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 .
In response to Applicant’s claims filed on July 31, 2025, claims 1-3, 5-11, 13-19 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 07/31/2025. In this action claim(s) 1-3, 5-11, 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunn et al. [1] (US Pub. No. 20180315141) and Tran et al. (US Pub. No. 20210256070) and Engineer et al. (US Patent No. 10726374) in further view of Banerjee et al. (US Pub. No. 20190266231). The Tran et al. reference has been added to address the amendment of detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time; and continuously determining, using the at least one processor, based on the detected event data, whether the time-based condition has been met.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues “In contrast, claim 1 is directed to solving a technical problem of providing an ability to assess real-time continuously received data from computing devices/sensors for the purposes of determine whether or not certain time-based conditions are met and executing further actions as a result…. Further, without conceding to the merits of the Office Action’s discussion related to Step 2A, Prong 1 and Step 2B, as previously stated, the undersigned respectfully submits that the subject matter of the amended claim 1 is clearly integrated into a practical application, similar to the concepts discussed in Example 47 of the 2024 PEG Update, thereby satisfying requirements of Step 2A, Prong 2. In particular, the current subject matter advantageously continuously receives sensor data and continuously determines in real-time whether certain time-based conditions are or are not met, so that appropriate actions (as extracted from the document) need to be executed.”
Examiner’s Reply:
The inputting and receiving of document data steps recite insignificant extra solution activity that amounts to mere data gathering. Determining if a contract is satisfied does not improve the functioning of a computer.
Applicant’s arguments:
In regards to claim 1 on Page(s) 12, applicant argues “As discussed herein, the specific improvement highlighted by the specification of the present application and recited in the amended claim 1 is the ability to continuously and in real time determine whether document identified time-based conditions are met/are not met and as a result, determine and execute certain actions identified in the document. This is similar to the specific improvement discussed with regard to claim 3 in Example 47, where an action is taken (e.g., blocking future traffic) based on monitored data in real time. This is a specific improvement of the prior art systems that have not been able to provide in the field of continuous, real-time determination of specific actions based on analysis of context of electronic documents and continuously received sensor data..”
Examiner’s Reply:
Applicant argues that the amended claims comprises statutory subject matter. Examiner respectfully disagrees. If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction or mental process egg determining contract conditions are met within a computing environment (being used a generic tools)), then it falls within the “Mental process” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea. The examiner notes that the computer as recited in the claims are being used for analyzing documents and their clauses with machine learning (being used a generic tools). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-3, 5-11, 13-19 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) 1, 9, and 17 contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In claims 1, 9, and 17 the newly added limitations of “the event data is continuously received” and “continuously determine, based on the detected event data, whether the time- based condition has been met;” are not supported by the instant spec.
Applicants suggested Par. [0016] of the instant spec as providing support. However, the paragraph only discloses continuously tracking of GPS sensor data. First, event data, as claimed, is not limited to sensor data. Second, the sensor data, as claimed in claim 6, is not limited to GPS sensor data. Moreover, the spec is silent on continuously determining anything.
