DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
2. This communication is in response to the communication filed 6/18/2026. Claims 9, 16-17 and 22 are cancelled. Claims 1, 11 and 18 are currently amended. Claims 1-8, 10-15, 18-21 and 23 are currently pending.
Claim Rejections - 35 USC § 101
3. 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.
3.1. Claims 1-8, 10-15, 18-21, and 23 are rejected under 35 U.S.C. § 101 because while the claims (1) are to a statutory category (i.e., process, machine, manufacture or composition of matter, the claims (2A1) recite an abstract idea (i.e., a law of nature, a natural phenomenon); (2A2) do not recite additional elements that integrate the abstract idea into a practical application; and (2B) are not directed to significantly more than the abstract idea itself.
In regards to (1), the claims are to a statutory category (i.e., statutory categories including a process, machine, manufacture or composition of matter). In particular, independent claims 1, 11 and 18, and their respective dependent claims are directed, in part, to methods and systems for determining an optimal sample size for a contract.
In regards to (2A1), the claims, as a whole, recite and are directed to an abstract idea because the claims include one or more limitations that correspond to an abstract idea including mental processes and/or certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. For example, independent claims 1, 11 and 18, as a whole, are directed to determining an optimal sample size for a contract by, in part, receiving data, determining a sampling distribution, determining a significance threshold, determining a probability, determining an expected star value, and presenting the expected star value which are human activities and/or interactions and therefore, certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. The dependent claims include all of the limitations of their respective independent claims and thus are directed to the same abstract idea identified for the independent claims but further describe the elements and/or recite field of use limitations.
Furthermore, the claims are also directed to an abstract idea because the claims, except for certain limitations (* identified below in bold), under the broadest reasonable interpretation, can be reasonably and practically performed in the human mind and/or with pen and paper using observation, evaluation, judgment and/or opinion. That is, other than reciting the certain additional elements, nothing in the claims precludes the limitations from being practically performed in the mind and/or with pen and paper.
CLAIM 1:
A system, comprising:
one or more processors; and
one or more non-transitory computer readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
extracting electronic response data from data collection objects, wherein the electronic response data includes electronic data representing response scores to queries in the data collection objects;
applying a data processing technique to the extracted electronic response data, the data processing technique including a data cleansing technique that detects and corrects inconsistencies caused by entry errors or corruption;
determining a sampling distribution for each of one or more sample sizes based on the response scores;
determining a significance threshold and a reliability coefficient for each of the one or more sample sizes, the reliability coefficient being output in an electronic form suitable for comparison with an adjustment grid, the adjustment grid being representable as columns and rows that indicate quantification of reliability and/or population distributions;
determining a probability for each of one or more performance measure values for the one or more sample sizes based on a cumulative distributive function (CDF) of the sampling distribution and one or more parameters of the adjustment grid;
determining an expected performance measure value for each of the one or more sample sizes based on the probability determined for each of the one or more performance measure values; and
generating commands for causing a presentation of the expected performance measure value determined for each of the one or more sample sizes in a user interface of a device, wherein at least one of the one or more sample sizes with a highest expected performance measure value is recommended, by automatically displaying the at least one of the one or more sample sizes as an optimal sample size;
receiving an update to at least one of the plurality of different performance measures, the update being received by a process including extracting additional electronic response data;
applying the data processing technique to the additional electronic response data;
determining a change in the highest expected performance value based on the update to the at least one of the plurality of different performance measures; and
causing the user interface to update in accordance with the change in the highest expected performance measure value and display one or more of the updated plurality of different performance measures, the user interface including interactive areas that present the plurality of different performance measures,
wherein the highest expected performance value is an aggregate performance value that is generated based on: (i) an aggregation of the plurality of different performance measures in a manner that identifies the optimal sample size according to the aggregation and (ii) the probability for each of the one or more performance measure values based on the CDF of the sampling distribution and the one or more parameters of the adjustment grid.
CLAIM 2:
The system of claim 1, wherein determining the probability for each performance measure value comprises:
determining a curve indicating the sampling distribution for the one or more sample sizes; and
determining a probability of a contract achieving each performance measure value by determining an area under the curve of a probability density function of the sampling distribution, wherein the curve is partitioned by at least one of a cutpoints, a significance threshold, or a reliability assignment according to the adjustment grid.
