DETAILED ACTION
Case Status
This office action is in response to remarks and amendments of 16 January 2026. Claims 1-16 and 21-24 have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 and 21-24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Amendments to independent claims 1 and 9 include “the response data object comprising … a result type of the data classification result”. The remarks identify pars. 25-33, 40 of the specification as providing support for the amended subject matter. However, no part of the specification discloses “the response data object comprising … a result type of the data classification result”; this is because the data classification result is the result type. Par. 25 discloses that a type result (entity type) is of the payload (entity) – the type result is not of the entity type itself. Pars. 30, 39, 40 make it clear that the type is of the data classification request and not of the data classification result. All respective dependent claims are likewise rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 22 and 24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 22 and 24 recite the limitation "the a batch processing request". There is insufficient antecedent basis for this limitation in the claim.
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 1-16 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-16 and 21-24 are directed to one of the eligible categories of subject matter.
With respect to independent claims 1 and 9, the generating, applying, comparing, formatted cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The receiving, outputting, to be consumed by application/service are recited at a high level of generality and does not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components (e.g. processing device, memory). Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claim 22, 24 the determining, detecting cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The storing, displaying are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 2, 3, 4, 5, 7, 10, 11, 12, 13, 15 the applied, adjustable, determined, adding, applying, configured, performing prediction cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
With respect to dependent claims 6, 8, 14, 16, 21, 23 accesses a database, outputs, outputting, transmitting, displaying are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-16 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over non-patent literature IBM InfoSphere Global Name Management, hereinafter IBM, in view of S. Iwase, JP 2006004070 A, hereinafter Iwase.
As per claim 1, IBM discloses A computer-implemented method for data classification, the method comprising:
receiving, by a processing device, a data classification request at a data classifier (page 40, “use the classify() method … and pass it the full name as a string value”);
generating, by the processing device, one or more variations of the data classification request as one or more variants comprising one or more tokens with variations to one or more of character spacing and punctuation (page 44, section titled “Generating name variants using NameWorks”; see also, page 18, first par.; “Name Phrases” and “Name Tokens” section starting on page 21; page 35 section titled “NameParser functions for parsing names”; page 46, getVariants() also is passed the full name as string input; page 47 section titled “Before you begin” – all of these sections disclose generation of variations of an input name that comprise one or more tokens with spacing and/or punctuation variations);
IBM does not expressly disclose, however Iwase in the related field of endeavor of automated name-type classification in customer or entity databases discloses applying, by the processing device, a first set of rules by the data classifier to the one or more tokens to determine a first type prediction for the one or more variants (Iwase, par. 22 discloses “The personal name determination condition storage unit 19 stores a condition for determining a personal name to be referred to by the personal name analysis unit 13.” Par. 30 includes “the personal name analysis unit 13 refers to the personal name determination conditions of the full name dictionary storage unit 15 and the personal name determination condition storage unit 19 based on all the words of the input character string subjected to the morphological analysis, and performs personal name analysis.” At least pars. 17, 20, 34 disclose a scores/possibility based prediction of the type of the name being of person type or corporate type, and determining the one with the higher possibility. Note that IBM also discloses rules on page 177: “You can set individual parameters for Personal names in the GNParms and SNParms sections and set parameters for Organization names in the ONParms section.”);
applying, by the processing device, a second set of rules by the data classifier to the one or more tokens to determine a second type prediction for the one or more variants (Iwase, par. 22 discloses “The corporate name determination condition storage unit 20 stores a condition for determining a corporate name to be referred to by the corporate name analysis unit 14.” Par. 32 includes “The corporate analysis unit 14 refers to the conditions of the corporate name analysis condition storage unit 20 shown in FIG. 6, and determines whether the name is a corporate name.” At least pars. 17, 20, 34 disclose a scores/possibility based prediction of the type of the name being of person type or corporate type, and determining the one with the higher possibility. Note that IBM also discloses rules on page 177: “You can set individual parameters for Personal names in the GNParms and SNParms sections and set parameters for Organization names in the ONParms section.”);
comparing, by the processing device, the first type prediction with the second type prediction to determine a final type prediction as a data classification result (see Iwase as cited above including at least pars. 17, 20, 34).
