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 .
This Office Action is in response to Application 19/040,483 filed on 01/29/2025.
Claims 1-20 are currently pending and examined below.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a nature phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-20 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Step 2A Prong One:
Claim 1 recites (additional elements underlined):
A method for automatic generation of requests for information, the method comprising:
receiving an incident report;
extracting context information associated with the incident report, wherein the context information comprises a set of attributes associated with an incident;
generating a set of contacts and a set of messages to be sent to the set of contacts based at least in part on the context information and an identity of a requesting user;
transmitting the set of messages to the set of contacts; and
generating and outputting, responsive to receiving information from the set of contacts, a context specific report containing the information received from the set of contacts.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements identified below, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgement, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements identified above do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth the managing of personal behavior or relationships or interactions between people (e.g., managing interactions between the requesting user and the set of contacts to obtain information). Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
Step 2A Prong Two:
In Step 2A Prong Two, the additional element(s) outlined above are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. The Examiner notes that “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent eligible subject matter" (see pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., in a computer environment). Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation (Step 2A Prong Two, No).
Step 2B:
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A Prong Two overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Their collective functions merely provide generic computer implementation (Step 2B, No).
Claims 2-12 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claims 2, 4-5, and 10-11 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claims 2, 4-5, and 10-11 also do not integrate the judicial exception into a practical application or amount to significantly more.
Claim 3 recites the additional element “performing optical character recognition (OCR). However, in Step 2A Prong Two, this additional element also does not integrate the judicial exception into a practical application because it amounts to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, adding insignificant extra-solution activity, and generally linking the use of the judicial exception to a particular technological environment or field of use. In Step 2B, this additional elements also does not amount to significantly more because it amounts to simply appending well-understood, routine, and conventional activity as evidenced by at least MPEP 2106.05(d)(II) (e.g., electronically scanning or extracting data from a physical document).
Claim 6 recites the additional elements “using a machine learning model”. Claim 7 recites the additional element “wherein the machine learning model is trained”. Claim 8 recites the additional element “wherein the machine learning model is configured to”. Claim 9 recites the additional element “wherein the machine learning model”. Claim 12 recites the additional elements “on a graphical user interface (GUI) of a computer system”, “using a processor of the computer system”, “automatically”, “in the GUI to”, and “within the GUI”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 13 recites substantially similar limitations as claim 1. Therefore, for the same reasons explained above with respect to claim 1, claim 13 also recites an abstract idea in Step 2A Prong One (i.e., certain method of organizing human activities, and mental processes). Claim 13 recites the additional elements “A system comprising: one or more processors; a memory coupled to the one or more processors, the memory including instructions that, when executed by the one or more processors, cause the processors to”. However, for the same reasons explained above with respect to claim 1, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more.
Claims 14-19 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 13 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 14 recites the additional element “performing optical character recognition (OCR). However, in Step 2A Prong Two, this additional element also does not integrate the judicial exception into a practical application because it amounts to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, adding insignificant extra-solution activity, and generally linking the use of the judicial exception to a particular technological environment or field of use. In Step 2B, this additional elements also does not amount to significantly more because it amounts to simply appending well-understood, routine, and conventional activity as evidenced by at least MPEP 2106.05(d)(II) (e.g., electronically scanning or extracting data from a physical document).
Claims 15-16 recite the additional element “wherein the processor is further configured to”. Claim 17 recites the additional element “using a machine learning model” and ‘wherein the machine learning model is trained to”. Claim 18 recites the additional element “wherein the machine learning model is configured to”. Claim 19 recites the additional elements “on a graphical user interface (GUI) of the system, and wherein the processor is further configured to”, “automatically”, “in the GUI to”, and “within the GUI”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 20 recites substantially similar limitations as claim 1. Therefore, for the same reasons explained above with respect to claim 1, claim 20 also recites an abstract idea in Step 2A Prong One (i.e., certain method of organizing human activities, and mental processes). Claim 20 recites the additional elements “A non-transitory computer-readable medium embodying program code tht is executable by one or more processors to cause the one or more processors to”. However, for the same reasons explained above with respect to claim 1, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more.