Dependent claims 2-3, 5-8, 10-11, 13-16, 18-19 is/are also rejected for inheriting the deficiencies of the independent claims from which they depend on.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-11, 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-3, 5-11, 13-19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-3, 5-8), non-transitory computer-readable storage medium (claim 9-11, 13-16), system (claims 17-19) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 9, and 17 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1, 9, and 17 recites the following limitations directed towards a Mental Processes:
applying, using the at least one processor, at least another machine-learned model to analyze the document to identify one or more computing devices associated with the document and one or more sensors associated with the document, the one or more sensors detect sensor data associated with one or more events, each event in the one or more events is associated with event data, the sensor data is at least a portion of the event data (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses);
for each time-based condition in the one or more time-based conditions:
identifying, using the at least one processor, a respective database cataloging the one or more events corresponding to the time-based condition (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses);
detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses); and
continuously determining, using the at least one processor, based on the detected event data, whether the time-based condition has been met (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses); and
determining, using the at least one processor, based on the detected event data, at least one time-based condition in the one or more time-based conditions that has not been met within a predetermined amount of time associated with the at least one time-based conditions, and triggering, using the at least one processor, transmitting of an alert to the one or more computing devices (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses); and
parsing, using the at least one processor, the document to extract one or more actions in response to the determining whether the time-based condition has been met, and triggering execution of the one or more actions (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 9, and 17:
inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document, the machine-learned model has been trained using one or more clauses of the document and one or more deviations of the one or more clauses to identifying the one or more time-based conditions for determining whether the amount of time in which the event needs to occur met the one or more time-based conditions, wherein the one or more deviations of the one or more clauses have one or more word association strengths that are similar to one or more word association strengths of the one or more clauses (recites insignificant extra solution activity that amounts to mere data gathering);
receiving, using the at least one processor, the one or more time-based conditions from the machine-learned model (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 9, and 17 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 10, and 18:
Step 2A Prong One Analysis:
wherein the one or more databases include information describing signature information of the document and shipping updates related to the document (The limitation recites insignificant extra solution activity that amounts to mere data gathering).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3, 11, and 19:
accessing historical documents, wherein one or more portions of each historical document is labeled with an event; and training the machine-learned model on the labeled historical documents (insignificant extra solution activity that amounts to mere data gathering).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5 and 13:
wherein determining based on the event data whether the time-based condition has been met comprises: comparing the sensor data to a threshold; and determining, in response to determining the sensor data misaligns with the threshold, that the time-based condition has been met (The limitation recites a mental process of evaluation and/or judgement capable of being performed by the human mind by reading, contracts and identifying time sensitive clauses).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6 and 14:
wherein the sensors include at least one of the following: a pressure sensor, a temperature sensor, a radio-frequency identification (RFID) sensor, an RFID tag, a light sensor, a humidity sensor, a global positioning system (GPS) sensor, and any combination thereof (insignificant extra solution activity that amounts to mere data gathering).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 15:
wherein the sensor data for each sensor is associated with a time the sensor data was captured by the sensor, the method further comprising: storing the event data with the associated time in one of the one or more databases (insignificant extra solution activity that amounts to mere data gathering).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8 and 16:
retrieving one or more historical documents; segmenting each historical document into a set of clauses; transmitting the set of clauses to a client device for labeling; receiving, from the client device, a label for each of the set of clauses; jittering each clause of the set of clauses to create one or more alternate clauses, each alternate clause labeled with the same label as the jittered clause; and training the machine-learned model on the labeled clauses and alternate clauses (insignificant extra solution activity that amounts to mere data gathering).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity for reading, contracts and identifying time sensitive clauses.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6-7, 9-11, 14-15, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunn et al. [1] (US Pub. No. 20180315141) and Tran et al. (US Pub. No. 20210256070) and Engineer et al. (US Patent No. 10726374) in further view of Banerjee et al. (US Pub. No. 20190266231).
With respect to claim 1, Hunn et al. [1] teaches a computer-implemented method, comprising:
inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document, suggest appropriate operations/courses of actions to be taken based upon the state of contracts and enterprise data);
applying, using the at least one processor, at least another machine-learned model to analyze the document to identify one or more computing devices associated with the document and one or more sensors associated with the document, the one or more sensors detect sensor data associated with one or more events, each event in the one or more events is associated with event data, the sensor data is at least a portion of the event data (Paragraph 32 discloses the system and method enables contracts and contractual relationships to be analyzed using data from a variety of sources, including (but not limited to): (a) the Internet of Things (e.g. network connected devices, edge computing devices, sensors);
receiving, using the at least one processor, the one or more time-based conditions from the machine-learned model (Paragraph 70 discloses extracting the conditions);
for each time-based condition in the one or more time-based conditions:
identifying, using the at least one processor, a respective database cataloging one or more events corresponding to the time-based condition (Paragraph 73 discloses events/operations perform internally to the contract (e.g. updates to other clauses) and externally (e.g. on other systems such as BDLs, accounting systems and payment systems via APIs) may then be stored). Hunn et al. [1] does not disclose detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time.