generating, by the processing device, a response object formatted to be consumed by an application or service (claim interpretation note: “formatted to be consumed by an application or service” is claimed as an intended use limitation; IBM, page 10 says “IBM NameWorks combines the individual IBM InfoSphere Global Name Management components into a single, unified, easy-to-use application programming interface (API), and also extends this functionality to Java applications and as a web service” and “the analyze() method performs all linguistic operations and produces a single, combined result that contains all analysis information for a name”), the response object comprising an entity with at least a portion of the data classification request (IBM, as discussed above, the getVariants(), analyze() and classify() methods receive the input name string as the data classification request and produce a result object associated with it, also, Iwase, at least par. 35 discloses an output that comprises at least a portion of the inputted name (i.e. data classification request)), a result type of the data classification result (see IBM as cited above including page 66 which says “You can use IBM NameWorks to categorize names as personal or organization … During name processing, names are associated with a name category, either personal or organization.” and Iwase, pars. 17, 20, 34 disclose a scores/possibility based prediction of the type of the name being of person type or corporate type, and determining the one with the higher possibility.), and a result reason based on the first type prediction and the second type prediction (IBM, page 18 includes “codes (called name category reason codes) that identify the reason that a name was classified as an organization name … they indicate patterns that would not be expected in a personal name… if the name matches one or more name category reason codes, it is assumed to be an organization name. Otherwise, it is a candidate to be a personal name” and page 70 says “Name categorization reasons identify which type of non-personal indicator or pattern was found. They provide an explanation of why the category was chosen. You can use reason codes for more detailed analysis, or to make your own name categorizations, based on these reason codes.” This means that reason codes reflect the combined outcome of applying personal name rule analysis and organization name rule analysis and Iwase, as cited above, that the output of the classification system includes the scores from two prediction units (one for personal names and one for corporate names)); and outputting, by the processing device, the response object (IBM, page 10 says “IBM NameWorks combines the individual IBM InfoSphere Global Name Management components into a single, unified, easy-to-use application programming interface (API), and also extends this functionality to Java applications and as a web service” and “the analyze() method performs all linguistic operations and produces a single, combined result that contains all analysis information for a name; also, Iwase, at least par. 35 discloses an output).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because both Iwase and IBM address the same problem domain of automated name-type classification in customer or entity databases. Iwase, as cited in the rejection, provides an explicit scoring based dual analysis framework for distinguishing personal and corporate name. IBM discloses industrial scale implementations of variant generation, transliteration and parameterized rule sets. The combination of the references would provide a more accurate name-classification system because Iwase’s dual analysis scoring framework would improve classification reliability across IBM’s global datasets. (Note that IBM also discloses determining a category of names using NameSifter on page 129, as well as category specific rules on page 177: “You can set individual parameters for Personal names in the GNParms and SNParms sections and set parameters for Organization names in the ONParms section.” IBM does not explicitly disclose doing the categorizing as two separate steps as claimed.)
As per claim 2, IBM in view of Iwase discloses The computer-implemented method of claim 1, wherein a first set of weights is applied to at least one result of the first set of rules, and a second set of weights is applied to at least one result of the second set of rules (Iwase par. 34 discloses that the “the individual / corporation determination rule is scored based on the number/presence of …” wherein the integer values in each of the entries of the second column of fig. 7 are rule specific points (i.e. weights). This means that different weights are applied to results of different rule sets, with a separate evaluation criterion for personal name rules versus corporate name rules).
As per claim 3, IBM in view of Iwase discloses The computer-implemented method of claim 2, wherein at least one weight of the first set of weights and at least one weight of the second set of weights are adjustable (see rejection of claim 2 and note that the integer values in each of the entries of the second column of Iwase, fig. 7 are not hardcoded; they are inputted, user-defined textual (i.e. “adjustable”) values entered in a database or file and are used in connection with the disclosed “a certain threshold value” of par. 34, which is similarly user-defined (i.e. “adjustable”). The language “a score is associated” in par. 20 also indicates a user-defined textual (i.e. “adjustable”) values).
