DETAILED ACTION
This communication is in response to the Application filed on 10/18/2023 (provisional). Claims 1-22 are pending and have been examined.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 230, 232, 718. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: Multi-Expert and Infinite Context Automated Agent Method and Apparatus.
The disclosure is objected to because of the following informalities:
in ¶ [63]: “In some embodiments, other knowledge bases 210 may be contained in the MEICAA. 200 Such knowledge bases 210 may include court rules …” should read:
In some embodiments, other knowledge bases 210 may be contained in the MEICAA 200. Such knowledge bases 210 may include court rules …
in ¶ [82]: “IN some embodiments, the MEICAA 200 may include other scopes identifiers 228 ...” should read:
In some embodiments, the MEICAA 200 may include other scopes identifiers 228 ...
in ¶ [83]: “In some embodiments, tach of these categories of legal practice, in turn, may have increasingly detailed sub-specialties.” should read:
In some embodiments, each of these categories of legal practice, in turn, may have increasingly detailed sub-specialties.
in ¶ [89]: “IN some embodiments, the venue requirements module 518 may include venue requirements applicable to the matter reflected in a JSON object.” should read:
In some embodiments, the venue requirements module 518 may include venue requirements applicable to the matter reflected in a JSON object.
in ¶ [93]: “The expert scope identifier 224may incorporate more granularity ...” should read:
The expert scope identifier 224 may incorporate more granularity ...
in ¶ [94]: “A global rules engine in the MEICAA might identify the standards to be consult a global or document type scoped knowledge base to identify whether the applicable criteria had been met with each review.” should read:
A global rules engine in the MEICAA might identify the standards to consult a global or document type scoped knowledge base to identify whether the applicable criteria had been met with each review.
in ¶ [95]: “This may aid an individual or user to down select form the vast array of professionals to a select few knowingly qualified for their needs.” should read:
This may aid an individual or user to down select from the vast array of professionals to a select few knowingly qualified for their needs.
in ¶ [100]: “... (2) identified as relating to the tax code knowledge base by virtue of the a subject matter scope identifier reflecting the tax code ...” should read:
(2) identified as relating to the tax code knowledge base by virtue of the subject matter scope identifier reflecting the tax code ...”
in ¶ [107]: “In some embodiments, the method 600 may determine if the matter requires feedback from a qualified expert in order produce work product of sufficient quality ...” should read:
In some embodiments, the method 600 may determine if the matter requires feedback from a qualified expert in order to produce work product of sufficient quality ...
in ¶ [114]: “After no further review is required, at block 718, the method 700 output updated work product to be made available to the end user.” should read:
After no further review is required, at block 718, the method 700 output updated work product is made available to the end user.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claim 9 is 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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “rejecting one or more database updates when changes are not made by the unauthorized user” in claim 9 is used by the claim to mean “rejecting one or more database updates when changes are not made by the unauthorized user, or rejecting one or more database updates when changes are made by the authorized user,” while the accepted meaning is “rejecting one or more database updates when changes are made by the unauthorized user, or rejecting one or more database updates when changes are not made by the authorized user” (see [12], where the method may update one or more databases when the changes are made by the authorized user. In some embodiments, the method may include rejecting one or more database updates when changes are not made by the unauthorized user). The term is indefinite because the specification does not clearly redefine the term.
As such, claim 9 is being specifically interpreted as the following:
The method of claim 8, further comprising:
rejecting one or more database updates when changes are made by the unauthorized user.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim(s) 1, and 15, the limitation(s) of “receiving a request from a user,” “analyzing the request to determine at least one area of expertise applicable to the request,” “assigning a scope identifier to the at least one area of expertise,” and “generating an output on the user request and the scope identifier associated with the request,” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” “memory in electronic communication with the processor,” and “instructions stored in the memory and executable by the processor” specifically for claim 15, nothing in the claim’s elements preclude the step from practically being performed in the mind. More specifically, but not including the generic computer components, the mental processes of a human listening to a speaker’s request, categorizing said request based on its associated area or field of expertise, identifying the appropriate operations and updates based on the scope of the request, and producing a piece of relevant work associated to both the area of expertise and the user’s request. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, only claim 15 recites three additional elements — using a processor, memory in electronic communication with the processor, and instructions stored in the memory and executable by the processor.
The processor in these steps is recited with a high-level of generality (see [124], where the device 900 includes one or more processors 902(s) (e.g., a central processing unit (CPU) graphics processing unit(s) (GPU) or both)), such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Furthermore, the memory in electronic communication with the processor is recited with a high-level of generality (see [125], where the main memory 904 and the processor 902 also constituting machine-readable media. Also see [127], where while the machine-readable medium 922 is shown in an example embodiment to be a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals), such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Similarly, the instructions stored in the memory and executable by the processor are recited with a high-level of generality (see [125], where the disk drive unit 916 includes a machine-readable medium 922 on which is stored one or more sets of instructions (e.g., software 924) embodying any one or more of the methodologies or functions described herein. The software 924 may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the device 900, the main memory 904 and the processor 902 also constituting machine-readable media), such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, and concerning claim 15 alone, the additional element of using a processor, memory, and instructions to perform the receiving, analyzing, assigning, and outputting steps amount to no more than mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 2, and 17, the claim(s) recite(s) “generating an output based on the user request and the scope identifier associated with the request, wherein the output is a specialized work product,” which reads on a human producing a piece of specialized work associated to both the area of expertise and the user’s request within a specific topic or category. No additional limitations are present.
