DETAILED ACTION
This communication is in response to the Applicant Arguments/Remarks filed 11/28/2025. Claims 1-3, 5-20 are pending in the application.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/6/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 11/28/2025 have been fully considered but they are not persuasive. In response to the argument A “Applicant asserts that the claims are not directed to a mental process, as they are instead directed to improvements in computer technology, specifically to database technology by improving the querying and interfacing of electronic data repositories”, and the generating step that includes the amended limitations: generating a numerical identifier score measuring a degree of matching between the first value of the numerical identifier and the second value of the numerical identifier, the numerical identifier score generated based at least in part upon 1) a keyboard distance between mismatching digits of the first value and the second value of the numerical identifier and 2) a digit-positional probability distribution of errors over digits of the numerical identifier.
The generating step above recites mental processes as an evaluation or judgement and mathematical calculations. Regarding the limitations “the numerical identifier score generated based at least in part upon (1) a keyboard distance between mismatching digits of the first value and the second value of the numerical identifier and (2) a digit-positional probability distribution of errors over digits of the numerical identifier”, calculating the keyboard distance can be done in the human mind based on visualization and memory. Specification, para. 76 discloses distance algorithms/mathematical calculations. And the “digit-positional probability distribution of errors over digits of the numerical identifier” is a mathematical modeling of errors. Each claimed step can be performed in the human mind, with the use of a physical aid such as pen and paper, and thus the steps fall within the mental processes grouping, and claim 1 recites an abstract idea. See MPEP § 2106.04(a)(2)(III). Even if performing this mentally/manually is time consuming, "relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible". (Citing Alice, 573 U.S. at 224 ("use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept)). Thus, the claim is nearly entirely directed to an abstract idea as a mental process and math.
Applicant's improvement argument is not an improvement to computer related technology because any improvement is purely in the abstract idea. The claims are considered to recite entirely mental processes. The additional elements are generic computing components. As noted in MPEP 2106.05(a), "It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements." Here, the only improvement applicant argues for is found fully in the abstract idea (judicial exception) alone. That is not an improvement to the functioning of the computer or computer technology.
Regarding the argument B in relating to the newly amended limitations, please see the newly cited columns and lines.
Schumacher et al. teaches at para. 7: each field may be compared by an exact match (e.g., identical last names) and/or by close matches. As an example, close matches can be characterized by nicknames, typos (edit distance), abbreviations, previous names, etc.; para. 93: a comparison can be done by measuring a distance between two numbers for certain attributes such as phone numbers. In this example, the term "distance" refers to a degree of difference in digits (e.g., the difference between a first telephone number 5125551235 and a second telephone number 5125551238 may be a distance of one as the phone numbers are off by one digit). (On the computer keyboard, number 5 is next to number 8);
para. 21: an automatic weight generation process according to the invention may further comprise generating candidate anonymous data useful for locating and removing erroneous data; para. 82-84: the MEI accepts a query in the form of a list of entity attributes and associated values. Examples of entity attributes in a health care example could be patient number, first name, last name, or phone number, etc. The data records in the MEI database are exactly matched against each combination of attributes to generate a plurality of candidate data records. Determining candidates from several combinations of attributes permits more fault tolerance because a data record may have a misspelled last name, but will still be a candidate because the combination of the first name and the phone number will locate the data record. Thus, a misspelling of one attribute will not prevent the data record from being a candidate. Once the group of candidates has been determined, the confidence level for each candidate data record may be calculated; para. 105, 148: (e.g., if it is desired to have 95% of the duplicates to score above the clerical-review threshold, set the default to be 0.05). This value depends upon the weights calculated for matching, the fraction of the time each
attribute has a valid value, and the distribution of those values);
Kvernvik et al. teaches at para. 94: the Keyboard Distance is the relative distance between two digits on the keyboard (digits’ locations/positions). The value is normalized between 0 and 1. Two digits that are next to each other get the weight 0.9. If the distance between the digits is more than one the weight is 0.5. As explained above, it is less likely that someone by mistake dials a 1 instead of a 9 as these digits are so far away from each other on the dial pad, which can be reflected by the Keyboard Distance.