As per claim 4, IBM in view of Iwase discloses The computer-implemented method of claim 2, wherein the first type prediction is determined based on adding a first result subset of applying the first set of weights to the at least one result of the first set of rules, and the second type prediction is determined based on adding a second result subset of applying the second set of weights to the at least one result of the second set of rules (see rejection of claim 3 as well as Iwase, pars. 6-8, 34 which describe a process where each rule contributes a numeric score based on a condition being met and the numbers are accumulated (par. 34: “sum of the values”) to arrive at a final determination. This summation/accumulation corresponds to the claimed adding limitations for both types of predictions (personal and corporate)).
As per claim 5, IBM in view of Iwase discloses The computer-implemented method of claim 1, wherein the first set of rules is configured to determine a likelihood that the data classification request comprises a name of a person, and the second set of rules is configured to determine a likelihood that the data classification request comprises a name of a company (both IBM and Iwase disclose this limitation as cited in the rejection of claim 1).
As per claim 6, IBM in view of Iwase discloses The computer-implemented method of claim 5, wherein at least one rule of the first set of rules accesses a person name frequency database and at least one rule of the second set of rules accesses a company name frequency database (see IBM, pages 14, 26, 67, 69, for disclosure of name frequency databases/lists including person and organization/company names and see Iwase pars. 8, 26, 27, 30, 32 disclose dictionary storages and list data of person and corporate/company names. See rationale to combine as provided in the rejection of claim 1).
As per claim 7, IBM in view of Iwase discloses The computer-implemented method of claim 1, wherein the data classifier is configurable between performing a single type prediction and a batch of type predictions (Iwase, at least pars. 6-8, 15 mention input string (i.e. single type prediction) and pars. 1, 3, 15, 32 disclose at least “classifying names in customer data into individual names and corporate names” (i.e. batch of type predictions). IBM also discloses this claim in at least page 9: “All of the component APIs perform an analytical function of a single name”, page 10: “Includes the functions that are necessary for evaluating a single name”, and page 11: “Distributed Search exposes the functionality of the NameHunter API in the form of a single server process that can accommodate complex and performance-intensive search requirements due to the size of data lists to be searched or the number of search transactions that occur at a given time.” )
As per claim 8, IBM in view of Iwase discloses The computer-implemented method of claim 7, wherein a user interface of the data classifier outputs information associated with the first type prediction and the second type prediction with the data classification result (IBM, pages 11, 154, 158 disclose user applications, GUI; see also, Iwase, at least par. 35 discloses the outputting).
As per claim 21, IBM in view of Iwase discloses The computer-implemented method of claim 1, wherein the data classifier comprises an application programming interface and a user interface; wherein based on receiving the data classification request as a system input at the application programming interface of the data classifier, outputting the response object comprises transmitting the response object as a system output to the application or service from which the data classification request was received as the system input through the application programming interface (IBM, pages 10-11 include “IBM NameWorks combines the individual IBM InfoSphere Global Name Management components into a single, unified, easy-to-use application programming interface (API), and also extends this functionality to Java applications and as a web service… the web service interface can be used either locally or remotely in SOA environments. Any programming environment that can utilize Web services can take advantage of the name analysis and comparison tools provided by IBM NameWorks” and par. 154 includes “client integration with third-party software and existing legacy systems. You can use the search capabilities of ENS from your own client program by means of the web service API, or you can use the graphical user interface provided in ENS. Even if you intend to use the web services, the GUI can be helpful in trying out searches and in understanding the capabilities and behavior of ENS, since it uses the web services for its searching”); and
wherein based on receiving the data classification request as a user input at the user interface of the data classifier, outputting the response object comprises displaying the response object as a user output on the user interface from which the data classification request was received as the user input through the user interface (IBM, page 154 discloses “Enterprise Name Search (ENS) provides an infrastructure for distributing high volume, large-scale, enterprise name searches across very large name lists. ENS leverages IBM NameWorks for efficient name search function, name list management, and to configure, manage and monitor the name search process.”; see also, Iwase, at least par. 35 discloses the outputting).