CLAIM 3:
The system of claim 1, wherein determining the expected performance measure value comprises: determining a mean across each expected performance measure, wherein the expected performance measure is a product of the probability for each performance measure value and the performance measure value.
CLAIM 4:
The system of claim 1, wherein the user interface includes a visual representation of the expected performance measure value determined for each of the one or more sample sizes, as a function of the one or more sample sizes at a contract level and a measure level.
CLAIM 5:
The system of claim 1, wherein the significance threshold is an output of a two sided t-test that compares a mean of a contract to an average of one or more other contracts.
CLAIM 6:
The system of claim 1, wherein the reliability coefficient compares a variance of a contract to variances between one or more other contracts.
CLAIM 7:
The system of claim 2, further comprising: determining a standard error for each of the one or more sample sizes, wherein the standard error measures variation in contract values, and wherein the standard error is based on the response scores and a user size.
CLAIM 8:
The system of claim 7, wherein each contract value is a mean of the response scores to the queries in the data collection objects of the user size is a product of a response rate and a sample size.
CLAIM 10:
The system of claim 1, wherein the data collection objects include a Consumer Assessment of Health Care Providers and Systems (CAHPS).
CLAIM 11:
A computer-implemented method comprising:
extracting, by one or more processors, electronic response data from data collection objects, wherein the electronic response data includes electronic data representing response scores to queries in the data collection objects;
applying, by the one or more processors, a data processing technique to the extracted electronic response data, the data processing technique including a data cleansing technique that detects and corrects inconsistencies caused by entry errors or corruption;
determining, by the one or more processors, a sampling distribution for each of one or more sample sizes based on the response scores;
determining, by the one or more processors, a significance threshold and a reliability coefficient for each of the one or more sample sizes, the reliability coefficient being output in an electronic format suitable for comparison with an adjustment grid, the adjustment grid being representable as columns and rows that indicate quantification of reliability and/or population distributions;
determining, by the one or more processors, a probability for each of one or more performance measure values for the one or more sample sizes based on a cumulative distributive function (CDF) of the sampling distribution and one or more parameters of an adjustment grid;
determining, by the one or more processors, an expected performance measure value for each of the one or more sample sizes based on the probability determined for each of the one or more performance measure values; and
generating commands for causing, by the one or more processors, a presentation of the expected performance measure value calculated for each of the one or more sample sizes in a user interface of a device, wherein at least one of the one or more sample sizes with a highest expected performance measure value is recommended by automatically displaying the at least one of the one or more sample sizes as an optimal sample size, the highest expected performance measure value being determined based on a plurality of different performance measures;
receiving, by the one or more processors, an update to at least one of the plurality of different performance measures, the update being received by a process including extracting additional electronic response data;
applying, by the one or more processors, the data processing technique to the additional electronic response data;
determining a change in the highest expected performance measure value based on the update to the at least one of the plurality of different performance measures; and
causing the user interface to update in accordance with the change in the highest expected performance measure value and display one or more of the updated plurality of different performance measures, the user interface including interactive areas that present the plurality of different performance measures,
wherein the highest expected performance value is an aggregate performance value that is generated based on: (i) an aggregation of the plurality of different performance measures in a manner that identifies the optimal sample size according to the aggregation and (ii) the probability for each of the one or more performance values based on the CDF of the sampling distribution and the one or more parameters of the adjustment grid.
CLAIM 12:
The computer-implemented method of claim 11, wherein determining the probability for each performance measure value comprises:
determining, by the one or more processors, a curve indicating the sampling distribution for the one or more sample sizes; and
determining, by the one or more processors, a probability of a contract achieving each performance measure value by determining an area under the curve of a probability density function of the sampling distribution, wherein the curve is partitioned by at least one of a cutpoints, a significance threshold, or a reliability assignment according to the adjustment grid.
CLAIM 13:
The computer-implemented method of claim 11, wherein determining the expected performance measure value comprises:
determining, by the one or more processors, a mean across each expected performance measure, wherein the expected measure performance measure is a product of the probability for each performance measure value and the performance measure value.
CLAIM 14:
The computer-implemented method of claim 11, wherein the user interface includes a visual representation of the expected performance measure value determined for each of the one or more sample sizes, as a function of the one or more sample sizes at a contract level and a measure level.