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, 5, 10-11, 13, 16, and 20 is/are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Connell et al. (US 2013/0254133 A1, hereinafter “Connell”).
As per Claim 1, Connell discloses A method for automatic generation of requests for information, the method comprising (¶ 6 “As part of a system or method for secure evidence sharing between providers and recipients of evidentiary documentation, identification information associated with an interested party may be received.” Claim 18 “A non-transitory computer-readable storage medium”. ¶ 82 “processors … memory”. Also see Figure 14.):
receiving an incident report (Examiner Note: ¶ 46 of the published speciation states “the incident report may be received directly from a user.” ¶ 54 of Connell discloses receiving an incident report directly from the user: “FIG. 4 illustrates an example of a graphical user interface 400 that is configured to allow a user to submit an evidence request. It should be understood that this example is illustrative in nature and is not meant to limit the invention. In one example, a user, may navigate via an internet browser to a web site similar to the one depicted in FIG. 4. The user may then select an incident date 402, a law enforcement agency 404, and a case or incident number 406. From a drop down menu 408, the user may select at least one type of report the user may be interested in receiving. The type of reports may include, but are not limited to, police reports, photographs, audio statements, in-car and on-person video. The user may then enter their first name 410, last name 412 and email address 414. The user may then indicate what type of user they are, for instance, a party involved in the incident 416, or an attorney representing a party to the incident 416. The selections available for type of user may correspond to permitted requestors as defined by appropriate open records legislation. Consider the situation in which the user selects to indicate that they are a party involved in the incident by selecting button 418, the user may then select other parties to notify. The user might select the relationship of the additional party by selecting an option from a drop down menu 420. Additionally, the user may enter in the additional party's email address in box 422. Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.” Also see at least Figure 4.);
extracting context information associated with the incident report, wherein the context information comprises a set of attributes associated with an incident (¶ 54 “FIG. 4 illustrates an example of a graphical user interface 400 that is configured to allow a user to submit an evidence request. It should be understood that this example is illustrative in nature and is not meant to limit the invention. In one example, a user, may navigate via an internet browser to a web site similar to the one depicted in FIG. 4. The user may then select an incident date 402, a law enforcement agency 404, and a case or incident number 406 [i.e., set of attributes associated with the incident]. From a drop down menu 408, the user may select at least one type of report the user may be interested in receiving. The type of reports may include, but are not limited to, police reports, photographs, audio statements, in-car and on-person video. The user may then enter their first name 410, last name 412 and email address 414. The user may then indicate what type of user they are, for instance, a party involved in the incident 416, or an attorney representing a party to the incident 416 [i.e., set of attributes associated with the incident]. The selections available for type of user may correspond to permitted requestors as defined by appropriate open records legislation. Consider the situation in which the user selects to indicate that they are a party involved in the incident by selecting button 418 [i.e., set of attributes associated with the incident], the user may then select other parties to notify. The user might select the relationship of the additional party by selecting an option from a drop down menu 420. Additionally, the user may enter in the additional party's email address in box 422. Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.” Also see at least Figure 4.);
generating a set of contacts and a set of messages to be sent to the set of contacts based at least in part on the context information and an identity of a requesting user (¶ 54 “Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404.” ¶ 57 “In accordance with at least one embodiment, the user, in a manner similar to that described above, may request evidence that may be under the control of multiple providers [i.e., a set of contacts]. In one example, the system 114 may parse the evidence request and generate multiple open records requests, each such open records request corresponding to a particular provider of the requested evidentiary documentation. For instance, if the user requested both an accident report and a 911 recording via box 408, the system 114 may parse the resulting evidence request and generate an open records request for each of the accident report and the 911 recording [i.e., generating a set of contacts and a set of messages to be sent to the set of contacts based at least in part on the context information and identity of a requesting user].” ¶¶ 31-32 and 48 explain that the evidentiary documentation is only for legally interested parties only. Therefore, the step of generating the set of contacts and messages to be set to the contacts is based on the context information extracted from the incident report, and the identity of the requesting user. Also see at least Figures 4 and 6-7.);