With respect to claim(s) 3, and 18, the claim(s) recite(s) “generating an output based on the user request and the scope identifier associated with the request, wherein the specialized work product is a legal work product,” which reads on a human producing a piece of specialized work of legal nature associated to both the area of expertise and the user’s request within the topic of law. No additional limitations are present.
With respect to claim(s) 4, and 19, the claim(s) recite(s) “assigning a scope identifier to the at least one area of expertise, wherein the scope identifier is associated with an area of law,” which reads on a human identifying the appropriate operations and updates based on the scope of the request within the topic of law. No additional limitations are present.
With respect to claim(s) 5, and 16, the claim(s) recite(s) “receiving user input requesting output review, identifying at least one qualified reviewer for the requested output review, receiving input from the at least one qualified reviewer; and updating the output based at least in part on the input from the at least one qualified reviewer,” which reads on a human listening to a speaker’s request for further review on the product, recognizing at least one person qualified to review the product per the user’s request, listening to the qualified person’s review on the product, and updating the product based on the review of the qualified individual’s output. No additional limitations are present.
With respect to claim(s) 6, and 20, the claim(s) recite(s) “assigning a credibility score to the output; and comparing the credibility score to an acceptable credibility score determined by the user,” which reads on a human marking a product with a credibility score, and measuring how acceptable this credibility score is based on a defined standard. No additional limitations are present.
With respect to claim(s) 7, and 21, the claim(s) recite(s) “determining a review type of the output based on the comparison,” which reads on a human choosing a certain review type based on how acceptable the credibility score of the output is based on the defined standard. No additional limitations are present.
With respect to claim(s) 8, and 22, the claim(s) recite(s) “analyzing changes to the output; determining which changes were made by an authorized user; and updating one or more databases when the changes are made by the authorized user,” which reads on a human reviewing a document or product to see what edits were made, identifying edits based on a known list of permitted individuals, and noting down the approved changed in a logbook, or record-keeping system. No additional limitations are present.
With respect to claim(s) 9, the claim(s) recite(s) “rejecting one or more database updates when changes are not made by the unauthorized user,” which reads on a human reviewing a proposed set of changes that were made by someone without permission, and then deciding not to record those changes in a database (e.g. refusing to enter data into a logbook). No additional limitations are present.
With respect to claim(s) 10, the claim(s) recite(s) “outputting, automatically, an original document from an original database; analyzing changes to the original document; determining which changes are made by at least one authorized reviewer; and updating the original database when the changes are made by the at least one authorized reviewer,” which reads on a human retrieving from a filing cabinet, folder, reviewing that document to identify any edits or markups, comparing those edits against a mental or written list of individuals permitted to make changes, and placing the updated document back into the filing cabinet or folder upon determining that an authorized individual made the changes. No additional limitations are present.
With respect to claim(s) 11, the claim(s) recite(s) “receiving credentials from a reviewer; analyzing the credentials of the reviewer; determining when the reviewer is an expert in a specific field; and assigning a scope identifier to the expert reviewer when the reviewer is an expert in the specific field,” which reads on a human accepting a written resume or verbal statement of qualifications from another person, reviewing that person’s background and experience, judging whether that person meets the threshold of being an “expert” in a specific field, and labeling that person as an expert for future reference, either by writing it down or checking a box on a paper form. No additional limitations are present.
With respect to claim(s) 12, the claim(s) recite(s) “outputting an original document when an original request from a user is received," which reads on a human receiving a verbal or written request from another person for a document or written product, and then physically handing over an original copy of said document. No additional limitations are present.
With respect to claim(s) 13, the claim(s) recite(s) “analyzing the original request from the user; and assigning an area of expertise to the request based at least in part on the analyzation,” which reads on a human reading or listening to a user’s request, breaking down the content of the request to understand what it concerns, and determining and labeling which field of expertise the request falls under. No additional limitations are present.
With respect to claim(s) 14, the claim(s) recite(s) “wherein the authorized reviewer is an expert in the area of expertise assigned to the request,” which reads on a human ensuring that the person selected to review a request possesses the necessary qualification and experience in the specific field the request belongs to. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 4, 15, 16, 17, 18, and 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Poon (US 20240428354 A1).