CLAIM 15:
The computer-implemented method of claim 11, wherein the significance threshold is an output of a two sided t-test that compares a mean of a contract to an average of one or more other contracts.
CLAIM 18:
One or more non-transitory computer readable media storing processor executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
extracting electronic response data from data collection objects, wherein the electronic response data includes electronic data representing response scores to queries in the data collection objects;
applying a data processing technique to the extracted electronic response data, the data processing technique including a data cleansing technique that detects and corrects inconsistencies caused by entry errors or corruption;
determining a sampling distribution for each of one or more sample sizes based on the response scores;
determining a significance threshold and a reliability coefficient for each of the one or more sample sizes, the reliability coefficient being output in an electronic form suitable for comparison with an adjustment grid, the adjustment grid being representable as columns and rows that indicate quantification of reliability and/or population distributions;
determining a probability for each of one or more performance measure values for the one or more sample sizes based on a cumulative distributive function (CDF) of the sampling distribution and one or more parameters of the adjustment grid;
determining an expected performance measure value for each of the one or more sample sizes based on the probability determined for each of the one or more performance measure values; and
generating commands for causing a presentation of the expected performance measure value determined for each of the one or more sample sizes in a user interface of a device, wherein at least one of the one or more sample sizes with a highest expected performance measure value is recommended by automatically displaying the at least one of the one or more sample sizes as an optimal sample size, the highest expected performance measure value being determined based on a plurality of different performance measures;
receiving an update to at least one of the plurality of different performance measures, the update being received by a process including extracting additional electronic response data;
applying the data processing technique to the additional electronic response data;
determining a change in the highest expected performance measure value based on the update to the at least one of the plurality of different performance measures; and
causing the user interface to update in accordance with the change in the highest expected performance measure value and display one or more of the updated plurality of different performance measures, the user interface including interactive areas that present the plurality of different performance measures,
wherein the highest expected performance value is an aggregate performance value that is generated based on: (i) an aggregation of the plurality of different performance measures in a manner that identifies the optimal sample size according to the aggregation and (ii) the probability for each of the one or more performance values based on the CDF of the sampling distribution and the one or more parameters of the adjustment grid.
CLAIM 19:
The one or more non-transitory computer readable media of claim 18, wherein determining the probability for each performance measure value comprises:
determining a curve indicating the sampling distribution for the one or more sample sizes; and
determining a probability of a contract achieving each performance measure value by determining an area under the curve of a probability density function of the sampling distribution, wherein the curve is partitioned by at least one of a cutpoints, a significance threshold, or a reliability assignment according to the adjustment grid.
CLAIM 20:
The one or more non-transitory computer readable media of claim 18, wherein determining the expected performance measure value comprises: determining a mean across each expected performance measure, wherein the expected performance measure is a product of the probability for each performance measure value and the performance measure value.
CLAIM 21:
The system of claim 1, wherein:
the electronic response data that is received from the data collection objects was generated by interactions with a first device after having received input from a first user; and
The device is configured to present the expected performance measure value on a second device for a second user.
CLAIM 23:
The system of claim 1, wherein the aggregation of the expected performance measure values is normalized such that a plurality of expected performance measure values are represented with a unified scoring scale used for presentation of the highest expected performance measure value, the aggregation being performed with normalized expected performance measure values such that the expected performance measure value is a normalized value.
* The limitations that are in bold are considered “additional elements” that are further analyzed below in subsequent steps of the 101 analysis. The limitations that are not in bold are abstract and/or can be reasonably and practically performed in the human mind and/or with pen paper.
Furthermore, the claims recite determining a significance threshold, a reliability coefficient for each of the one or more sample sizes, a probability for each of the one or more performance measure values, an expected performance measure value, an optimal sample size, etc. which are mathematical calculations. As such, the claims may also be properly categorized under the abstract category of mathematical concepts.
In regards to (2A2), the claims do not recite additional elements that integrate the abstract idea into a practical application. The additional elements in the claims (i.e., * identified above in bold) do not integrate the abstract idea into a practical application because the additional elements merely add insignificant extra-solution activity to the abstract idea; merely link the use of the judicial exception to a particular technological environment or field of use; and/or simply append technologies and functions, specified at a high level of generality, to the abstract idea (i.e., the additional elements do not amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer).
