DETAILED ACTION
Remarks
This communication is in response to the amendment/arguments filed on December 8, 2025 has been fully considered. The rejection is made final. Claims 1-6 are pending for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the 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 Notes
Examiner cites particular columns and line numbers 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 that, in preparing responses, the applicant 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.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Response to Arguments
Applicant's arguments filed December 8, 2025 have been fully considered but they are not persuasive.
Applicant on pages 6-7 states about claims 1, 2-3 and 5 that the limitations “determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity, wherein the grouping rules-based determination and the machine learning model-based determination are determined in parallel” are not mental process because these limitations are provide several technical benefits that are concrete computer-function improvements.
As in Sragow, a human can determine that H Sragow in one record represents the same person as Howard Sragow in another record due to both these records corresponding to the same household, having the same last name (i.e., grouping, based on the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity). The claims additional elements “machine learning model” is a generic computer component (e.g., software) being used to implement the abstract idea. Simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide improvements to the functioning of a computer or to any other technology or technical field; and does not integrate a judicial exception into a practical application, see Alice Corp. v. CLS Bank or Mayo Collaborative Services v. Prometheus Labs. The claims are clearly directed to an abstract idea without significantly more. The focus of the claims here is not an improvement in computers as tools, but on certain independent abstract ideas that use computers as tools for collecting information, analyzing it, and displaying certain results of the collection and analysis. The specification may purport an improvement using a conventional mechanism (please see paragraph [0021]), but the claims do not focus on how the usage of that mechanism alters the system in a way that leads to an improvement in the technology of the system. The specification does not distinguish between the invention and conventional solutions. The specification contains no disclosure of an improvement to the functionality of the computer itself. The focus should be on improving the tool (i.e., computer), rather than merely showing that an abstract idea is beneficially improvement on a computer. The examiner is unable to find any argument contradicting this position, thus, applicant’s arguments are not deemed persuasive. Therefore, the present claims are directed to patent ineligible subject matter.
Applicant on pages 7-8 states about claims 4 and 6 that the limitations “generating, based on one or more machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action” are not mental process because these limitations are integrated into a practical application.
A human can mentally group names based on rule that if the person has computer science degree, then he/she be in “software development” group. Here “machine learning model” is considered as “apply it”. The claims additional elements “machine learning model” is a generic computer component (e.g., software) being used to implement the abstract idea. Simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide improvements to the functioning of a computer or to any other technology or technical field; and does not integrate a judicial exception into a practical application, see Alice Corp. v. CLS Bank or Mayo Collaborative Services v. Prometheus Labs. The word “apply it” is not a must requisite describe in the MPEP 2106.05(f) “For claim limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent)” emphasis (equivalent).
In response to Applicant’s argument on page 10-11 that “The cites references either alone or in combination, have not been shown in the office action to teach “determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity””, is acknowledged but not deemed to be persuasive.
Sragow [0173] discloses the overmatch exclusion module 514 can determine whether the date of birth in the household-sharing records 410 is the exact same and whether names in these same records 410 are known nicknames of each other. Known nicknames can be designated names that are grouped together as being different versions of the same name (i.e., determining, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity). … If a first record 410 includes the name Richard and the birth date 15 Nov. 1976, and a second record 410 includes the name Dick and the birth date 15 Nov. 1976, then the overmatch exclusion module 514 can determine that these records 410 represent the same person at 716. … the overmatch exclusion module 414 may determine that H Sragow in one record 410 represents the same person as Howard Sragow in another record 410 due to both these records corresponding to the same household, having the same last name (i.e., determining, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity). Sragow [0177]) discloses a group exclusivity rule or criterion is applied to the records 410 being considered for matching to the same person (i.e., grouping rules-based determination). Sragow [0227] and 0233-02340 discloses one or more of the record matching systems described herein may be implemented in an AI or machine-learning system. … The neural network 1302 may be used to implement the machine learning as described herein, and various implementations may use other types of machine learning networks (i.e., record matching for grouping is based on one or more machine learning models). Therefore, Sragow teaches the above limitation of claim 1.