Feinstein further teaches said limitation at col.11:45-56: based on the range and distribution, a probability distribution function is selected. The probability distribution function that is selected has smallest error between the probability distribution
function and the values.
Thus, the usage of Feinstein’s “probability distribution function that is selected has smallest error between the probability distribution function and the values” in combination of Kvernvik’s teaching at para. 94 “the Keyboard Distance is the relative distance between two digits on the keyboard” is applied to the positions/locations of the digits on the keyboard. The combination of references does teach the argument B.
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 1-3, 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3, 5-9 fall within the statutory category of an apparatus or system. Claims 10-14 fall within the statutory category of a process. Claims 15-20 fall within the statutory category of an article of manufacture. Please see below.
Step 2A, Prong One: the claims recite a Judicial Exception.
Claim 1 recites “A record matching”. This is an intended use but also stating the high-level abstract idea, that is a mental evaluation or judgement of matching records.
The step of “receiving a query record comprising a first value of the numerical identifier and a first date for the date identifier” is mental process because in the BRI, records storing numerical identifier value/social security number and a date identifier/birthdate. Thus, a request/search for a record of an entity is mentally performable with pen and paper.
The step of “searching the data records for a record matching the query record, the searching comprising: retrieving a reference record from the data records, the reference record comprising a second value of the numerical identifier and a second date for the date identifier” is a mental evaluation or judgement for retrieving matched records using an entity’s social security number and a relating birthdate.
The three steps of generating a numerical identifier/SSN degree matching score, a date identifier score/birthdate degree matching score, and an overall/combined matching score and “returning the reference record as a matched record to the query record based on the overall matching score exceeding a threshold value” are mental processes as an evaluation or judgement and mathematical calculations. Regarding the limitations “the numerical identifier score generated based at least in part upon (1) a keyboard distance between mismatching digits of the first value and the second value of the numerical identifier and (2) a digit-positional probability distribution of errors over digits of the numerical identifier”, calculating the keyboard distance can be done in the human mind based on visualization and memory. Specification, para. 76 discloses distance algorithms/mathematical calculations. And the “digit-positional probability distribution of errors over digits of the numerical identifier” is a mathematical modeling of errors. Each claimed step can be performed in the human mind, with the use of a physical aid such as pen and paper, and thus the steps fall within the mental processes grouping, and claim 1 recites an abstract idea. See MPEP § 2106.04(a)(2)(1II). Thus, the claim is nearly entirely directed to an abstract idea as a mental process and math.
The limitations “computing system, comprising: a processing device”, “the processing device”, “a non-transitory computer-readable storage medium having program code executable by the processing device to perform operations comprising:” and “keyboard” are ‘apply it’ on a computer as per MPEP 2106.05(f), and does not provide integration into a practical application or significantly more.
The independent claims 10 and 15 recite limitations of commensurate scope. For the reasons stated above for claim 1, claims 10 and 15 also recite mental processes and mathematical calculations which are abstract ideas.
Step 2A, Prong Two: exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the additional elements are “computing system, comprising: a processing device”, “a non-transitory computer-readable storage medium having program code executable by the processing device to perform operations comprising:” and “keyboard”, which are merely applying the abstract idea on a computer as per MPEP 2106.05(f), and does not provide integration into a practical application. Thus, claims 1, 10 and 15 are directed to abstract ideas.
Step 2B: “Inventive Concept” or “Significantly More”
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Here, said claims do not recite specific limitations (alone or when considered as an ordered combination) that were not well understood, routine, and conventional. More particularly, the claims recite generic computer components (e.g., “computing system, comprising: a processing device”, “a non-transitory computer-readable storage medium having program code executable by the processing device to perform operations comprising:” and “keyboard”, in claims 1, 10 and 15) performing generic computing functions that are well understood, routine, and conventional (e.g., plotting data, reorganizing data, forecasting data). See Alice, 573 U.S. at 226 (“Nearly every computer will include a “communications controller’ and [a] ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 614 (Fed. Cir. 2016) (holding generic computer components insufficient to add an inventive concept to an otherwise abstract idea); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network--with no further specification--is not even arguably inventive.”).