transmitting the set of messages to the set of contacts (¶ 30 “The system 114 may generate, for each corresponding evidence request, an open records request that the system 114 may then send to a law enforcement agency to be processed. An open records request may be a letter, an email or a fax that complies with open records legislation for the appropriate/selected agency in the appropriate/selected jurisdiction.” ¶ 54 “Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.” ¶ 57 “In accordance with at least one embodiment, the user, in a manner similar to that described above, may request evidence that may be under the control of multiple providers. In one example, the system 114 may parse the evidence request and generate multiple open records requests, each such open records request corresponding to a particular provider of the requested evidentiary documentation. For instance, if the user requested both an accident report and a 911 recording via box 408, the system 114 may parse the resulting evidence request and generate an open records request for each of the accident report and the 911 recording.”); and
generating and outputting, responsive to receiving information from the set of contacts, a context specific report containing the information received from the set of contacts (¶ 35 “Once a provider 116 has registered with the secure evidence sharing engine 114 the provider 116 may upload evidentiary documentation. Upon receipt of the uploaded evidentiary documentation, the secure evidence sharing engine 114 may store the documentation in a data store used to store such information. Additionally, the secure evidence sharing engine 114 may identify legally interested parties. The secure evidence sharing engine 114 may further determine parties to notify based on the previous indication by a legally interested party, that an additional party should be notified. Once parties are determined and/or identified, the secure evidence sharing engine may then notify a potential recipient 128. This notification may come in multiple forms. For example, the notification could be achieved by, but is not limited to, text messaging, emailing, postal mail, automated phone messaging, or by a social networking status entry such as a "tweet" via Twitter.RTM.. The notification may include information indicating to the recipient that evidentiary documentation is available for their viewing. Once a recipient 128 has received notification that evidentiary documentation is available for viewing, they may choose to access the web site designed to be the graphical user interface for the secure evidence sharing engine 114 to pay for or obtain for free, the evidentiary documentation available.” ¶ 62 “In accordance with at least one embodiment, once the case file is established, individual pieces of evidence may be uploaded into the case file by the provider, or a checklist of evidence currently available, or expected to be available in the future may be established. A default list of customarily available evidence can also be generated by the system in lieu of a specific list of evidence. The list of available or potentially available evidence can then be shared by the system with recipients so they can decide which evidence they would like providers to upload. Providers may choose to refrain from uploading certain evidence unless ordered by a recipient in advance a revenue stream is generated.” ¶ 80 “Some time later, a notified party may access the web site, seeking to access the evidentiary documentation at step 1218. The secure evidence sharing engine may determine whether or not the notified party is registered at step 1220. If the notified party is not registered, the notified party may be prompted to go through the registration process at step 1222. If the notified party is registered, the payment processing engine, a component of the secure evidence sharing engine, may determine if a fee is required at step 1224. If a fee is required, the notified party may be prompted for payment at step 1226. Once payment is received at step 1228, the notified party may access the evidentiary documentation at step 1230. If the payment processing engine determines that no fee is required, the notified party may not be prompted for payment and may be immediately enabled to access the evidentiary documentation at step 1230.”).
As per Claim 2, Connell discloses wherein accessing the context information comprises extracting text data from the incident report (¶ 54 “Once the user selects the submit button 424, the information entered [i.e., text data]may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.” Also see at least Figure 4.).