In response to Applicant’s argument on page 11-12 that “The cites references either alone or in combination, have not been shown in the office action to teach “grouping, based on the user input, the first data record and the second data record.””, is acknowledged but not deemed to be persuasive.
Sragow [0046] discloses the external order processing device may communicate with an internal pharmacy order processing device and/or other devices located within the system 100. In some implementations, the external order processing device may receive a user requesting fulfillment of a prescription drug (i.e., user input). Sragow [0060] prescription data 126 include user names, medication or treatment (such as lab tests), dosing information, etc. Sragow [0153], [0173] discloses the overmatch exclusion module 514 can determine whether the date of birth in the household-sharing records 410 is the exact same and whether names in these same records 410 are known nicknames of each other. Known nicknames can be designated names that are grouped together as being different versions of the same name (i.e., grouping, based on the user input, the first data record and the second data record). … If a first record 410 includes the name Richard and the birth date 15 Nov. 1976, and a second record 410 includes the name Dick and the birth date 15 Nov. 1976, then the overmatch exclusion module 514 can determine that these records 410 represent the same person at 716. … the overmatch exclusion module 414 may determine that H Sragow in one record 410 represents the same person as Howard Sragow in another record 410 due to both these records corresponding to the same household, having the same last name (i.e., grouping, based on the user input, the first data record and the second data record). Sragow [0107], [0173] discloses that the manager device 402 can examine the groups (e.g., pairs) of names in a set of records 410 and measure how often each combination of these grouped names has the same distinguishing feature or a different distinguishing feature in the demographic information associated with each name in the group. If the percentage affinity of each is greater than a designated threshold, and occurs with sufficient volume for confidence, then the manager device 402 decides that the combination of names represent nicknames or different versions of the name for the same person (i.e., grouping, based on the user input, the first data record and the second data record). Therefore, Sragow teaches the above limitation of claim 1.
In response to Applicant’s argument on page 12-13 that “The cites references either alone or in combination, have not been shown in the office action to teach “generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules””, is acknowledged but not deemed to be persuasive.
Lucas [0109] discloses If the DREAMS? Engine is the heart of the platform, the Knowledge Engine 15 is its brains, using advanced artificial intelligence (AI) and machine learning to analyze both patient-specific data and de-identified population-wide data. Lucas [0174-0175] discloses that the rules-based machine-learning component. Lucas [0186] discloses the ongoing utilization of machine learning against de-identified data stores gives the user organization the ability to discover deeper insights not otherwise available in other platforms. Lucas [0199] discloses applying a rules-based decision algorithm, based on the health and wellness plans, the person's health system records and real-time health and wellness information, to trigger one or more updates selected from the group (i.e., generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules). Therefore, Lucas teaches the above limitation of claim 1.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent Claims 1, 4, 5 and 6:
Step 1 Analysis:
Claims 1 and 4 recite “A system …”; therefore, the claim is a machine.
Claims 5 and 6 recite “A method…”, the claim recites a series of steps and therefore is process.
Step 2A Prong One Analysis: The claim, under the broadest reasonable interpretation, recites limitations directed to an abstract idea, including mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of mere instructions to apply an exception language. In particular, the following limitations are directed to an abstract idea:
identifying at least two different data records of a plurality of different data records, wherein each data record is associated with a respective entity, wherein each data record includes a plurality of respective record fields and corresponding record field values, and wherein at least a first record field value of a first data record is different from a corresponding first record field value of a second data record;
determining, based on a plurality of different grouping rules, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise a same entity;
determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity, wherein the grouping rules-based determination and the machine learning model-based determination are determined in parallel;
presenting, in response to the grouping rules-based determination indicating the respective entities are the same entity, a first graphical user interface element of a graphical user interface;
presenting a second graphical user interface element of the graphical user interface indicating whether the machine learning-based determination indicates that the respective entities are the same entity or not the same entity;
receiving, through the graphical user interface, a user input;
grouping, based on the user input, the first data record and the second data record.