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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 the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Claims 2-3, 5 recite "wherein each of the query record and the reference record further comprises an address identifier, the query record comprising a first address for the address identifier and the reference record comprising one or more reference addresses for the address identifier chronologically ordered, and wherein the searching further comprises: generating an address identifier score measuring a degree of matching between the first address and the one or more reference addresses, the address identifier score generated based on matching scores for individual address components of the first address and the one or more reference addresses and respective positions of the one or more reference addresses in the reference record"; “wherein the date identifier score is generated by: calculating similarities between the first date and the second date based on years, months, and days in the first date and the second date; and generating the date identifier score based on a weighted combination of the similarities”; “wherein the numerical identifier score is generated further based on a probability distribution of errors over digits of the numerical identifier”.
In the broadest reasonable interpretation, said limitations recite mental processes and mathematical calculations which are an abstract idea because matching a search record including address, date/birthdate with the stored/reference record including calculating scores of similarities and/or a probability distribution of errors can be set in the mind of the users and mathematical calculations with using pen and paper. Thus, the claim limitations, under its broadest reasonable interpretation, cover performance of the limitations in the mind and fall within the "Mental Processes" and “Mathematical concepts” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Claims 6-9 recite "wherein the similarity score for each combination of the variants of the query name and the reference name is adjusted based on a frequency of a last name in the query name and the reference name”; “wherein generating the overall matching score by combining at least the numerical identifier score and the date identifier score comprising: calculating a surface area score by multiplying the numerical identifier score and the date identifier score”; “wherein each of the query record and the reference record further comprises a name identifier and an address identifier, wherein the searching further comprises calculating a name identifier score and an address identifier score, and wherein the overall matching score is further generated by combining two or more of the numerical identifier score, the date identifier score, the name identifier score and the address identifier score, the combining comprising: calculating a surface area score by multiplying two of the numerical identifier score, the date identifier score, the name identifier score, and the address identifier score; or calculating a volume score by multiplying three of the numerical identifier score, the date identifier score, the name identifier score, and the address identifier score”; “wherein the numerical identifier represents a social security number of an individual, the name identifier represents a name of the individual, the address identifier represents a physical address associated with the individual, and the date identifier represents a date of birth of the individual”.
In a BRI, said limitations recite mental processes and mathematical calculations which are an abstract idea because matching a search record including address, name, date/birthdate with the stored/reference record including calculating scores of similarity and matching score and/or combination of scores, adjusting the scores based on the frequency of a certain attribute, e.g., name can be set in the mind of the users and mathematical calculations with using pen and paper. The dependent claims 11-14 and 16-20 recite limitations of commensurate scope with claims 2-9. For the reasons stated above dependent claims 11-14 and 16-20 also recite mental processes and mathematical calculations which are abstract ideas. Thus, the claims 1-3, 5-20, under its broadest reasonable interpretation, cover performance of the limitations in the mind with usage of pen and paper and fall within the "Mental Processes" and “Mathematical concepts” groupings of abstract ideas. Accordingly, the claims recite abstract ideas.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-6, 10-13, 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schumacher et al. (US 20080005106) in view of Kvernvik et al. (US 20130262452) and further in view of Feinstein (US 10715570).
Specification, para. 70 discloses “numerical identifier” such as a social security number, name, an address identifier, date of birth identifier etc.