As per Claim 5, Connell discloses further comprising:
generating and outputting a set of additional messages, wherein the set of additional messages provide an indication that certain information was not received (The Examiner notes that the italicized and underlined limitation is not given patentable weight because it merely describes the intended use of the additional messages. However, for the sake of advancing prosecution, see at least ¶ ¶ 56 “The open records request sent to the law enforcement agency 404 may be associated with one or more time periods within which a response to the open records request is expected and/or required. For example, the one or more time periods may be specified in an open records law that governs the open records request. Such time periods may include a time period for acknowledging receipt of the open records request and a time period for fulfilling the open records request (e.g., providing the requested records to the requestor). The system 114 may resend the open records request, or a notification, such as a reminder, referring to the open records request, to the law enforcement agency 404 periodically until an appropriate response to the open records request is received. These notifications or retransmitted open records requests may indicate specific mandated open record deadlines, for example, as determined by open records legislation associated with the providing agency.”).
As per Claim 10, Connell discloses wherein the identity of the requesting user comprises an attorney, a police officer, a civilian, or a combination thereof, and wherein the identity is determined by extracting the identity from a profile associated with the requesting user (¶ 27 “In accordance with at least one embodiment, the legally interested party (i.e. a private party, an insurance agency, an eyewitness, a government agency, etc.) may register with the secure evidence sharing system. Registration may include an acceptance of terms and conditions and/or a possible fee payment. The registration may occur before or after the incident involving law enforcement. Once registered, the legally interested party may be enabled to receive notifications when future incidents transpire and/or the party may receive notifications for past events for which evidence is available.” ¶ 49 “FIG. 3 illustrates an example of a graphical user interface 300 that is configured to collect new user registration information. It should be understood that this example is illustrative in nature and is not meant to limit the invention. In one example, a user who wishes to register with the secure evidence sharing engine 301 might navigate via an internet browser to a web site similar to the one depicted in FIG. 3. The secure evidence sharing engine 301 is an example of the secure evidence sharing engine 114 of FIG. 1. The user may then enter in registration information which may include a first name 302, a last name 304, a company associated with 306, a first line of their contact address 308, a possible second line of their contact address 310, a city corresponding to the contact address 312, a state 314, a zip code 316, a phone number 318, a cell number 320, a fax number 322, an email address 324, or a combination thereof. A profile may be created and a password may be established for secure access.” ¶ 54 “FIG. 4 illustrates an example of a graphical user interface 400 that is configured to allow a user to submit an evidence request. It should be understood that this example is illustrative in nature and is not meant to limit the invention. In one example, a user, may navigate via an internet browser to a web site similar to the one depicted in FIG. 4. The user may then select an incident date 402, a law enforcement agency 404, and a case or incident number 406. From a drop down menu 408, the user may select at least one type of report the user may be interested in receiving. The type of reports may include, but are not limited to, police reports, photographs, audio statements, in-car and on-person video. The user may then enter their first name 410, last name 412 and email address 414. The user may then indicate what type of user they are, for instance, a party involved in the incident 416, or an attorney representing a party to the incident 416. The selections available for type of user may correspond to permitted requestors as defined by appropriate open records legislation. Consider the situation in which the user selects to indicate that they are a party involved in the incident by selecting button 418, the user may then select other parties to notify. The user might select the relationship of the additional party by selecting an option from a drop down menu 420. Additionally, the user may enter in the additional party's email address in box 422. Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.”).
As per Claim 11, Connell discloses wherein the set of attributes comprises an agency case number, a county and state, a time associated with the incident, a precise location of the incident based on global positioning coordinates, a name of a road, a plurality of party names involved in the incident, or any combination thereof (¶ 54 and Figure 4).
As per Claims 13 and 20, they recite substantially similar limitations as claim 1. Therefore, claims 13 and 20 are rejected using the same rationale.
As per Claim 16, it recites substantially similar limitations as claim 5. Therefore, claim 16 is rejected using the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3-4 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Connell in view of Jones et al. (US 2023/0419413 A1, hereinafter “Jones”).
As per Claim 3, Connell discloses wherein extracting text data from the incident report … on the incident report (¶ 54 “Once the user selects the submit button 424, the information entered [i.e., text data]may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404. In accordance with at least one embodiment, submission of an evidence request may generate a legally compliant open records request that is automatically transmitted to the law enforcement agency.” Also see at least Figure 4.).