Step 2A - Prong Two: Integrated into a Practical Application
The judicial exception is not integrated into a practical application. In particular, the additional steps: the “identifying”, “determining”, “presenting”, “receiving”, “grouping” and “executing” steps mount to data gathering data which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “grouping”, and “executing” steps are considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, the 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the step of “identifying”, “determining”, “presenting”, “receiving”, “grouping” and “executing” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements 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.
Accordingly, claims 1 and 4 are directed to an abstract idea.
Independent claims 5 and 6 have the similar limitations as claims 1 and 4 respectively and are rejected for at least the same reasons as claims 1 and 4.
Regarding claim 2. The system of claim 1, wherein the instructions further cause the system to perform: determining a respective performance for each of the one or more grouping rules; generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action; executing the grouping rule recommendation action.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 3. The system of claim 1, wherein the instructions further cause the system to perform: determining a respective performance for each of the one or more machine learning models; generating, based on one or more other machine learning models and the respective performances of the one or more machine learning models, a grouping rule recommendation action; executing the grouping rule recommendation action.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mathew et al. (US Patent Publication No. 2021/0065150 A1, ‘Mathew’, hereafter) in view of Sragow et al. (US Patent Publication No. 2022/0180988 A1, ‘Sragow’, hereafter).
Regarding claim 1. Mathew teaches a system comprising: one or more processors; and
memory storing instructions that, when executed by the one or more processors (A user device can also implement and/or be used with features described herein. Example user devices can be computer devices including some similar components as the device 1000, e.g., processor(s) 1002, memory 1006, etc. An operating system, software and applications suitable for the client device can be provided in memory and used by the processor. One or more methods can be performed as part of or component of an application running on the system, or as an application or software running in conjunction with other applications and operating systems, Mathew [0234-0235), cause the system to perform:
identifying at least two different data records of a plurality of different data records, wherein each data record is associated with a respective entity, wherein each data record includes a plurality of respective record fields and corresponding record field values, and wherein at least a first record field value of a first data record is different from a corresponding first record field value of a second data record (Mathew [0004], [0218]);
receiving, through the graphical user interface, a user input (Mathew [0141], [0146-0148] and Fig. 6C);
Mathew does not teach
determining, based on a plurality of different grouping rules, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise a same entity;
determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity, wherein the grouping rules-based determination and the machine learning model-based determination are determined in parallel;
presenting, in response to the grouping rules-based determination indicating the respective entities are the same entity, a first graphical user interface element of a graphical user interface;
presenting a second graphical user interface element of the graphical user interface indicating whether the machine learning-based determination indicates that the respective entities are the same entity or not the same entity;
grouping, based on the user input, the first data record and the second data record.
However, Sragow teaches
determining, based on a plurality of different grouping rules, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise a same entity (Sragow [0107], [0153], [0173]);
determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity (Sragow [0177]), wherein the grouping rules-based determination and the machine learning model-based determination are determined in parallel (Sragow [0233-0234]);
presenting, in response to the grouping rules-based determination indicating the respective entities are the same entity, a first graphical user interface element of a graphical user interface (Sragow [0118]);
presenting a second graphical user interface element of the graphical user interface indicating whether the machine learning-based determination indicates that the respective entities are the same entity or not the same entity (Sragow [0234]);
grouping, based on the user input, the first data record and the second data record (Sragow [0107], [0153], [0173]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Mathew and Sragow before him/her, to modify Mathew with the teaching of Sragow’s systems and methods for patient record matching. One would have been motivated to do so for the benefit of efficient way of patient’s health record (i.e., data records) matching using an artificial intelligence (AI) record matching system (Sragow, Abstract, [0005]).