Regarding claim 1, Poon teaches a method to produce an automated work product, the method comprising:
receiving a request from a user (see [0017], where the system receives a request for AI processing from a user, wherein the request comprises a quantity value associated with the AI processing; performs the AI processing to generate output based on the request; estimates a processing metric for the quantity value associated with the AI processing, wherein the processing metric comprises a value indicative of the amount of processing performed; determines a cost value based on the modified processing metric, wherein the cost value reflects the cost associated with the AI processing; and provides the cost value to the user for billing purposes in relation to the AI processing);
analyzing the request to determine at least one area of expertise applicable to the request (see [0062], where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing);
assigning a scope identifier to the at least one area of expertise (see [0062], where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing); and
generating an output based on the user request and the scope identifier associated with the request (see [0043], where at step 320, the system performs the AI processing to generate output based on the request. Also see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals. Also see [0045], where the legal work product output can potentially encompass a wide range of materials and documents commonly utilized in legal practice. In various embodiments, this may include one or more of, e.g., draft contracts, legal memos, research summaries, legal opinions, or other forms of legal analysis. In some embodiments, the AI processing leverages its capabilities, such as, e.g., understanding legal concepts, analyzing textual information, and retrieving relevant legal knowledge, to generate output that aligns with the standards and requirements of legal work products).
Regarding claim 2, Poon teaches the method of claim 1, wherein the output is a specialized work product (see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals).
Regarding claim 3, Poon teaches the method of claim 2, wherein the specialized work product is a legal work product (see [0045], where the legal work product output can potentially encompass a wide range of materials and documents commonly utilized in legal practice. In various embodiments, this may include one or more of, e.g., draft contracts, legal memos, research summaries, legal opinions, or other forms of legal analysis. In some embodiments, the AI processing leverages its capabilities, such as, e.g., understanding legal concepts, analyzing textual information, and retrieving relevant legal knowledge, to generate output that aligns with the standards and requirements of legal work products).
Regarding claim 4, Poon teaches the method of claim 3, wherein the scope identifier is associated with an area of law (see [0057], where the processing metric provides insights into the amount of work performed by the AI system, offering a quantifiable indicator of the computational effort expended. It captures the level of sophistication, depth, and intricacy involved in the AI processing. For instance, in the legal domain, the processing metric may reflect the degree of legal analysis conducted, the level of language understanding and generation, or the complexity of the legal reasoning applied).
Regarding claim 15, Poon teaches an apparatus for generating an output, comprising:
a processor (see [0094], where processor 401 may perform computing functions, such as running computer programs);
memory in electronic communication with the processor (see [0094], where the volatile memory 402 may provide temporary storage of data for the processor 401. RAM is one kind of volatile memory. Volatile memory typically requires power to maintain its stored information. Storage 403 provides computer storage for data, instructions, and/or arbitrary information. Non-volatile memory, which can preserve data even when not powered and including disks and flash memory, is an example of storage. Storage 403 may be organized as a file system, database, or in other ways. Data, instructions, and information may be loaded from storage 403 into volatile memory 402 for processing by the processor 401); and
instructions stored in the memory and executable by the processor (see [0094], where data, instructions, and information may be loaded from storage 403 into volatile memory 402 for processing by the processor 401. Also see [0100], where the present disclosure may be provided as a computer program product, or software, that may include a machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure) to cause the apparatus to:
receive a request from a user (see [0017] where, the system receives a request for AI processing from a user, wherein the request comprises a quantity value associated with the AI processing; performs the AI processing to generate output based on the request; estimates a processing metric for the quantity value associated with the AI processing, wherein the processing metric comprises a value indicative of the amount of processing performed; determines a cost value based on the modified processing metric, wherein the cost value reflects the cost associated with the AI processing; and provides the cost value to the user for billing purposes in relation to the AI processing;
analyze the request to determine an area of expertise applicable to the request (see [0062], where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing);
assign a scope identifier to the area of expertise (see [0062], where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing); and
generate an output based on the user request and the scope identifier associated with the request (see [0043], where at step 320, the system performs the AI processing to generate output based on the request. Also see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals. Also see [0045], where the legal work product output can potentially encompass a wide range of materials and documents commonly utilized in legal practice. In various embodiments, this may include one or more of, e.g., draft contracts, legal memos, research summaries, legal opinions, or other forms of legal analysis. In some embodiments, the AI processing leverages its capabilities, such as, e.g., understanding legal concepts, analyzing textual information, and retrieving relevant legal knowledge, to generate output that aligns with the standards and requirements of legal work products).
Regarding claim 17, Poon teaches the apparatus of claim 15, wherein the specialized work product is a legal work product (see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals).
Regarding claim 18, Poon teaches the apparatus of claim 15, wherein the specialized work product is a legal work product (see [0045], where the legal work product output can potentially encompass a wide range of materials and documents commonly utilized in legal practice. In various embodiments, this may include one or more of, e.g., draft contracts, legal memos, research summaries, legal opinions, or other forms of legal analysis. In some embodiments, the AI processing leverages its capabilities, such as, e.g., understanding legal concepts, analyzing textual information, and retrieving relevant legal knowledge, to generate output that aligns with the standards and requirements of legal work products).
