DETAILED ACTION
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 § 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, 2, 6-12, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention
is directed to abstract idea without significantly more.
Step 1
Claims 1, 2, 6-12, and 17-20 are within the four statutory categories. However, as will be
shown below, claims 1, 2, 6-12, and 17-20 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 11, and 12 are representative of the inventive concept and recite:
Claim 1
A method for normalizing health related events (HREs) count variables, the method comprising:
storing, in a storage unit that comprises a volatile memory unit and a non-volatile memory unit, the HREs count variables, the HREs count variables represent an occurrence of HREs of different types in relation to a group of patients;
granting, to a plurality of users, a remote access to the storage unit via one or more man machine interfaces, thereby facilitating an update of the HREs count variables by one or more users of the plurality of users;
converting, by a processor that comprises one or more processing circuits, the HREs count variables to normalized HRE information items; wherein a HRE count variable represents a number of occurrences of a HER of a given type of the different types during a defined period in relation to a patient of the group; wherein a normalized HRE information item related to the HRE count variable is normalized to the patients of the group and is normalized to the HREs of the different types that are related to the patient; wherein the converting comprises applying a term frequency and inverse document frequency (TF-IDF) process; wherein the applying comprises (i) calculating, per patient, a proportionate HRE occurrence of each HRE of the different types of HREs that are related to the patient; and (ii) calculating, for each HRE type, an inverse document frequency value that is indicative of a number of patients of the group that experienced, during the defined period, the HRE of the type: wherein for each HRE type, the inverse document frequency value equals (a) a number of patients of the group divided by (b) the number of the patients of the group that experienced, during the defined period, the HRE of the type;
storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items makes available to at least one user of the plurality of users the normalized HRE information items.
Claim 11
A method for normalizing health related events (HREs) count variables, the method comprising:
storing, in a storage unit that comprises a volatile memory unit and a non-volatile memory unit, the HREs count variables, the HREs count variables represent an occurrence of HREs of different types in relation to a group of patients;
granting, to a plurality of users, a remote access to the storage unit via one or more man machine interfaces, thereby facilitating an update of the HREs count variables by one or more users of the plurality of users;
and converting, by a processor that comprises one or more processing circuits, the HREs count variables to normalized HRE information items; wherein a HRE count variable represents a number of occurrences of a HRE of a given type of the different types during a defined period in relation to a patient of the group; wherein a normalized HRE information item related to the HRE count variable is normalized to the patients of the group and is normalized to the HREs of the different types that are related to the patient; wherein the converting comprises applying a term frequency and inverse document frequency (TF-IDF) process;
and storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items makes available to at least one user of the plurality of users the normalized HRE information items;
wherein the method further comprises (i) applying to a health related data of a patient, a machine learning method adapted to convert parameters of the health related data, some of which may be indicative of a diagnosis of an autoimmune disease, into a vector that provides a compact representation of the health related data that reflects a medical condition of the person; and(ii) applying a classifier model to the vector generated in step (i) to identify whether the medical condition of the person indicates a likelihood of the person having or developing a disease selected out of an autoimmune disease or a chronic disease; wherein the health related data comprises the normalized HRE information items.
