DETAILED ACTION
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 .
Claims 1-13 of this US application are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/26/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 12-13 are objected to because of the following informalities:
Claims 1 and 12-13 recite “AI” which should be defined or spelled out before using its abbreviation form.
Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 12 and 13:
Step 1:
Claim 1 recites “A work support system”. The claim recites the work support system comprising at least one processor and therefore is a machine.
Claim 12 recites “A method”. The claim recites a series of steps and therefore is a process.
Claim 13 recites “A non-transitory information storage medium having stored thereon a program” and therefore is a manufacture.
Step 2A Prong One:
Claims 1, 12 and 13 recite the limitation “execute/executing” which specifically recite “execute/executing, based on the field information and an AI, generation-related processing relating to generation of storage data to be stored in the database;” This limitation are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting at least one “processor”, a “non-transitory information storage medium” and a generic AI, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “execute/executing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper generating a data record base on field information and the generic AI. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claims 1, 12 and 13 recite the additional elements “acquire/acquiring field information relating to each field of the database;” and “store/storing the storage data in the database.” The limitations amount to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims 1, 12 and 13 recite the limitation “store/storing the storage data in the database.” The limitation amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). As discussed above, the additional elements of using at least one “processor”, a “non-transitory information storage medium” and a generic AI to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim 3 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 4 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 5 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire user attribute information relating to an attribute of the user; and execute the generation-related processing further based on the user attribute information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 6 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire related data stored in a related database relating to the database; and execute the generation-related processing further based on the related data.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 7 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional element “execute the generation-related processing relating to generation of a plurality of pieces of the storage data that are mutually different;” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim also recites the additional element “store the plurality of pieces of the storage data that are mutually different in the database.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The limitation also amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 8 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire, based on input of the user, generation number information relating to the number of pieces of the storage data to be generated; and execute the generation-related processing further based on the generation number information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 9 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional element “execute the generation-related processing relating to the generation of the storage data that is part of a record of the database;” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim also recites the additional element “store, in the database, the record including the storage data as the part.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The limitation also amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 10 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional elements “acquire, based on input of the user, correction content information relating to correction content in the storage data; execute correction-related processing relating to generation of correction portion data, which is post-correction data of a correction portion corresponding to the correction content in the storage data, based on the correction content information and the AI; and correct the storage data by replacing the correction portion in the storage data by the correction portion data.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 11 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire verification content information relating to verification content of the database; and execute the generation-related processing further based on the verification content information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 5-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kanner et al. (US 2020/0143258, hereinafter “Kanner”).
Regarding claim 1, Kanner teaches A work support system, which is configured to support work of a user through use of a database designed with no-code or low-code, the work support system comprising at least one processor configured to ([0036]: A “computing device” is a device in including a microprocessor and memory configured to execute instructions; [0057] and Fig. 1: The embodiment of FIG. 1 is executed by a server system that has a computer-implemented infrastructure for receiving, organizing, categorizing, securing, and sharing any personal information (physical or digital) of users.):
acquire field information relating to each field of the database ([0057] and Fig. 1: In process 12, for each item of information in the received set of items, the server system obtains, as a result of parsing the received set of items, new information including an information type and a set of data fields pertinent to the information type. [0058]: The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.);
execute, based on the field information and an AI, generation-related processing relating to generation of storage data to be stored in the database ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
store the storage data in the database ([0057] and Fig. 1: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
Regarding claim 2, Kanner teaches wherein the at least one processor is configured to: acquire information relating to the database, the information being information other than the field information ([0057] and Fig. 1: In process 12, for each item of information in the received set of items, the server system obtains, as a result of parsing the received set of items, new information including an information type and a set of data fields pertinent to the information type. [0058]: The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.); and
execute the generation-related processing further based on the other information ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information.).
Regarding claim 5, Kanner teaches wherein the at least one processor is configured to: acquire user information relating to an attribute of the user ([0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date.); and
execute the generation-related processing further based on the user attribute information ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system. [0075] and Fig. 7: In process 13, still in this example of the driver's license, the derived information 72 includes a new derived event “Expiration”; a new derived contact “Tom Smith”; a new derived residence and the derived associations between the contact, the residence and the driver's license, as represented also by FIG. 4.).
Regarding claim 6, Kanner teaches wherein the at least one processor is configured to: acquire related data stored in a related database relating to the database ([0058]: FIG. 2 is a block diagram showing the information flow, in accordance with an embodiment of the present invention, of the task 12 of FIG. 1, wherein the information type and set of data fields are obtained by parsing by an artificial intelligence engine, which is executed either on the computing device or the server system. Information type definitions 22 stored in the taxonomy database 21 are used to train an artificial intelligence engine 23. The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.); and
execute the generation-related processing further based on the related data ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.).
Regarding claim 7, Kanner teaches wherein the at least one processor is configured to: execute the generation-related processing relating to generation of a plurality of pieces of the storage data that are mutually different ([0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date.); and
store the plurality of pieces of the storage data that are mutually different in the database ([0057]: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
Regarding claim 8, Kanner teaches wherein the at least one processor is configured to: acquire, based on input of the user, generation number information relating to the number of pieces of the storage data to be generated ([0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date. [0099]: The “Membership Card” information screen of FIG. 19A includes a list 1902 of parameters associated with the specific membership card of the user Frank, such as “Card Number,” “Effective Date,” “Expiration Date,” etc.); and
execute the generation-related processing further based on the generation number information ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.).