Here, the additional elements (e.g., one or more processors, non-transitory computer readable medium, interface, computer, etc.) are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea using generic computer technologies. Moreover, the claims recite “cause the one or more processors to perform operations”, etc. devoid of any meaningful technological improvement details and thus, further evidence the additional elements are merely being used to leverage generic technologies to automate what otherwise could be done manually. 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.
Furthermore, the additional elements do not recite improvements to the functioning of a computer, or to any other technology or technical field—the additional elements merely recite general purpose computer technology; the additional elements do not recite applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition—there is no actual administration of a particular treatment; the additional elements do not recite applying the judicial exception with, or by use of, a particular machine—the additional elements merely recite general purpose computer technology; the additional elements do not recite limitations effecting a transformation or reduction of a particular article to a different state or thing—the additional elements do not recite transformation such as a rubber mold process; the additional elements do not recite applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment—the additional elements merely leverage general purpose computer technology to link the abstract idea to a technological environment.
In regards to (2B), the claims, individually, as a whole and in combination with one another, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of (A) a generic computer structure(s) that serves to perform computer functions that serve to merely link the abstract idea to a particular technological environment (i.e., computers); and/or (B) functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Here, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer technologies. Mere instructions to apply an exception using generic computer technologies cannot provide an inventive concept.
Moreover, paragraphs [0027]-[0030] of applicant's specification (US 2025/0125023) recites that the system/method is implemented using equipment such as hand-held computers, desktop computers, laptop computers, wireless communication devices, cell phones, smartphones, mobile communications devices, a Personal Communication System (PCS) device, tablets, server computers, gateway computers, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof which are well-known general purpose or generic-type computers and/or technologies. The use of generic computer components recited at a high level of generality to process information through an unspecified processor/computer does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). 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 improves any other technology. Their collective functions merely provide conventional computer implementation.
Furthermore, the additional elements are merely well-known general purpose computers, components and/or technologies that receive, transmit, store, display, generate and otherwise process information which are akin to functions that courts consider well-understood, routine, and conventional activities previously known to the pertinent industry, such as, performing repetitive calculations; receiving or transmitting data over a network; electronic recordkeeping; retrieving and storing information in memory; and sorting information (See, for example, MPEP § 2106).
Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
Response to Arguments
4. Applicant's arguments filed 6/18/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 6/18/2026.
4.1. Applicant argues, on pages 14-17 of the response, that the features of the claims are entirely unrelated to an abstract idea, such as, the amended limitations reciting data cleansing techniques that are useful for ensuring accurate importation of response data from data collection objects.
It is respectfully reiterated that that the claimed invention is directed to determining an optimal sample size for a contact to calculate performance measure values or star ratings of healthcare providers based on patient surveys to thereby improve healthcare. These are human activities and/or interactions and thus, properly categorized as certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer.
Furthermore, the claims are also directed to an abstract idea because the claims recite and are directed to mental concepts. For example, it is submitted that extracting response data, determining sampling distribution, determining a significance threshold and a reliability coefficient, determining a probability for each performance measure value, determining an expected performance measure value, presenting the expected performance measure values and recommended optimal sample sizes, and the like are mental concepts because they can be performed manually by a human in the human mind and/or with pen and paper using observation, evaluation, judgment and/or opinion.
Moreover, the amended limitations in the independent claims, namely, “applying a data processing technique to the extracted electronic response data, the data processing technique including a data cleansing technique that detects and corrects inconsistencies caused by data entry errors or corruption” can also be performed in the human mind and/or with pen and paper using observation, evaluation, judgment and/or opinion. For example, nothing in the claims precludes a person from visually reviewing entered data, detecting errors or corruptions in the data, and manually correcting the errors or corruptions. While the claims recite features such as, electronic data, an electronic form, automatically, a user interface including interactive areas, etc., these features do not render the claims non-abstract and are considered additional elements analyzed in the subsequent prongs of the 101 analysis.
As such, it is respectfully submitted that the pending claims are directed to an abstract idea. Furthermore, the claims neither recite any additional elements that integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea itself, as detailed in previous office actions and incorporated herein, and thus, the claims are not patent-eligible subject matter under 35 U.S.C. § 101.
Conclusion
5. 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 Michael Tomaszewski whose telephone number is (313)446-4863. The examiner can normally be reached M-F 5:30 am - 2:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL TOMASZEWSKI/Primary Examiner, Art Unit 3681
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