Claim 12
A non-transitory computer readable medium that stores instructions for normalizing health related events (HREs) count variables, the non- transitory computer readable medium stores instruction that once executed by a computerized system cause the computerized system to:
store, in a storage unit that comprises a volatile memory unit and anon-volatile memory unit, the HREs count variables, the HREs count variables represent an occurrence of HREs of different types in relation to a group of patients;
grant, to a plurality of users, a remote access to the storage unit via one or more man machine interfaces, thereby facilitating an update of the HREs count variables by one or more users of the plurality of users;
convert ,by a processor that comprises one or more processing circuits, the processor and the storage unit belong to the computerized system, the HREs count variables to normalized HRE information items; wherein a HRE count variable represents a number of occurrences of a HRE of a given type of the different types during a defined period in relation to a patient of the group; wherein a normalized HRE information item related to the HRE count variable is normalized to the patients of the group and is normalized to the HREs of the different types that are related to the patient; wherein the converting comprises applying a term frequency and inverse document frequency (TF-IDF) process; wherein the applying comprises (i) calculating, per patient, a proportionate HRE occurrence of each HRE of the different types of HREs that are related to the patient; and (ii) calculating, for each HRE type, an inverse document frequency value that is indicative of a number of patients of the group that experienced, during the defined period, the HRE of the type; wherein for each HRE type, the inverse document frequency value equals (a) a number of patients of the group divided by (b) the number of the patients of the group that experienced, during the defined period, the HRE of the type;
and store the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items make available to at least one user of the plurality of users the normalized HRE information items.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
converting, calculating, applying, and granting) or using pen and paper. Other than reciting generic computer components/functions such as “storage unit”, “processor that comprises one or more processing circuits”, “non-transitory computer readable medium that stores instructions”, “computerized system”, and “machine interfaces”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the method language, the claim encompasses the user organizing data, analyzing it, and making is available for later access. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions of granting also covers behavioral or interactions between people (i.e. the system and processor), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2, 6-10, and 17-20 recite additional subject matter which further narrows or defines the abstract idea embodied in the claim.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims
recite the following additional limitations:
Claim 1 recites: “storage unit that comprises a volatile memory unit and a non-volatile memory unit”, “storage unit via one or more man machine interfaces”, “processor that comprises one or more processing circuits”, and “storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items makes available to at least one user of the plurality of users the normalized HRE information items”
Claim 11 recites: “storage unit that comprises a volatile memory unit and a non-volatile memory unit”, “storage unit via one or more man machine interfaces”, “processor that comprises one or more processing circuits”, “storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items makes available to at least one user of the plurality of users the normalized HRE information items”, and “machine learning”
Claim 12 recites: “A non-transitory computer readable medium that stores instructions”, “non- transitory computer readable medium stores instruction that once executed by a computerized system cause the computerized system”, “storage unit via one or more man machine interfaces”, “processor that comprises one or more processing circuits, the processor and the storage unit belong to the computerized system”, and “store the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items make available to at least one user of the plurality of users the normalized HRE information items”
In particular, the additional elements do not integrate the abstract idea into a practical application,
other than the abstract idea per se, because the additional elements amount to no more limitations
which:
Amount to mere instructions to apply an exception. The limitations are recited as being
performed by “storage unit that comprises a volatile memory unit and a non-volatile memory unit”, “storage unit via one or more man machine interfaces”, “processor that comprises one or more processing circuits”, “machine learning” , “A non-transitory computer readable medium that stores instructions”, and “non- transitory computer readable medium stores instruction that once executed by a computerized system cause the computerized system”, which are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The model is used to generally apply the abstract idea without limiting how the model functions. The model (machine learning) is described at a high level such that it amounts to using a computer with a generic model to apply the abstract idea.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items makes available to at least one user of the plurality of users the normalized HRE information items”.
Dependent claim 20 recite machine learning/model
In particular, the additional elements do not integrate the abstract idea into a practical application,
other than the abstract idea per se, because the additional elements amount to no more limitations
which:
Amount to mere instructions to apply an exception. The limitations are recited as being
performed by machine learning/model, which are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The model is used to generally apply the abstract idea without limiting how the model functions. The model is described at a high level such that it amounts to using a computer with a generic model to apply the abstract idea.
Dependent claims 2, 6-10, and 17-19 do not include any additional elements beyond those already
recited in independent claims 1, 11, and 12 and dependent claim 20, hence do not integrate the aforementioned abstract idea into a particular application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 11, and 12 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: A method in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of an additional element such as:
Storing which is expressly used to record or process digital information in a storage medium for future use (Para 0011, Bingham(US 20100031349 A1) discloses: “FIG. 1 illustrates a conventional data storage system having a memory, controller and a connector for connecting to external computer systems or other system components…”) in a manner that would be well-understood, routine, and conventional.