While Connell extracts text data from the incident report, Connell does not appear to explicitly disclose … comprises performing optical character recognition (OCR) ….
However, Jones teaches … comprises performing optical character recognition (OCR) … (¶ 51 “Incident data received according to one or more steps outlined above may be further processed, subsequent to and/or in connection with the presentation of data collection requests and incident data collection. For instance, the program may be configured to utilize optical character recognition (OCR) and/or object recognition software to analyze images for additional incident data. In an embodiment, the program may be configured to direct the user to capture images of one or more third-party driver documents via the camera of the user's mobile electronic device and may execute OCR software to recognize text on the images for storage and/or inclusion in a structured incident data report or the like. More particularly, the program may be configured to request and receive images of an insurance card, driver's license, VIN, and/or license plate, and utilize OCR to determine phone number, legal name, driver's license number, state of licensure, license plate number, insurance company, policy number and/or other information from the image(s).” Also see at least ¶¶ 33, 71, 99, and 126.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine OCR for recognizing text as taught by Jones, into Connel. One of ordinary skill in the art would have be motivated to do so for storage and/or inclusion of the recognized text in a structured incident data report, and for creating additional incident data (Jones, ¶¶ 51 and 99). One of ordinary skill in the art would have also be motivated to do so in order to quickly and efficiently determine phone numbers, legal names, driver’s license numbers, state of licensure, license plate number, insurance company, policy numbers, and other information from images by utilizing OCR (Jones,¶ 51). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 4, Connell discloses wherein the method further comprises:
wherein the set of contacts are associated with a plurality of entities located within a predetermined distance from the precise location, within a defined jurisdictional boundary associated with the incident, or a combination thereof (¶ 45 “In accordance with at least one embodiment, the secure evidence sharing engine 201 may contain a party identification engine 220 that may be responsible for identifying providers of requested evidentiary documentation and/or identifying legally interested parties upon receipt of evidentiary documentation. The party identification engine 220 is an example of a party identification component. For example, the party identification engine 220 may parse received requests for evidentiary documentation to identify one or more types of requested evidentiary documentation and then identifying one or more corresponding providers of the requested evidentiary documentation based at least in part on the identified one or more types of the requested evidentiary documentation. The party identification engine 220 may utilize any suitable information related to requested evidentiary documentation to identify corresponding providers including a related legal jurisdiction and/or geographic location. As another example, upon receipt of evidentiary documentation, the party identification engine 220 may parse the identification information from the evidentiary documentation and create a tuple of multiple identifiers to be used later for lookup of documents pertaining to a particular person or object. The multiple identifiers correspond to identifiers associated with a person or object. Once the tuple has been created, the party identification engine 220 may pass the data conversion engine 222. Additionally, the party identification engine 220 may store contact information associated with additional parties to be notified, for example, as indicated by a submitted evidence request, in an evidence request data store 223. Identifying information associated with the additional parties may be at least, a request identifier, incident identifier, relationship to original party, and email address, to name a few. A set of interested parties, or the like, is identified for each incident of evidentiary documentation. Uploads of related information, in any suitable context, can trigger notifications to the identified set of interested parties.”).
While Connell extracts various information from the incident report, Connell does not appear to explicitly disclose extracting from the incident report a precise location of an accident, the precise location comprising a latitude value and a longitude value.