However, Tran et al. discloses detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time (Paragraph 883 discloses providing real-time information from sensor data from various vehicle parts are integrated with blockchain to make real-time decisions and transactions involving services and payments); and
continuously determining, using the at least one processor, based on the detected event data, whether the time-based condition has been met (Paragraphs 878-879 discloses verifying completion of contractual terms using a third party computer agent. [0879] owners of IoT devices and sensors share generated IoT data in exchange for real-time micropayments).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] with Tran et al. This would have provided an improved business experience between two or more parties. See Tran et al. Paragraphs 2-6.
Hunn et al. [1] as modified by Tran et al. does not disclose inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document;
parsing, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions.
However, Engineer et al. discloses inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document (Column 17 Lines 49-61 discloses time line 602 represents a time line associated with the performance of the agreement. In this example, time line 602 represents the passage of time over the lifetime of an agreement made between two or more parties. Accordingly, critical events 604-616 represent the various critical events that may be expected to occur during the lifetime of agreement 602);
parsing, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions (Column 24 Lines 24-31 discloses during an intake phase agreement documents may be scanned or parsed to determine the critical events that may be associated with an agreement. Accordingly, in some embodiments, the critical event definitions may be stored with a document).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. with Engineer et al. This would have provided an improved business experience between two or more parties. See Engineer et al. Column 1 Lines 12-39.
Hunn et al. [1] as modified by Tran et al. and Engineer et al. does not disclose deviations of the one or more clauses to identifying the one or more time-based conditions for determining whether the amount of time in which the event needs to occur met the one or more time-based conditions, wherein the one or more deviations of the one or more clauses have one or more word association strengths that are similar to one or more word association strengths of the one or more clauses.
However, Banerjee et al. teaches
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. and Engineer et al. with Baranjee et al. This would have provided an improved business experience between two or more parties. See Banjeree et al. Paragraphs 3-9.
The Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 2, Tran et al. teaches the method of claim 1, wherein the one or more databases include information describing signature information of the document and shipping updates related to the document (Paragraphs 881-884 discloses placing a Bill of Lading on a blockchain and terms of the shipping contract are executed in code based on real-time data provided from IoT devices (Smart Agents) accompanying shipping containers. [0882] blockchain in auto supply chains. [0883] providing real-time information from sensor data from various vehicle parts are integrated with blockchain to make real-time decisions and transactions involving services and payments. [0884] recording environmental conditions during the shipment of one or more products and during). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Hunn et al. [1] reference and the Tran et al. reference is applicable to dependent claim 2.
The Hunn et al. [1] reference as modified by Tran and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 3, Hunn et al. [1] teaches the method of claim 1, further comprising:
accessing historical documents, wherein one or more portions of each historical document is labeled with an event (Paragraph 40 discloses enabling the history of a contract and/or corpus of contracts to be analyzed); and
training the machine-learned model on the labeled historical documents (Paragraph 40 discloses enabling the history of a contract and/or corpus of contracts to be analyzed).
The Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 6, Tran et al. teaches the method of claim 1, the sensors include one or more of a pressure sensor, a temperature sensor, a radio-frequency identification (RFID) sensor, an RFID tag, a light sensor, a humidity sensor, and a GPS (Paragraphs 878-881 discloses IoT devices and sensors share generated IoT data in exchange for real-time micropayments. producing energy produced by IoT energy harvester generates cryptocurrency value registered on the blockchain placing a Bill of Lading on a blockchain and terms of the shipping contract are executed in code based on real-time data provided from IoT d). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Hunn et al. [1] reference and the Tran et al. reference is applicable to dependent claim 6.
The Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 7, Hunn et al. [1] teaches the method of claim 1, wherein the sensor data for each sensor is associated with a time the sensor data was captured by the sensor (Paragraph 109 discloses relevant IoT sensor data monitoring shipping conditions, relevant data from external systems/applications (e.g. accounting data, calendar dates/event data, web services data, transaction data from payment systems, data and transactions from BDLs, relevant ERP/IMS/CRM systems data, events/operations performed on external resources, etc.), metrics around breaches of clauses and penalties, feeds of events that have occurred under a given contract or specified group of contracts, payments that have been made under contracts, notifications sent to appropriate persons as discussed above, and other relevant communications and data), the method further comprising:
storing the event information with the associated time in one of the one or more databases (Paragraph 109 discloses relevant IoT sensor data monitoring shipping conditions, relevant data from external systems/applications (e.g. accounting data, calendar dates/event data, web services data, transaction data from payment systems, data and transactions from BDLs, relevant ERP/IMS/CRM systems data, events/operations performed on external resources, etc.), metrics around breaches of clauses and penalties, feeds of events that have occurred under a given contract or specified group of contracts, payments that have been made under contracts, notifications sent to appropriate persons as discussed above, and other relevant communications and data).
With respect to claim 9, Hunn et al. [1] teaches a non-transitory computer-readable storage medium containing computer program code that, when executed by a processor, causes the processor to perform steps comprising:
input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document,
apply, using the at least one processor, at least another machine-learned model to analyze the document to identify one or more computing devices associated with the document and one or more sensors associated with the document, the one or more sensors detect sensor data associated with one or more events, each event in the one or more events is associated with event data, the sensor data is at least a portion of the event data (Paragraph 32 discloses the system and method enables contracts and contractual relationships to be analyzed using data from a variety of sources, including (but not limited to): (a) the Internet of Things (e.g. network connected devices, edge computing devices, sensors);
receiving, using the at least one processor, the one or more time-based conditions from the machine-learned model (Paragraph 70 discloses extracting the conditions);
for each time-based condition in the one or more time-based conditions:
identify, using the at least one processor, a respective database cataloging one or more events corresponding to the time-based condition (Paragraph 73 discloses events/operations perform internally to the contract (e.g. updates to other clauses) and externally (e.g. on other systems such as BDLs, accounting systems and payment systems via APIs) may then be stored). Hunn et al. [1] does not disclose detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time.
However, Tran et al. discloses detect, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time (Paragraph 883 discloses providing real-time information from sensor data from various vehicle parts are integrated with blockchain to make real-time decisions and transactions involving services and payments); and
continuously determine, using the at least one processor, based on the detected event data, whether the time-based condition has been met (Paragraphs 878-879 discloses verifying completion of contractual terms using a third party computer agent. [0879] owners of IoT devices and sensors share generated IoT data in exchange for real-time micropayments).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] with Tran et al. This would have provided an improved business experience between two or more parties. See Tran et al. Paragraphs 2-6.
Hunn et al. [1] as modified by Tran et al. does not disclose input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document;
parse, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions.
However, Engineer et al. discloses input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document (Column 17 Lines 49-61 discloses time line 602 represents a time line associated with the performance of the agreement. In this example, time line 602 represents the passage of time over the lifetime of an agreement made between two or more parties. Accordingly, critical events 604-616 represent the various critical events that may be expected to occur during the lifetime of agreement 602);
parse, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions (Column 24 Lines 24-31 discloses during an intake phase agreement documents may be scanned or parsed to determine the critical events that may be associated with an agreement. Accordingly, in some embodiments, the critical event definitions may be stored with a document).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. with Engineer et al. This would have provided an improved business experience between two or more parties. See Engineer et al. Column 1 Lines 12-39.