Interface which is a device or program enabling a user to communicate with a computer(Para 47, Sastry(US 11138019 B1) discloses: “The IO interface 506 includes conventional interfaces to the computer 501 known in the art.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2, 6-10, and 17-19 do not include any additional elements beyond those already
addressed above for claims 1, 11, and 12 and dependent claim 20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 11, and 12 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
Subject Matter Free of Prior Art
Claims 5-7 , 16-18, 11, and 20 distinguish over the prior art for the following reasons.
The following is a statement of reasons for the subject matter free of prior art:
Claim 1 (in part):
“…converting, by a processor that comprises one or more processing circuits, the HREs count variables to normalized HRE information items; wherein a HRE count variable represents a number of occurrences of a HER of a given type of the different types during a defined period in relation to a patient of the group; wherein a normalized HRE information item related to the HRE count variable is normalized to the patients of the group and is normalized to the HREs of the different types that are related to the patient; wherein the converting comprises applying a term frequency and inverse document frequency (TF-IDF) process; wherein the applying comprises (i) calculating, per patient, a proportionate HRE occurrence of each HRE of the different types of HREs that are related to the patient; and (ii) calculating, for each HRE type, an inverse document frequency value that is indicative of a number of patients of the group that experienced, during the defined period, the HRE of the type: wherein for each HRE type, the inverse document frequency value equals (a) a number of patients of the group divided by (b) the number of the patients of the group that experienced, during the defined period, the HRE of the type…”
*Claims 11 and 12 contain similar elements as claim 1
The underlined elements above, in the combination recited, represent the elements of the claim that are considered to be subject matter free of prior art.
The closest available prior art of record as follows:
Chen(US20210257066A1) discloses a medical data classification method, but does not
fairly disclose or suggest the aforementioned configuration for the claimed invention.
Chen(US20250131184A1) discloses machine learning from medical records, but does not
fairly disclose or suggest the aforementioned configuration for the claimed invention.
Quatro(US20250045304A1) discloses a system for interpreting inputted information, but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Based on the evidence presented above, none of the closest available prior art of record fairly discloses or suggests the underlined elements of the claimed invention. For this reason, claims 1, 11, and 12 would be considered be subject matter free of prior art.
Claims 2, 6-10, and 17-20 would also be found to be subject matter free of prior art for at least the same rationale as applied to parent claims 1, 11, and 12 above, and incorporated herein.
Response to Arguments
35 U.S.C. 101
(Page 8) Regarding the assertion that per the Ex Parte Desjardins decision, the machine learning recited in claims 1 and 20 provides a solution that is concrete, accurate, and efficient for identifying whether the medical condition indicates likelihood of the person having or developing a disease.
Applicant's arguments filed have been fully considered but they are not persuasive. The claims currently recite the machine learning at a high level with minimal detail as to how the machine learning functions or operates. Claim 1 in the Ex Parte Desjardins describes in detail the technical detail on how the machine learning operates and makes obvious that the invention does not entail applying a generic machine learning model for a specific application.
(Page 9) Regarding the assertion that the solution exhibits technical benefits such as increased accuracy and enhanced relevancy and claim 12 integrates the abstract idea into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. This judicial exception is not integrated into a practical application. The additional elements identified in the claims do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception and Add insignificant extra-solution activity to the abstract idea. Even though the method includes a more involved way to normalize variables, the method itself is considered generally abstract and can be done mentally and using pen/paper.
(Page 10) Regarding the assertion that the features exhibited in example 42, exhibit similar features as recited in the independent claims, hence integrates the abstract idea into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. The amended claim as a whole integrates mental processes and certain methods of organizing human activity and merely utilizes generic computers to perform abstract functions. The claim in example 42 contains specific elements which recite a specific improvement over prior art systems. The amended claim in question is not focused on allowing remote users to share information in real time in a standardized format, rather, its main intent is to describe generic data processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Weinrich(US20230343443A1) discloses a system for hands-free medical data extraction, hazard detection, and digital biometric patient identification. Some disclosures of this invention are similar to that of this instant pending application.
Xu(US20220171946A1) discloses enhanced logits for natural language processing. Some disclosures of this invention are similar to that of this instant pending application.
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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/S.G.P./Examiner, Art Unit 3685
/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685