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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