As per claims 1, 10, 15, Schumacher et al. teaches
a record-matching computing system, comprising: a processing device (para. 7: a computer information processing system may implement computer program algorithms for matching data records); a data repository for storing data records regarding entities (fig. 2, item 56: entity database; para. 45: the information about entities within the data records may be information about patients within a plurality of hospitals which are owned by a health care organization),
wherein each data record comprises a numerical identifier and a date identifier; and a non-transitory computer-readable storage medium having program code executable by the processing device to perform operations comprising: receiving, at the processing device, a query record comprising a first value of the numerical identifier and a first date for the date identifier (para. 7: a computer information processing system may implement computer program algorithms for matching data
records. Some algorithms may match search criteria with data source systems by comparing individual fields of a record; para. 44: one of the users transmits a query to the MEI 32 and receive a response to the query back from the MEI; para. 88: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN)/numerical identifier, date of birth/date identifier, etc. for identifying a person; para. 63: the querying operations permit the user to retrieve information from the MEI about a particular entity or data from one of the control databases. After a user selects the query operation in step 122, the user may select from a particular query operation that may include an entity retrieval operation 124, or a database query operation 128);
accessing, via the processing device, the data repository; searching, via the processing device, the data records for a record matching the query record, the searching comprising: retrieving, via the processing device, a reference record from the data records, the reference record comprising a second value of the numerical identifier and a second date for the date identifier (para. 7, 63: for the entity retrieval operation, the MEI may execute the match operation 300 described below. During the match operation, an input query may be matched against data records within the various information sources, as described in more detail below with reference to FIG. 15. For the database retrieval operation, the operator specifies a database and a set of attribute values that indicates the records of interest. The MEI locates those records in the specified database that has corresponding values for the specified attributes; para. 112-113: the data processing system allows a user to select/access anonymous values from a list generated by profiling the data. For example, for attributes like SSN, Phone Number, Zip code, and alternate ID, the anonymous values are determined by frequency; fig. 5);
generating, via the processing device, a numerical identifier score measuring a degree of matching between the first value of the numerical identifier and the second value of the numerical identifier, the numerical identifier score generated based at least in part upon (1) a keyboard distance between mismatching digits of the first value and the second value of the numerical identifier (para. 7: a computer information processing system may implement computer program algorithms for matching data records. Some algorithms may match search criteria with data source systems by comparing individual fields of a record; para. 93: attributes can be in different types. To accommodate different types of attributes, various comparison techniques can be utilized. A comparison can be done by measuring a distance between two numbers for certain attributes such as phone numbers. In this example, the term "distance" refers to a degree of difference in digits (e.g., the difference between a first telephone number 5125551235 and a second telephone number 5125551238 may be a distance of one as the phone numbers are off by one digit). In this way, the closeness of two data records can be associatively reflected in terms of the closeness of their telephone numbers. (On the computer keyboard, number 5 is next to number 8); para. 253);
and (2) a digit-positional probability distribution of errors over digits of the numerical identifier (para. 17: calculate new weights based upon the unmatched probability tables and the new discrepancy tables. Information from the unmatched set probabilities and the matched set probabilities are combined to form the actual weights; para. 21: an automatic weight generation process according to the invention may further comprise generating candidate anonymous data useful for locating and removing erroneous data; para. 82-84: the MEI accepts a query in the form of a list of entity attributes and associated values. Examples of entity attributes in a health care example could be patient number, first name, last name, or phone number, etc. The data records in the MEI database are exactly matched against each combination of attributes to generate a plurality of candidate data records. Determining candidates from several combinations of attributes permits more fault tolerance because a data record may have a misspelled last name, but will still be a candidate because the combination of the first name and the phone number will locate the data record. Thus, a misspelling of one attribute will not prevent the data record from being a candidate. Once the group of candidates has been determined, the confidence level for each candidate data record may be calculated; para. 105, 148: (e.g., if it is desired to have 95% of the duplicates to score above the clerical-review threshold, set the default to be 0.05). This value depends upon the weights calculated for matching, the fraction of the time each
attribute has a valid value, and the distribution of those values);
generating, via the processing device, a date identifier score measuring a degree of matching between the first date and the second date (para. 82-84: the MEI accepts a query in the form of a list of entity attributes and associated values. Examples of entity attributes in a health care example could be patient number, first name, last name, or phone number, etc. The data records in the MEI database are exactly matched against each combination of attributes to generate a plurality of candidate data records. Determining candidates from several combinations of attributes permits more fault tolerance because a data record may have a misspelled last name, but will still be a candidate because the combination of the first name and the phone number will locate the data record. Thus, a misspelling of one attribute will not prevent the data record from being a candidate. Once the group of candidates has been determined, the confidence level for each candidate data record may be calculated; para. 253-256: the birth date comparison uses edit distance, and the matched probability tables are generated from calculating this edit distance on the birth date subset of the matched set. This is the same process used for SSN);
generating, via the processing device, an overall matching score by combining at least the numerical identifier score and the date identifier score; and returning, via the processing device, the reference record as a matched record to the query record based on the overall matching score exceeding a threshold value (para. 50, 102-103: for records to be in a matched set, they have to meet at least two thresholds. The first one pertains to the overall match score and the second one pertains to a percentage of a possible match. For example, suppose scoring record one ("R1") against itself results a 10. Thus, the most any record can score against R1 is 10. In order to be in the matched set, records would have to score above a certain threshold value (e.g., 6) and has to be a certain percentage (e.g., 95%) of a possible score (e.g., 10). Setting the latter one high (e.g., 95% or more) can result almost identical pairs per attribute type).