Hunn et al. [1] as modified by Tran et al. and Engineer et al. does not disclose
However, Banerjee et al. teaches more clauses (Paragraph 69 discloses the machine learning model 115 may be trained to recognize complex relationships between the content of a document (e.g., clauses, terms, line items, and/or the like), the attributes of the document (e.g., transaction, entity, industry, commodity, region, date, and/or the like) and Paragraph 69 discloses the machine learning model 115 may be trained to identify additional clauses, terms, and/or line items that may be relevant to the existing clauses, terms, and/or line items in the first document 145A).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. and Engineer et al. with Baranjee et al. This would have provided an improved business experience between two or more parties. See Banjeree et al. Paragraphs 3-9.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 2, because claim 10 is substantially equivalent to claim 2.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 3, because claim 11 is substantially equivalent to claim 3.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 6, because claim 14 is substantially equivalent to claim 6.
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 7, because claim 15 is substantially equivalent to claim 7.
With respect to claim 17, Hunn et al. [1] teaches a system comprising:
one or more processors (Paragraph 16 discloses processors); and
a non-transitory computer-readable storage medium (Paragraph 82 discloses a storage medium) containing computer program code that, when executed by the one or more processors, causes the one or more processors to perform steps comprising:
input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document,
apply, using the at least one processor, at least another machine-learned model to analyze the document to identify one or more computing devices associated with the document and one or more sensors associated with the document, the one or more sensors detect sensor data associated with one or more events, each event in the one or more events is associated with event data, the sensor data is at least a portion of the event data (Paragraph 32 discloses the system and method enables contracts and contractual relationships to be analyzed using data from a variety of sources, including (but not limited to): (a) the Internet of Things (e.g. network connected devices, edge computing devices, sensors);
receiving, using the at least one processor, the one or more time-based conditions from the machine-learned model (Paragraph 70 discloses extracting the conditions);
for each time-based condition in the one or more time-based conditions:
identify, using the at least one processor, a respective database cataloging one or more events corresponding to the time-based condition (Paragraph 73 discloses events/operations perform internally to the contract (e.g. updates to other clauses) and externally (e.g. on other systems such as BDLs, accounting systems and payment systems via APIs) may then be stored). Hunn et al. [1] does not disclose detecting, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time.
However, Tran et al. discloses detect, using the at least one processor, the event data associated with at least one event in the one or more events, the event data is continuously received from the one or more identified computing devices and the one or more sensors in real time (Paragraph 883 discloses providing real-time information from sensor data from various vehicle parts are integrated with blockchain to make real-time decisions and transactions involving services and payments); and
continuously determine, using the at least one processor, based on the detected event data, whether the time-based condition has been met (Paragraphs 878-879 discloses verifying completion of contractual terms using a third party computer agent. [0879] owners of IoT devices and sensors share generated IoT data in exchange for real-time micropayments).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] with Tran et al. This would have provided an improved business experience between two or more parties. See Tran et al. Paragraphs 2-6.
Hunn et al. [1] as modified by Tran et al. does not disclose input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document;
parse, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions.
However, Engineer et al. discloses input, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document (Column 17 Lines 49-61 discloses time line 602 represents a time line associated with the performance of the agreement. In this example, time line 602 represents the passage of time over the lifetime of an agreement made between two or more parties. Accordingly, critical events 604-616 represent the various critical events that may be expected to occur during the lifetime of agreement 602);
parse, using the at least one processor, the document to extract one or more actions in response the determining whether the time-based condition has been met, and triggering execution of the one or more actions (Column 24 Lines 24-31 discloses during an intake phase agreement documents may be scanned or parsed to determine the critical events that may be associated with an agreement. Accordingly, in some embodiments, the critical event definitions may be stored with a document).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. with Engineer et al. This would have provided an improved business experience between two or more parties. See Engineer et al. Column 1 Lines 12-39.
Hunn et al. [1] as modified by Tran et al. and Engineer et al. does not disclose inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document, the machine-learned model has been trained using one or more clauses of the document and one or more deviations of the one or more clauses to identifying the one or more time-based conditions for determining whether the amount of time in which the event needs to occur met the one or more time-based conditions, wherein the one or more deviations of the one or more clauses have one or more word association strengths that are similar to one or more word association strengths of the one or more clauses.