Regarding claim 19, Poon teaches the apparatus of claim 18, wherein the scope identifier is associated with an area of law (see [0057], where the processing metric provides insights into the amount of work performed by the AI system, offering a quantifiable indicator of the computational effort expended. It captures the level of sophistication, depth, and intricacy involved in the AI processing. For instance, in the legal domain, the processing metric may reflect the degree of legal analysis conducted, the level of language understanding and generation, or the complexity of the legal reasoning applied).
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) 5, 8, 10, 16, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poon in view of Lipman (US 20200311688 A1).
Regarding claim 5, Poon teaches all the limitations of claim 1 but fails to teach receiving user input requesting output review; identifying at least one qualified reviewer for the requested output review; receiving input from the at least one qualified reviewer; and updating the output based at least in part on the input from the at least one qualified reviewer.
However, Lipman does teach:
receiving user input requesting output review (see [0161], where save button 1830 provides supervisors with the ability to delay completion of a document and save it for later completion. After a supervisor decides that they have finished filling out the template and creating the document, they can select Submit for Review button 1840. Optionally, they can delete the draft by clicking on a cancel button 1860. Also see [0094], where after the draft document is saved and submitted for review the reviewer receives a notification that the draft document is ready for review (Step 212);
identifying at least one qualified reviewer for the requested output review (see [0008], where the users (i.e., employees and contractors) are all assigned an identifier, such as a unique email address and those addresses are associated with one or more credential levels, such as “Employee”, “Contractor”, “Administrator”, “Supervisor”, “Reviewer” or “HR reviewer”, “HR” and/or “Hotline Recipient”. Also see [0094], where one user (employee or contractor) for each employer is designated to fill the “reviewer” role);
receiving input from the at least one qualified reviewer (see [0010], where the reviewer can then open and review the draft document, and then either approve or reject the draft document. If the reviewer rejects the draft document, the reviewer can coach the supervisor to produce a more effective document by editing, asking questions, and/or adding comments to the draft document. These edits, comments or questions are returned with the draft to the supervisor. Also see [0096], where if the reviewer does not approve the draft document, a reviewer-supervisor review/revision loop is created. In that review/revision loop, the reviewer can suggest edits and/or add questions or comments to the draft document for the supervisor (Step 218). After these edits, questions, and comments are saved, the reviewer can send the draft back to the supervisor); and
updating the output based at least in part on the input from the at least one qualified reviewer (see [0011], where after the supervisor accepts or rejects the reviewer's edits, responds to questions and comments, and/or asks questions, the supervisor resubmits the draft to the reviewer. The system then notifies the reviewer that the draft document is again ready for their new review. This process is repeated until the supervisor has created a document that is “Approved” by the reviewer. Also see [0097], where when the supervisor logs into the system, they can open the draft document and see the reviewer's edits, questions, and/or comments (Step 222). Next, the supervisor can either accept or reject the reviewer's edits and make changes based on the questions and/or comments (Step 224)).
Poon and Lipman are both considered to be analogous to the claimed invention because they are in the same field of systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon to incorporate the teachings of Lipman to receive user input requesting output review; identify at least one qualified reviewer for the requested output review; receive input from the at least one qualified reviewer; and update the output based at least in part on the input from the at least one qualified reviewer in order to remove the delays caused by face-to-face review meetings, and allow iterative electronic updates without the need to coordinate schedules (see [0006], where the entire submission, review and disposition are performed electronically and there’s no need to a face-to-face meeting for reviewing of the submitted invention).
Regarding claim 8, Poon teaches all the limitations of claim 1 but fails to teach analyzing changes to the output; determining which changes were made by an authorized user; and updating one or more databases when the changes are made by the authorized user.
However, Lipman does teach:
analyzing changes to the output (see [0096], where if the reviewer does not approve the draft document, a reviewer-supervisor review/revision loop is created. In that review/revision loop, the reviewer can suggest edits and/or add questions or comments to the draft document for the supervisor (Step 218). After these edits, questions, and comments are saved, the reviewer can send the draft back to the supervisor. Also see [0097], when the supervisor logs into the system, they can open the draft document and see the reviewer's edits, questions, and/or comments (Step 222). Next, the supervisor can either accept or reject the reviewer's edits and make changes based on the questions and/or comments (Step 224));
determining which changes were made by an authorized user (see [0008], where the users (i.e., employees and contractors) are all assigned an identifier, such as a unique email address and those addresses are associated with one or more credential levels, such as “Employee”, “Contractor”, “Administrator”, “Supervisor”, “Reviewer” or “HR reviewer”, “HR” and/or “Hotline Recipient”); and
updating one or more databases when the changes are made by the authorized user (see [0016], where after the HR reviewer approves the draft document, the Document Creation system can add the approved document to the blockchain to make it retrievable and immutable. Also see [0097], where after the supervisor addresses the reviewer's questions, comments and edits, including, accepting or rejecting specific proposed reviewer edits the supervisor resubmits the draft to the reviewer for review. The reviewer then receives a new notification that the draft document is again ready for review (Step 212). The reviewer again logs into the system (Step 214) to open and review the draft (Step 216). The loop repeats until the reviewer approves the draft document. Also see [0101], where either after the reviewer approves the draft or the supervisor responds to the HR reviewer's proposed revisions, the HR reviewer accepts the draft document and the draft document becomes a final document and is added to the blockchain (Step 244) and it can no longer be changed).