Schumacher et al. does not explicitly teach keyboard distance.
Kvernvik et al. teaches keyboard distance of digits values at para. 94: the Keyboard Distance is the relative distance between two digits on the keyboard. The value is normalized between 0 and 1. Two digits that are next to each other get the weight 0.9. If the distance between the digits is more than one the weight is 0.5. As explained above, it is less likely that someone by mistake dials a 1 instead of a 9 as these digits are so far away from each other on the dial pad, which can be reflected by the Keyboard Distance; para. 106).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Schumacher to include the keyboard distance teaching of Kvernvik in order to effectively generate a matching score needed for measuring the matching degrees between the search field and the existed attribute for the data processing system.
Even if Schumacher, Kvernvik et al. do not teach (2) a digit-positional probability distribution of errors over digits of the numerical identifier,
Feinstein teaches said limitation at col.11:45-56: based on the range and distribution, a probability distribution function is selected. The probability distribution function that is selected has smallest error between the probability distribution
function and the values.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Schumacher, Kvernvik to include the probability distribution function that is selected has smallest error between the probability distribution function and the values teaching of Feinstein in order to lead to a more accurate and reliable of the underlying data generating process and/or accurate query results.
As per claims 2, 12, 17, Schumacher et al. teaches
wherein each of the query record and the reference record further comprises an address identifier, the query record comprising a first address for the address identifier and the reference record comprising one or more reference addresses for the address identifier chronologically ordered (para. 44: one of the users transmits a query to the MEI 32 and receive a response to the query back from the MEI; para. 88: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN)/numerical identifier, date of birth/date identifier, etc. for identifying a person; para. 63-64: the querying operations permit the user to retrieve information from the MEI about a particular entity or data from one of the control databases. After a user selects the query operation in step 122, the user may select from a particular query operation that may include an entity retrieval operation 124, or a database query operation 128. The volatility of the data may indicate fraud if the data about a particular entity is changing frequently. The MEI may also be queried about the past history of changes of the data in the data records so that, for example, the past addresses for a particular entity may be displayed. Once the queries or matches have been completed, the data is returned to the user. Thus, past addresses and/or new addresses are stored in chronological order),
wherein the searching further comprises: generating an address identifier score measuring a degree of matching between the first address and the one or more reference addresses, the address identifier score generated based on matching scores for individual address components of the first address and the one or more reference addresses and respective positions of the one or more reference addresses in the reference record (para. 84: the confidence level may be calculated based on a scoring routine, which may use historical data about a particular attribute, such as a last address. Thus, if the current address and past addresses match a query, the confidence level is higher than that for a data record with the same current address but a different old address; para. 88: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN)/numerical identifier, date of birth/date identifier, etc. for identifying a person; the abstract: provide a system and method of automatically generating weights for matching data records. Each field of a record may be compared by an exact match and/or close matches and each comparison can result in a mathematical score which is the sum of the field comparisons. To sum up the field scores accurately, the automatic weight generation process comprises an iterative process; para. 2: automatic weight generation for probabilistic matching of data records across databases where a match score indicates the likelihood of records belonging to the same entity).