However, Banerjee et al. teaches inputting, using at least one processor, an executed document to a trained machine-learned model, and analyzing, using the machine-learned model, the document to identify one or more time-based conditions indicated in the document, wherein each time-based condition in the one or more time-based conditions defines an amount of time in which an event needs to occur based on the document, the machine-learned model has been trained using one or more clauses of the document and one or more deviations of the one or more clauses to identifying the one or more time-based conditions for determining whether the amount of time in which the event needs to occur met the one or more time-based conditions, wherein the one or more deviations of the one or more clauses have one or more word association strengths that are similar to one or more word association strengths of the one or more clauses (Paragraph 69 discloses the machine learning model 115 may be trained to recognize complex relationships between the content of a document (e.g., clauses, terms, line items, and/or the like), the attributes of the document (e.g., transaction, entity, industry, commodity, region, date, and/or the like) and Paragraph 69 discloses the machine learning model 115 may be trained to identify additional clauses, terms, and/or line items that may be relevant to the existing clauses, terms, and/or line items in the first document 145A).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. and Engineer et al. with Baranjee et al. This would have provided an improved business experience between two or more parties. See Banjeree et al. Paragraphs 3-9.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 2, because claim 18 is substantially equivalent to claim 2.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 3, because claim 19 is substantially equivalent to claim 3.
Claim(s) 5, 8, 13, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunn et al. [1] (US Pub. No. 20180315141) and Tran et al. (US Pub. No. 20210256070) and Engineer et al. (US Patent No. 10726374) and Banerjee et al. (US Pub. No. 20190266231) in further view of Hunn et al. [2] (US Pub. No. 20170287090).
The Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 5, Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. does not disclose comparing the sensor data to a threshold.
However, Hunn et al. [2] teaches the method of claim 1, wherein determining based on the event information whether the time-based condition has been met comprises:
comparing the sensor data to a threshold (Paragraph 161 discloses the contract rule may extend the warranty from 12 months to 24 months if the performance/quality and industry benchmark data cross certain thresholds indicate a higher level of poor absolute and relative performance); and
responsive to determining the sensor data misaligns with the threshold, determining that the time-based condition has been met (Paragraph 161 discloses the contract rule may extend the warranty from 12 months to 24 months if the performance/quality and industry benchmark data cross certain thresholds indicate a higher level of poor absolute and relative performance).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Hunn et al. [1] and Tran et al. and Engineer et al. and Baranjee et al. with Hunn et al. [2]. This would have provided an improved business experience between two or more parties. See Hunn et al. [2] Column 1 Lines 12-39.
The Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. teaches all the limitations of claim 1. With respect to claim 8, Hunn et al. [1] reference as modified by Tran et al. and Engineer et al. and Banerjee et al. does not disclose comparing the sensor data to a threshold.
However, Hunn et al. [2] teaches the method of claim 1, further comprising:
retrieving one or more historical documents (Paragraph 107 discloses current and historical data is needed);
segmenting each historical document into a set of clauses (Paragraph 114 discloses The augmentation of one or more clause may be based on predefined conditions or dynamically detected conditions (e.g. from an event streaming engine));
transmitting the set of clauses to a client device for labeling by an external operator (Paragraph 114 discloses updating the contract or individual clauses);
receiving, from the client device, a label for each of the set of clauses (Paragraph 70 discloses programmable clauses, which can enable the terms and conditions of the data-driven contract to update and change in real time in response to data and external events after the contract is formed);
jittering each clause of the set of clauses to create one or more alternate clauses, each alternate clause labeled with the same label as the jittered clause (Paragraph 70 discloses programmable clauses, which can enable the terms and conditions of the data-driven contract to update and change in real time in response to data and external events after the contract is formed); and
training the machine-learned model on the labeled clauses and alternate clauses (Paragraph 112 discloses Complex Event Processing (‘CEP’) that enable real-time intelligence to streaming data, making it easy to identify complex sequences of atomi