However, Jones teaches extracting from the incident report a precise location of an accident, the precise location comprising a latitude value and a longitude value (¶ 33 “The incident data may be information about one or more third-party drivers—if a third party was involved—such as name, phone number, insurance company, policy number, license plate, driver's license number, state that issued the driver's license, images of official documents (e.g., insurance card, driver's license, license plate), witnesses, witness names, witness phone numbers, witness statements, audio files and/or text files (generated using talk-to-text software) of witness statements, images of the user's car and images of any other car involved, makes of the relevant cars, models of the relevant cars, year of manufacture of the relevant cars, images of the incident scene, metadata generated from the images (such as location and time of the incident), data related to weather during the accident, live data feeds (such as weather, temperature, speed, GPS coordinates, etc.), and the location, date, and time of the incident, etc.” ¶ 53 “Moreover, the program may be configured to generate metadata from photographs of the accident scene and/or of relevant objects, for example to automatically determine the location and time of the incident, the weather during or soon after the accident, or to otherwise generate information that may be relevant to processing of insurance claims.” ).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine the step of extracting from the incident report a precise location of an accident, the precise location comprising a latitude value and a longitude value as taught by Jones, into Connell. One of ordinary skill in the art would have been motivated to do so in order to verify the validity of an incident, and to automatically determine the location and time of an incident (Jones, ¶¶ 49, 53, and 101). One of ordinary skill in the art would have also been motivated to do so in order to identify discrepancies and/or firm up conclusions regarding conditions during the accident (Jones, ¶ 53). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 14, it recites substantially similar limitations as claims 2-3. Therefore, claim 14 is rejected using the same rationale.
As per Claim 15, it recites substantially similar limitations as claim 4. Therefore, claim 15 is rejected using the same rationale.
Claim(s) 6-9 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Connell in view of Goswami et al. (US 2024/0037464 A1, hereinafter “Goswami”).
As per Claim 6, Connell discloses wherein the set of contacts and the set of messages are generated … (¶ 54 “Once the user selects the submit button 424, the information entered may be processed by the system and an open records request may be generated and submitted to the indicated law enforcement agency 404.”).
While Connell generates the set of contacts and the set of messages, Connell does not appear to explicitly do so … using a machine learning model.
However, Goswami teaches a set of contacts and a set of messages being generated … using a machine learning model (¶¶ 120-124 and 160. Also see at least ¶¶ 5, 7, and 118).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine the use of the machine learning model as taught by Goswami, into Connell. One of ordinary skill in the art would have been motivated to do so in order to provide more accurate and improved contact recommendations (Goswami, ¶¶ 122-123). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 7, while Connell generates the set of contacts, and resends the open requests periodically until an appropriate response to the open request is received, Connell does not appear to explicitly disclose wherein the machine learning model is trained to generate the set of contacts based on relative success rates that the set of contacts have responded to messages previously.
However, Goswami teaches wherein the machine learning model is trained to generate the set of contacts based on relative success rates that the set of contacts have responded to messages previously (¶ 38 “A responder is responsible for responding to one or more notification events.” ¶ 120 “the responder recommendation tool 418 may also be referred to as a machine-learning model recommendation engine.” ¶ 122 “The responder recommendation tool 418 [i.e., machine learning model] uses a historical collection of incidents stored in the data store 410, causes associated with the incidents, skills identified as required for (or at least helpful in) resolving the incidents. Each of the incidents in the data store 410 may be associated with a respective type. Such data are paired with a historical collection of responders who have previously (e.g., in the past) responded to the incidents [i.e., is trained to generate a set of contacts based on relative success rates that the set of contacts have responded to messages previously] (e.g., responded to incidents having certain types). Given an incident as input, the responder recommendation tool 418 outputs recommend responders or teams or responders [i.e., machine learning model is trained to generate a set of contacts] that may be called upon (such as by associating one or more of the recommend responders with the incident) by the assigned responder to at least assist in resolving the incident.” ¶ 123 “The responder recommendation tool 418 can be regularly retrained using additional data (which are described above) so that improved responder recommendations are obtained. Refining the responder recommendations includes that, over time, and as more responders are associated with successful resolution of incidents, the system 400 learns to provide more accurate responder recommendations [i.e., the machine learning model is trained to generate the set of contacts based on relative success rates that the set of contacts have responded to messages previously].” Also see at least ¶¶ 124, 158, 160, 162-163, and 185.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine the use of the machine learning model as taught by Goswami, into Connell. One of ordinary skill in the art would have been motivated to do so in order to provide more accurate and improved contact recommendations (Goswami, ¶¶ 122-123). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 8, Connell discloses analyze the incident report to identity a plurality of entities associated with the incident report (¶¶ 6, 54, and Figure 4).