Poon and Lipman are both considered to be analogous to the claimed invention because they are in the same field of systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon to incorporate the teachings of Lipman to receive user input requesting output review; identify at least one qualified reviewer for the requested output review; receive input from the at least one qualified reviewer; and update the output based at least in part on the input from the at least one qualified reviewer in order to ensure that only authorized users and approved modifications can be permanently saved while unauthorized changes are rejected, providing an improvement in both document generation and document security (see [0004], where businesses experience issues of privacy, confidentiality, and control of the information HR and non-HR employees can access. Also see [0005], where there is a need for a system and method for document and documentation creation, communication, management and verification of documents as pertaining to the employment context. Also see [0016], where after the HR reviewer approved the draft document, the Document Creation system can add the approved document to the blockchain. Also see [0097], where when the supervisor logs into the system, they can open the draft document and see the reviewer's edits, questions, and/or comments (Step 222)).
Regarding claim 10, Poon teaches a method of training an artificial intelligence language model, the method comprising:
outputting, automatically, an original document from an original database (see [0026], where the legal services platform may integrate with existing legal databases, knowledge repositories, and external data sources to enhance the quality and accuracy of the AI processing. In some embodiments, it may also incorporate machine learning algorithms that continuously learn from user interactions and feedback to improve the output of the AI processing. Also see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals. Also see [0048], where the performance of the AI processing involves the system's ability to leverage vast amounts of, e.g., data, training models, and/or knowledge repositories. The system may apply one or more AI models and/or methodologies to extract insights, make informed decisions, and generate high-quality output. The system may utilize, e.g., pre-existing templates, legal databases, or proprietary knowledge bases to enhance the accuracy, relevance, and/or efficiency of the output generation process);
but fails to teach analyzing changes to the original document; determining which changes are made by at least one authorized reviewer; and updating the original database when the changes are made by the at least one authorized reviewer.
However, Lipman does teach:
analyzing changes to the original document (see [0096], where if the reviewer does not approve the draft document, a reviewer-supervisor review/revision loop is created. In that review/revision loop, the reviewer can suggest edits and/or add questions or comments to the draft document for the supervisor (Step 218). After these edits, questions, and comments are saved, the reviewer can send the draft back to the supervisor. Also see [0097], when the supervisor logs into the system, they can open the draft document and see the reviewer's edits, questions, and/or comments (Step 222). Next, the supervisor can either accept or reject the reviewer's edits and make changes based on the questions and/or comments (Step 224));
determining which changes are made by at least one authorized reviewer; and (see [0008], where the users (i.e., employees and contractors) are all assigned an identifier, such as a unique email address and those addresses are associated with one or more credential levels, such as “Employee”, “Contractor”, “Administrator”, “Supervisor”, “Reviewer” or “HR reviewer”, “HR” and/or “Hotline Recipient”); and
updating the original database when the changes are made by the at least one authorized reviewer (see [0016], where after the HR reviewer approves the draft document, the Document Creation system can add the approved document to the blockchain to make it retrievable and immutable. Also see [0097], where after the supervisor addresses the reviewer's questions, comments and edits, including, accepting or rejecting specific proposed reviewer edits the supervisor resubmits the draft to the reviewer for review. The reviewer then receives a new notification that the draft document is again ready for review (Step 212). The reviewer again logs into the system (Step 214) to open and review the draft (Step 216). The loop repeats until the reviewer approves the draft document. Also see [0101], where either after the reviewer approves the draft or the supervisor responds to the HR reviewer's proposed revisions, the HR reviewer accepts the draft document and the draft document becomes a final document and is added to the blockchain (Step 244) and it can no longer be changed).
Poon and Lipman are both considered to be analogous to the claimed invention because they are in the same field of systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon to incorporate the teachings of Lipman to receive user input requesting output review; identify at least one qualified reviewer for the requested output review; receive input from the at least one qualified reviewer; and update the output based at least in part on the input from the at least one qualified reviewer in order to create a closed-loop training system for an artificial language model where the AI model learns from both the original document and the authorized reviewer’s modifications, enhancing the AI model’s accuracy while maintaining a secure record of all changes (see Poon’s [0015], where while these existing platforms offer valuable resources, they fail to fully address the need for efficient, cost-effective legal services. Lawyers still struggle to accurately track and bill for the time spent utilizing these tools, resulting in potential revenue losses and suboptimal pricing models. Moreover, the existing solutions do not provide a comprehensive mechanism for estimating the value and efficiency gained through the use of artificial intelligence (AI) and language models in legal work. Also see [0016], where to overcome these limitations, there is a need in the field of legal services to create a new and useful system for intelligent valuation of AI processing in a legal services platform).