Regarding claim 9, Kanner teaches wherein the at least one processor is configured to: execute the generation-related processing relating to the generation of the storage data that is part of a record of the database ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
store, in the database, the record including the storage data as the part ([0057] and Fig. 1: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
Regarding claim 10, Kanner teaches wherein the at least one processor is configured to: acquire, based on input of the user, correction content information relating to correction content in the storage; execute correction-related processing relating to generation of correction portion data, which is post-correction data of a correction portion corresponding to the correction content in the storage data, based on the correction content information and the AI data ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system. [0075] and Fig. 7: In process 13, still in this example of the driver's license, the derived information 72 includes a new derived event “Expiration”; a new derived contact “Tom Smith”; a new derived residence and the derived associations between the contact, the residence and the driver's license, as represented also by FIG. 4.); and
correct the storage data by replacing the correction portion in the storage data by the correction portion data ([0057]: In process 14, the server system prompts the user to confirm or change the values of the new and derived information. [0066]: In this FIG. 5E, the user confirms the information type and the set of data fields by clicking on the prompt “The info is correct” 56. The user can also change the information type or some of the data fields by clicking “Edit” on this screen.).
Regarding claim 11, Kanner teaches wherein the at least one processor is configured to: acquire verification content information relating to verification content of the database ([0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
execute the generation-related processing further based on the verification content information ([0057]: In process 14, the server system prompts the user to confirm or change the values of the new and derived information. [0066]: In this FIG. 5E, the user confirms the information type and the set of data fields by clicking on the prompt “The info is correct” 56. The user can also change the information type or some of the data fields by clicking “Edit” on this screen.).
Claim 12 is rejected under the same rationale as claim 1.
Claim 13 is rejected under the same rationale as claim 1. Kanner also teaches A non-transitory information storage medium having stored thereon a program for causing a computer configured to support work of a user through use of a database designed with no-code or low-code ([0036]: A “computing device” is a device in including a microprocessor and memory configured to execute instructions; [0057] and Fig. 1: The embodiment of FIG. 1 is executed by a server system that has a computer-implemented infrastructure for receiving, organizing, categorizing, securing, and sharing any personal information (physical or digital) of users.).
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.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Kanner in view of Dsouza (US 2017/0220323).
Regarding claim 3, Kanner teaches the system of claim 1 as discussed above. Kanner does not explicitly teach wherein the at least one processor is configured to: acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information.
Dsouza teaches wherein the at least one processor is configured to: acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information ([0023]: In such case, the design retrieval system 101 extracts one or more attributes from the business requirement and technical specification documents. The one or more attributes entered by the users and/or extracted by the design retrieval system 101 comprises design attributes, domain specific attributes and system specific attributes… The design retrieval system 101 identifies one or more patterns for the keywords by applying at least one of artificial intelligence, machine learning, and default prompt pattern analysis and recognition techniques. The design retrieval system 101 generates a query string for searching the design database 105 based on the one more patterns identified from the keywords, in order to determine the architectural designs associated with the user inputs provided by the users.).
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 server system of Kanner with the teaching about the design retrieval system of Dsouza because it helps in improving the productivity, reducing the inconsistency with an organisation and also reduces re-work and maintenance efforts (Dsouza, [0064]).
Regarding claim 4, Kanner teaches the system of claim 1 as discussed above. Kanner does not explicitly teach wherein the at least one processor is configured to: acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt.
Dsouza teaches wherein the at least one processor is configured to: acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt ([0039]: The validation module 219 validates the one or more attributes by identifying the missing attributes in the one or more attributes received from the users. Further, the validation module 219 checks for the minimum availability of the attributes using which the architectural designs can be determined. Further, if the validation module identifies missing attributes, the validation module 219 prompts the users to provide with the missing attributes in order to determine the architectural designs associated with the software application.).
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 server system of Kanner with the teaching about the design retrieval system of Dsouza because it helps in improving the productivity, reducing the inconsistency with an organisation and also reduces re-work and maintenance efforts (Dsouza, [0064]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mathew et al. (US 11,138,218) discloses that extraction rules can be used to extract one or more values for a field from events by parsing the event data and examining the event data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends.
Baum et al. (US 2021/0398633) discloses that a prescription engine can be a neural network engine that is trained to receive image files, audio files, video files, text messages, etc., transmitted by mobile devices of prescribers and extract from the received files/messages prescriber, patient, prescribed item, etc., so as to populate the extracted information into the fields of a prescription form to generate a prescription in an electronic format. In some instances, the prescription engine can include or be coupled to other AI -powered engines which may be used in the extraction of the above-noted information.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H NGUYEN whose telephone number is (571)270-1766. The examiner can normally be reached Monday-Friday, 8:30am-5pm EST.
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, Ajay Bhatia can be reached at (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHONG H NGUYEN/ Primary Examiner, Art Unit 2156
January 28, 2026