Regarding claim 5, although claim 5 directed to a method, it is similar in scope to claim 1. The system steps of claim 1 substantially encompass the method recited in claim 5. Therefore; claim 5 is rejected for at least the same reason as claim 1 above.
Regarding claim 6, although claim 6 directed to a method, it is similar in scope to claim 4. The system steps of claim 4 substantially encompass the method recited in claim 6. Therefore; claim 6 is rejected for at least the same reason as claim 4 above.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Mathew et al. (US Patent Publication No. 2021/0065150 A1, ‘Mathew’, hereafter) in view of Sragow et al. (US Patent Publication No. 2022/0180988 A1, ‘Sragow’, hereafter) and further in view of Lucas et al. (US Patent Publication No. 2024/0339226 A1, ‘Lucas’, hereafter).
Regarding claim 2. Mathew and Sragow do not teach, wherein the instructions further cause the system to perform:
determining a respective performance for each of the one or more grouping rules;
generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action; executing the grouping rule recommendation action.
However, Lucas teaches wherein the instructions further cause the system to perform:
determining a respective performance for each of the one or more grouping rules (Sragow [0153], [0173]);
generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action; executing the grouping rule recommendation action (Lucas [0199]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Mathew, Sragow and Lucas before him/her, to further modify Mathew with the teaching of Sragow’s integrated health and wellness platform for health care, wellness, conditioning, fitness, and high-performance management. One would have been motivated to do so for the benefit of using rule-base decision algorithm to suggest or recommend new activities for the improvement of patient’s health (Lucas, Abstract, [0199]).
Regarding claim 3. Mathew as modified teaches, wherein the instructions further cause the system to perform:
determining a respective performance for each of the one or more machine learning models (Sragow [0233-0234]);
generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action; executing the grouping rule recommendation action (Lucas [0199]).
Regarding claim 4. Mathew teaches a system comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors (A user device can also implement and/or be used with features described herein. Example user devices can be computer devices including some similar components as the device 1000, e.g., processor(s) 1002, memory 1006, etc. An operating system, software and applications suitable for the client device can be provided in memory and used by the processor. One or more methods can be performed as part of or component of an application running on the system, or as an application or software running in conjunction with other applications and operating systems, Mathew [0234-0235), cause the system to perform:
identifying one or more grouping rules of a plurality of different grouping rules, wherein each of the grouping rules is configured to identify whether at least two different data records of a plurality of different data records are each associated with a same entity (Mathew [0004], [0218]), and
executing the one or more grouping rules on the plurality of different data records (Mathew [0004], 0081], [0217-0221]);
Mathew does not explicitly teach
wherein the plurality of different data records are deployed in a production environment;
determining a respective performance for each of the one or more grouping rules;
However, Sragow teaches
wherein the plurality of different data records are deployed in a production environment (Sragow [0237]);
determining a respective performance for each of the one or more grouping rules (Sragow [0153], [0173]);
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Mathew and Sragow before him/her, to modify Mathew with the teaching of Sragow’s systems and methods for patient record matching. One would have been motivated to do so for the benefit of efficient way of patient’s health record (i.e., data records) matching using an artificial intelligence (AI) record matching system (Sragow, Abstract, [0005]).
Mathew and Sragow do not teach
generating, based on one or more machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action;
executing the grouping rule recommendation action.
However, Lucas teaches
generating, based on one or more other machine learning models and the respective performances of the one or more grouping rules, a grouping rule recommendation action (Lucas [0199]);
executing the grouping rule recommendation action (Lucas [0199]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Mathew, Sragow and Lucas before him/her, to further modify Mathew with the teaching of Sragow’s integrated health and wellness platform for health care, wellness, conditioning, fitness, and high-performance management. One would have been motivated to do so for the benefit of using rule-base decision algorithm to suggest or recommend new activities for the improvement of patient’s health (Lucas, Abstract, [0199]).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9:30AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168