Regarding claim 16, which depends from claim 15 and recites an apparatus, this claim is rejected as unpatentable over the same combination of prior art applied against claim 5 (i.e., Poon in view of Lipman). Poon teaches all the limitations of claim 15 as noted above. As detailed in the rejection of claim 5, the disclosed method teaches or renders obvious each step of the apparatus recited in claim 16. Accordingly, claim 16 is rejected for the same reasons set forth in the rejection of claim 5.
Regarding claim 22, which depends from claim 15 and recites an apparatus, this claim is rejected as unpatentable over the same combination of prior art applied against claim 8 (i.e., Poon in view of Lipman). Poon teaches all the limitations of claim 15 as noted above. As detailed in the rejection of claim 8, the disclosed method teaches or renders obvious each step of the apparatus recited in claim 22. Accordingly, claim 22 is rejected for the same reasons set forth in the rejection of claim 8.
Claim(s) 6, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poon in view of Magary (US 11966704 B1).
Regarding claim 6, Poon teaches all the limitations in claim 1 but fails to teach assigning a credibility score to the output; and comparing the credibility score to an acceptable credibility score determined by the user.
However, Magary does teach:
assigning a credibility score to the output (see column 7, lines 2-7, where the technology developed by the inventors may generate a score, such as a reliability score, indicative of a degree to which the ML model itself is reliable to generate verified output. For example, the score may indicate a probability to which the ML model is likely to hallucinate the output); and
comparing the credibility score to an acceptable credibility score determined by the user (see column 18, lines 22-27, where the model output verification software application 102 can cause aperiodic training of the generative ML model 110, such as when a threshold amount of generated feedback data has been reached and/or satisfied or when the average reliability score of the generative ML model 110 falls below a specified threshold).
Poon and Magary are both considered to be analogous to the claimed invention because they are in the same field of handling natural language data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon to incorporate the teachings of Magary to assign a credibility score to the output; and compare the credibility score to an acceptable credibility score determined by the user in order to quantify the reliability of an automatically generated legal document, improving document accuracy and reducing the risk of user reliance on erroneous language (see column 6, lines 60-67, where the technology developed by the inventors mitigates the unreliability of model outputs by assigning a metric to respective model outputs indicating a degree to which a model output is verified. For example, the technology developed by the inventors may generate a score, such as a verification score, indicative of a degree to which the model output is not verified, partially verified, or verified based on a comparison of the model output to data source(s), such as publicly accessible and reliable data source(s)).
Regarding claim 20, which depends from claim 15 and recites an apparatus, this claim is rejected as unpatentable over the same combination of prior art applied against claim 6 (i.e., Poon in view of Magary). Poon teaches all the limitations of claim 15 as noted above. As detailed in the rejection of claim 6, the disclosed method teaches or renders obvious each step of the apparatus recited in claim 20. Accordingly, claim 20 is rejected for the same reasons set forth in the rejection of claim 6.
Claim(s) 7, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poon in view of Magary (US 11966704 B1) and further in view of Peters (US 20200372557 A1)
Regarding claim 7, which depends on both claim 1 and claim 6, Poon teaches all the limitations in claim 1, and Magary teaches all the limitations in claim 6, respectively; however, both fail to teach determining a review type of the output based on the comparison.
However, Peters does teach determining a review type of the output based on the comparison (see [0142], where the classification system 202 may determine a reliability score that is below a threshold and may generate an alert to an operator to review the product information 244, the regulatory classification data 246, the business classification data 248, or any combination thereof. In some implementations, the alert may allow the user to review, edit, and optionally authenticate the data. Other implementations are also possible).
Poon, Magary, and Peters are considered to be analogous to the claimed invention because they are in the same field of handling natural language data, and systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon and Magary to incorporate the teachings of Peters to determine a review type of the output based on the comparison in order to automatically route low-confidence outputs to expert reviewers for manual validation while allowing high-confidence outputs to proceed without needing further intervention, improving system efficiency and quality control (see [0138, where if the reliability value is less than the threshold, the product information, regulatory classifications 246, business logic classifications 248, and the reliability values may be provided to a device associated with an administrator. Also see [0136] where if the reliability value greater than or equal to a threshold, data related to the regulatory classifications and the business logic classifications may be provided to the computing device, at 1010).
Regarding claim 21, which depends from claim 20 and claim 15, and recites an apparatus, this claim is rejected as unpatentable over the same combination of prior art applied against claim 7 (i.e., Poon in view of Magary and Peters). Poon teaches all the limitations of claim 15 as noted above. Likewise, Magary teaches all the limitations of claim 20 as noted above. As detailed in the rejection of claim 7, the disclosed method teaches or renders obvious each step of the apparatus recited in claim 21. Accordingly, claim 21 is rejected for the same reasons set forth in the rejection of claim 7.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Lipman (US 20200311688 A1) and further in view of Thompson (US 20070220068 A1).
Regarding claim 9, which depends on both claim 1 and claim 8, Poon teaches all the limitations in claim 1, and Lipman teaches all the limitations in claim 8, respectively; however, both fail to teach rejecting one or more database updates when changes are made by the unauthorized user.