As per claim 22, IBM in view of Iwase discloses The computer-implemented method of claim 21, further comprising: storing the data classification result to a file system based on determining that the a batch processing request is received by the data classifier, and wherein displaying the response object as the user output on the user interface is based on detecting a single prediction request through the user interface (Iwase, at least pars. 6-8, 15 mention input string (i.e. single type prediction) and pars. 1, 3, 15, 32 disclose at least “classifying names in customer data into individual names and corporate names” (i.e. batch of type predictions). IBM also discloses this claim in at least page 9: “All of the component APIs perform an analytical function of a single name”, page 10: “Includes the functions that are necessary for evaluating a single name”, and page 11: “Distributed Search exposes the functionality of the NameHunter API in the form of a single server process that can accommodate complex and performance-intensive search requirements due to the size of data lists to be searched or the number of search transactions that occur at a given time.” )
As per claims 9-16, 23-24, they are analogous and therefore likewise rejected.
Response to Arguments
Applicant's arguments filed 16 January 2026 have been fully considered.
Page 8 of the remarks presents the following:
PNG
media_image1.png
248
582
media_image1.png
Greyscale
Examiner respectfully disagrees. “Generating a response object to be formatted to be consumed by an application or service” does not preclude performance as a mental activity because it broadly encompasses organizing and presenting classification information (including entity, result type, result reason) which are the types of determinations that can be performed mentally or with pen and paper. The output being called a “response object” merely recites the intended use or destination of that information. The fact that the output is consumable by an application or service is extra-solution activity that does not integrate the abstract idea into a practical application as the claimed steps are all directed to the abstract classification process itself regardless of what downstream system receives the result. The response object is recited at a high level of generality with no structural definition beyond its contents, which are themselves the outputs of the abstract mental process.
The remarks point to pars. 3, 20, and 50 of the application as filed in an attempt to show that the claims provide technical effects and technology improvements. Firstly, the claims themselves must include the components or steps of the invention that provide the improvement described in the specification. MPEP § 2106.04(d)(1). Secondly, none of pars. 3, 20, and 50 of the application describe components or steps of an invention that provides an improvement; they describe an improvement to the abstract idea itself rather than an improvement to an underlying computer technology or technical field.
Page 9 of the remarks presents the following:
PNG
media_image2.png
175
581
media_image2.png
Greyscale
It should be noted that in the receiving step of claims 1 and 9, the claimed data classifier only functions to receive something (the data classification request); it is not claimed as actually doing anything immediately upon receiving and/or before the generating step, much less outputting anything such as a classification or a class label. The generating step has nothing to do with the output of a classifier – it just creates variations of the received thing. This claimed sequence of receiving and then generating corresponds to Applicant’s argument that IBM discloses “separate methods that are suggested to be performed sequentially.”
Even if the claims are amended to require the generating step to be based on some output of the data classifier of the receiving step, IBM would still teach this in view of page 47, section titled “Before you begin” because the getVariants() method depends on the output of the classify() method which takes in as input a full name as string value as stated on page 40.
The remarks present that the cited art does not teach or suggest the amended subject matter. Examiner respectfully disagrees.
Regarding the limitation “generating, by the processing device, one or more variations of the data classification request as one or more variants comprising one or more tokens with variations to one or more of character spacing and punctuation” See IBM page 44, section titled “Generating name variants using NameWorks”, “Name Phrases” and “Name Tokens” section starting on page 21, page 35 section titled “NameParser functions for parsing names”, page 46, getVariants() also is passed the full name as string input, page 47 section titled “Before you begin” – all of these sections disclose generation of variations of an input name that comprise one or more tokens with spacing and/or punctuation variations. Page 18, first paragraph says “NameParser creates a parse tree from the result of analyzing the structure and distribution patterns of an input name. The parse tree is a hierarchy that groups the elements in a name into structural units, beginning with individual tokens (space or punctuation-delimited strings), which might combine into name phrases, which combine to form a full personal name.”
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20150286629
Pars. 22-26
Machine learning based classification to determine classes of different types of entities such as person and organization, scores, weights
US 20250150423
Par. 39
“identify and classify named entities, such as names of people, places, organizations”
US 20210374347
Pars. 31-32
“probability that the first token is a person … probability that the first token is an organization”
US 20090144609
Par. 180
“entities can be grouped into categories such as places, people, and organizations.”
US 20200081973
Par. 4
determination of the category of the entity including people and organization names
US 20230368557
Pars. 10,-13, 230-232
“disambiguation of entities that … refer to different individuals or organizations” … “In an entity-specific ensemble, multiple models are trained to identify specific types of entities.”
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 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, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Syed Hasan
Primary Examiner
Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154
Prosecution Insights