As per claims 3, 13, 18, Schumacher et al. teaches
wherein the date identifier score is generated by: calculating similarities between the first date and the second date based on years, months, and days in the first date and the second date; and generating the date identifier score based on a weighted combination of the similarities (para. 376-377: Computing the Similarity Weights: the similarity weights reside in a 1-dimentional weight table indexed from 0, to 16. 0 is the weight for missing data and this weight is always 0. A value of 1 is interpreted as exact (or near exact match); para. 90: a default "Date of Birth" may have a value of 99/99/9999; para. 95: a weight of 4 is given because the birthday matches).
As per claims 5, 11, 16, Schumacher et al. teaches
wherein each of the query record and the reference record further comprises a name identifier, the query record comprising a query name for the name identifier and the reference record comprising a reference name for the name identifier,
wherein the searching further comprises generating a name identifier score measuring a degree of matching between the query name and the reference name by: processing the query name and the reference name to create multiple variants for each of the query name and the reference name (para. 7: match search criteria with data source systems by comparing individual fields of a record. In some embodiments, each field may be compared by an exact match (e.g., identical last names) and/or by close matches. As an example, close matches can be characterized by nicknames, typos (edit distance), abbreviations, previous names, etc. Matching according to one embodiment of the invention accounts for anonymous values (e.g., an anonymous name "John Doe"; para. 47: for a health care system containing information about a patient, the control databases may contain a rule that the nickname "Bill" is the same as the full name "William." Therefore, the MEI will determine that data records otherwise identical except for the first name of "Bill" and "William" contain information about the same entity and should be linked together/thus, create multiple variants; para. 54: the information sources may have four patients, with data records S, T, U, and V respectively, who are all named George Smith and the operator may enter the following nonidentity rules (i.e., S.notequal.T, T.notequal.U, U.notequal.V, V.notequal.S) to keep the data records of the four different entities separate and unlinked by the MEI);
calculating a similarity score for each combination of the variants of the query name and the reference name; and calculating the name identifier score based on the similarity scores for the combinations of the variants of the query name and the reference name (para. 95: suppose in comparing two records a weight of 3 is given because the name matches and a weight of 4 is given because the birthday matches, so the final result of this combined (i.e., name-birthday) comparison has a weight of 7. However, these two attributes are conditionally uncorrelated, which means that knowing one does not necessarily give weight to the other (i.e., the scores add to give the final score). As a more specific example, knowing that two different people are named "John Smith" does not indicate whether the birthdays should or should not match. On the other hand, suppose two different people have addresses that match. The chance or likelihood that they would also have phone numbers that match would be higher. Thus, the result from this second (i.e., address-phone) comparison is not additive (i.e. if the address alone weight is 4 and the phone alone weight is 3, then the address-plus-phone weight may be 5, which is less than the sum; para. 378-379: computing the matched similarity data. For each matched pair, compare all business name pairs keeping the following Counts…; para. 386: computing similarity scores for attributes).
As per claim 6, Schumacher et al. teaches
wherein the similarity score for each combination of the variants of the query name and the reference name is adjusted based on a frequency of a last name in the query name and the reference name (para. 7-8: each comparison can result in a mathematical score which is the sum of the field comparisons. To sum up the field scores accurately, each type of comparison or comparison function can have a weighting factor associated thereto. For example, an exact match may be weighted much higher than an abbreviation match, and social security number match may be weighted much higher than a date-of-birth match. According to one embodiment of the invention, weighting can account for other factors such as the frequency of the component in the data source records. For example, a match on "John Smith" may be weighted much less than matches on a name like "Moonbeam Horsefeather"; para. 88-91: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN)/numerical identifier, date of birth/date identifier, etc. for identifying a person, identify the occurrence and frequency of certain types of attributes (e.g., more than one people could have the same last name and/or the same birthday); provide the user with the ability to add, modify, or otherwise edit the anonymous name database).
Claim(s) 7-9, 14, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schumacher et al. (US 20080005106) in view of Kvernvik et al. (US 20130262452) and further in view of Feinstein (US 10715570) and Mineno (US 20120197889).