However, Thompson does teach rejecting one or more database updates when changes are made by the unauthorized user (see [0008], where the utilities generate a mirror copy of all controlled or "original" files. The utilities then applies/controls to the original files. While users may access and change the original files, the utility may revert to the mirror copy of the file if the user does not have the authority (e.g., as determined from log in information or other identification information) to make the changes. That is, after the unauthorized user alters (e.g., edits, deletes, etc.) a file, the utility may replace the unauthorized edited file with the last authorized version of the file (i.e. the mirror copy). Alternatively, when a user with authority to change a file edits a file and/or such changes are approved, the mirror copy may be updated. In this regard, the mirror copy becomes the latest authorized version of the file. Such controlled access to and replacement of files allows for controlling numerous business processes. Further, such a utility may allow for access to all files in a file system such that users/employees may review the documents without having the ability to alter the mirror copy.
Poon, Lipman, and Thompson are considered to be analogous to the claimed invention because they are in the same field of handling natural language data, and systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon and Lipman to incorporate the teachings of Peters to reject one or more database updates when changes are not made by the unauthorized user in order to automatically restore unauthorized changes to a generated document and replacing it with a mirror copy of the last authorized version, therefore improving corruption of a master document and maintaining data integrity and security (see abstract, where an electronic file control system is presented that generates a mirror copy of all controlled or "original" files. The system then allows controlled access to the original files. However, changes to the original files are not reflected in the mirror copies until authorized by an approving entity. That is, while users may access and change the original files, the utility may revert to the mirror copy of the file if the user does not have the authority to make the changes. That is, after the unauthorized user alters (e.g., edits, deletes, etc.) a file, the utility may replace the unauthorized edited file with the last authorized version of the file (i.e. the mirror copy). Alternatively, when such changes are approved, the mirror copy may be updated and thereby become the latest authorized version of the file).
Claim(s) 11, 12, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poon in view of Lipman (US 20200311688 A1) and further in view of Martinez (US 20080040355 A1).
Regarding claim 11, which depends on both claim 1 and claim 10, Poon teaches all the limitations in claim 1, and both Poon and Lipman teach all the limitations in claim 10; however, both fail to teach receiving credentials from a reviewer; analyzing the credentials of the reviewer; determining when the reviewer is an expert in a specific field; and assigning a scope identifier to the expert reviewer when the reviewer is an expert in the specific field.
However, Martinez does teach:
receiving credentials from a reviewer (see [0058], where the selection of a review team/reviewers involves retrieving information about the review team/reviewers from a centralized reviewer database 450. This reviewer database 450 may include information about review teams and/or individual reviewers for use in matching the review teams/reviewers with invention disclosures that have technology and business subject matter coverage that overlaps the review team/reviewers' areas of expertise. The reviewer database 450 stores information regarding the work experience, title, job role, physical location, organizational location, and the like, for each review team and/or reviewer. If the reviewer database 450 operates from a review team level, then the data stored for each review team may be an aggregate of information obtained about each individual reviewer associated with that review team);
analyzing the credentials of the reviewer (see [0062], where the requirements for selection of individuals from the candidate directory 485 for inclusion in the reviewer database 450 is implementation specific and is governed by the eligibility rules 490 established by the particular business organization. The eligibility determination engine 480 applies these eligibility rules to the entries in the candidate directory 485 periodically, or upon request, to select additional reviewers to be added to the reviewer database 450);
determining when the reviewer is an expert in a specific field (see [0059], where the reviewer database 450 may store expertise profiles 455 for each review team/reviewer. These expertise profiles 455 may identify the categories of technology/business that the reviewer is considered to have sufficient knowledge in so that the reviewer is eligible to review invention disclosures in these categories of technology/business. Also see [0060], where these categories may then be used to augment the expertise profiles 455 by adding categories to the expertise profiles 455 when a sufficient number of invention disclosures are reviewed and/or submitted by the reviewer. In this way, the reviewer's expertise profile may be dynamically updated as the reviewer submits new invention disclosures or reviews invention disclosures in various technology/business categories); and
assigning a scope identifier to the expert reviewer when the reviewer is an expert in the specific field (see [0073], where each category and subcategory has an associated identifier that may be used to identify the category/subcategory. In the depicted example, the identifier is the "Tech Tag" and is an alphanumeric identifier. In most instances the "Tech Tag" is a number such as "600" but in some instances related subcategories may be denoted by tech tags such as "810A." Other types of identifiers may be used without departing from the spirit and scope of the present invention. ... In addition, the same taxonomy and category identifiers may be used to represent a reviewer's expertise in a particular technology/business area. Thus, the category identifiers may be stored in a reviewer's expertise profile thereby identifying the reviewer as having knowledge in a particular category of technology/business).
Poon, Lipman, and Martinez are considered to be analogous to the claimed invention because they are in the same field of handling natural language data, and systems or methods specially adapted for legal, administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon and Lipman to incorporate the teachings of Martinez to receive credentials from a reviewer; analyze the credentials of the reviewer; determine when the reviewer is an expert in a specific field; and assign a scope identifier to the expert reviewer when the reviewer is an expert in the specific field in order to streamline the reviewing process of an output to the correct reviewing team or person with field expertise, therefore reducing costs and improving quality control (see [0006], where invention disclosures are often routed to review teams based on physical and/or organizational constructs rather than the expertise of the particular review team. Also see [0007], where [review] routing approaches that rely on physical location or organizational affiliations have drawbacks. Also see [0009], where all of these problems translate into a needless expense for the company. Therefore, it would be beneficial to have a system and method for routing invention disclosures to review teams based on the expertise of the review team members).
Regarding claim 12, which depends on claim 1, claim 10, and claim 11, Poon teaches all the limitations in claim 1, Poon and Lipman teach all the limitations in claim 10, and Martinez teaches all the limitations in claim 11.
Furthermore, Poon does teach outputting an original document when an original request from a user is received (see [0026], where the legal services platform may integrate with existing legal databases, knowledge repositories, and external data sources to enhance the quality and accuracy of the AI processing. In some embodiments, it may also incorporate machine learning algorithms that continuously learn from user interactions and feedback to improve the output of the AI processing. Also see [0044], where the AI processing generates a legal work product output. That is, within the legal services platform, the output generated by the AI processing takes the form of and represents a legal work product that is intended to be relevant and useful for legal professionals. Also see [0048], where the performance of the AI processing involves the system's ability to leverage vast amounts of, e.g., data, training models, and/or knowledge repositories. The system may apply one or more AI models and/or methodologies to extract insights, make informed decisions, and generate high-quality output. The system may utilize, e.g., pre-existing templates, legal databases, or proprietary knowledge bases to enhance the accuracy, relevance, and/or efficiency of the output generation process).
Regarding claim 13, which depends on claim 1, claim 10, claim 11, and claim 12, Poon teaches all the limitations in claim 1 and claim 12, Poon and Lipman teach all the limitations in claim 10, and Martinez teaches all the limitations in claim 11.
Furthermore, Poon does teach analyzing the original request from the user (see [0017], where the system receives a request for AI processing from a user, wherein the request comprises a quantity value associated with the AI processing; performs the AI processing to generate output based on the request; estimates a processing metric for the quantity value associated with the AI processing, wherein the processing metric comprises a value indicative of the amount of processing performed; determines a cost value based on the modified processing metric, wherein the cost value reflects the cost associated with the AI processing; and provides the cost value to the user for billing purposes in relation to the AI processing. Also see [0062, where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing.); and assigning an area of expertise to the request based at least in part on the analyzation (see [0062], where the weighted percentages serve as modifiers that may adjust the processing metric based on specific considerations deemed relevant by the legal services platform or its users. These considerations may include, e.g., factors such as the priority level of the request, the level of expertise required for the AI processing, or the degree of customization and tailored output provided by the AI processing).
Regarding claim 14, which depends on claim 1, claim 10, claim 11, claim 12, and claim 13, Poon teaches all the limitations in claim 1, claim 12, and claim 13, Poon and Lipman teach all the limitations in claim 10, and Martinez teaches all the limitations in claim 11.
Furthermore, Martinez does teach wherein the authorized reviewer is an expert in the area of expertise assigned to the request (see [0059], where the reviewer database 450 may store expertise profiles 455 for each review team/reviewer. These expertise profiles 455 may identify the categories of technology/business that the reviewer is considered to have sufficient knowledge in so that the reviewer is eligible to review invention disclosures in these categories of technology/business. Also see [0060], where these categories may then be used to augment the expertise profiles 455 by adding categories to the expertise profiles 455 when a sufficient number of invention disclosures are reviewed and/or submitted by the reviewer. In this way, the reviewer's expertise profile may be dynamically updated as the reviewer submits new invention disclosures or reviews invention disclosures in various technology/business categories.
Poon, Lipman, and Martinez are considered to be analogous to the claimed invention because they are in the same field of handling natural language data and systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Poon, Lipman, and Martinez to incorporate the teachings of Cronin to include a method where the authorized reviewer is an expert in the area of expertise assigned to the request in order to ensure the generated legal documents are reviewed by individuals with genuine expertise in the subject matter without the need for face-to-face review meetings or incorrect review routing, therefore improving review quality and efficiency, and reducing legal risk (see [0006], where invention disclosures are often routed to review teams based on physical and/or organizational constructs rather than the expertise of the particular review team. Also see [0007], where [review] routing approaches that rely on physical location or organizational affiliations have drawbacks. Also see [0009], where all of these problems translate into a needless expense for the company. Therefore, it would be beneficial to have a system and method for routing invention disclosures to review teams based on the expertise of the review team members).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN HONG FANG-WU whose telephone number is (571)270-0607. The examiner can normally be reached Monday - Friday, 8AM to 5PM.
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/JOHN HONG FANG-WU/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
06/10/2026
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