As per claims 7, 19, Schumacher et al. teaches
wherein generating the overall matching score by combining at least the numerical identifier score and the date identifier score comprising: calculating a surface area score by multiplying the numerical identifier score and the date identifier score (para. 112-113: for attributes like SSN, Phone Number, Zip code, and alternate ID, the anonymous values are determined by frequency. Values are marked as anonymous if their frequency is greater than a configurable multiplier of the average frequency. As a more specific example, if the average phone number occurs 1.2 times, then a value is flagged as anonymous if it has occurred more than f_phone*1.2 times).
Even if Schumacher, Kvernvik, Feinstein et al. do not explicitly teach the limitation calculating a surface area score by multiplying the numerical identifier score and the date identifier score,
Mineno teaches
calculating a surface area score by multiplying the numerical identifier score and the date identifier score (fig. 8: matching search definition with source record; para. 143-144: the searching unit 122 multiplies scores of the application results of the conditions when using the AND condition, name score, address score, date of birth score).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Schumacher, Kvernvik, Feinstein to include at least the multiplying two of the numerical identifier score teaching of Mineno in order to effectively generate a matching score needed for measuring the matching degrees between the search field and the existed attribute in order to exclude unrelated records from the search results.
As per claims 8, 14, 20, Schumacher et al. teaches
wherein each of the query record and the reference record further comprises a name identifier and an address identifier (para. 88: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN)/numerical identifier, date of birth/date identifier, etc. for identifying a person),
wherein the searching further comprises calculating a name identifier score and an address identifier score (para. 84: The confidence level may be calculated based on a scoring routine, which may use historical data about a particular attribute, such as a last address. Thus, if the current address and past addresses match a query, the confidence level is higher than that for a data record with the same current address but a different old address; para. 96: provide multi-dimension scores in the form of a multi-dimensional array of conditions, each has a weight to a condition associated with the attributes (e.g., "address exact, phone missing," "address off a little bit, phone missing," "address exact, phone exact," etc.),
wherein the overall matching score is further generated by combining two or more of the numerical identifier score, the date identifier score, the name identifier score and the address identifier score (para. 50, 95: the scores add to give the final score). As a more specific example, knowing that two different people are named "John Smith" does not indicate whether the birthdays should or should not match. On the other hand, suppose two different people have addresses that match. The chance or likelihood that they would also have phone numbers that match would be higher; para. 102-103: for records to be in a matched set, they have to meet at least two thresholds. The first one pertains to the overall match score and the second one pertains to a percentage of a possible match. For example, suppose scoring record one ("R1") against itself results a 10. Thus, the most any record can score against R1 is 10. In order to be in the matched set, records would have to score above a certain threshold value (e.g., 6) and has to be a certain percentage (e.g., 95%) of a possible score (e.g., 10). Setting the latter one high (e.g., 95% or more) can result almost identical pairs per attribute type; para. 112-113).
Schumacher, Kvernvik, Feinstein et al. do not explicitly teach the limitation below.
Mineno teaches
the combining comprising: calculating a surface area score by multiplying two of the numerical identifier score, the date identifier score, the name identifier score, and the address identifier score; or calculating a volume score by multiplying three of the numerical identifier score, the date identifier score, the name identifier score, and the address identifier score (fig. 8: matching search definition with source record; para. 143-144: the searching unit 122 multiplies scores of the application results of the conditions when using the AND condition, name score, address score, date of birth score).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Schumacher, Kvernvik, Feinstein to include at least the multiplying two of the numerical identifier score teaching of Mineno in order to effectively generate a matching score needed for measuring the matching degrees between the search field and the existed attribute in order to exclude unrelated records from the search results.
As per claim 9, Schumacher et al. teaches
wherein the numerical identifier represents a social security number of an individual, the name identifier represents a name of the individual, the address identifier represents a physical address associated with the individual, and the date identifier represents a date of birth of the individual (para. 86-88: exemplary identifying information for an entity might include fields or attributes like first name, last name, address, phone number, social security number (SSN), date of birth, etc. for identifying a person).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Trepetin (US 20080109459 A1) teaches at para. 46: a uniform distribution of all values in the namespaces. Anend et al. (US 11163955) teaches keyboard distance algorithm at col. 1:63-65.
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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/LINH BLACK/Examiner, Art Unit 2163 12/13/2025
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163