Connell does not appear to explicitly disclose wherein the machine learning model is configured to: assign numerical weights to each entity of the plurality of entities, wherein the numerical weights are assigned based on a likelihood that each entity will respond to the set of messages, based on a relevancy of the information held by each entity, or a combination thereof.
However, Goswami teaches wherein the machine learning model is configured to (¶¶ 120-124 and 160. Also see at least ¶¶ 5, 7, 118, and 185):
assign numerical weights to each entity of the plurality of entities, wherein the numerical weights are assigned based on a likelihood that each entity will respond to the set of messages, based on a relevancy of the information held by each entity, or a combination thereof (¶¶ 122, 160, 170, 175, and 185. Also see at least ¶¶ 5, 7, 118-124).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine the use of the machine learning model and the assigning of numerical weights to each entity of the plurality of entities wherein the numerical weights are assigned based on a likelihood that each entity will respond to the set of messages, based on a relevancy of the information held by each entity, or a combination thereof as taught by Goswami, into Connell. One of ordinary skill in the art would have been motivated to do so in order to provide more accurate and improved contact recommendations (Goswami, ¶¶ 122-123 and 170). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 9, Connell discloses does not appear to explicitly disclose wherein the machine learning model prioritizes sending the set of messages to entities with higher numerical weights by: analyzing the numerical weight for each entity to generate a priority list having a descending order based on the numerical weights; evaluating a respective numerical weight of an entity against a threshold; and responsive to determining the threshold is satisfied, transmitting a message requesting information to the entity.
However, Goswami teaches wherein the machine learning model prioritizes sending the set of messages to entities with higher numerical weights by (¶¶ 160, 170, and 185. Also see at least ¶¶ 118-124, and 175):
analyzing the numerical weight for each entity to generate a priority list having a descending order based on the numerical weights (¶¶ 160, 170, and 185. Also see at least ¶¶ 118-124, and 175);
evaluating a respective numerical weight of an entity against a threshold (¶¶ 160, 170, and 185. Also see at least ¶¶ 118-124, and 175); and
responsive to determining the threshold is satisfied, transmitting a message requesting information to the entity (The Examiner notes that this limitations is a contingent limitation that is not required to be performed if the threshold is not satisfied. However, for the sake of advancing prosecution, see at least ¶¶ 160, 170, and 185. Also see at least ¶¶ 118-124, and 175).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine wherein the machine learning model prioritizes sending the set of messages to entities with higher numerical weights by analyzing of the numerical weights to generate a priority list having a descending order based on the numerical weighs and evaluating a respective numerical weight of an entity against a threshold as taught by Goswami, into Connell. One of ordinary skill in the art would have been motivated to do so in order to provide more accurate and improved contact recommendations (Goswami, ¶¶ 122-123 and 170). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 17, it recites substantially similar limitations as claims 6-7. Therefore, claim 17 is rejected using the same rationale.
As per Claim 18, it recites substantially similar limitations as claim 8-9. Therefore, claim 18 is rejected using the same rationale.
Claim(s) 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Connell in view of Shadpour et al. (US 2021/0064200 A1, hereinafter “Shadpour”).
As per Claim 12, Connell discloses wherein the context specific report is displayed on a graphical user interface (GUI) of a computer system, and wherein the method further comprises (¶ 36, 55, and 80):
…user selection of the information contained within the context specific report … of the context specific report (¶ 65 and Figure 8).
While Connell disclose the context specific report being displayed on a graphical user interface of a computer system, Connell does not appear to explicitly disclose tracking, using a processor of the computer system … to determine an amount of use of each information …; and automatically rearranging the information in the GUI to display the most used information to an updated position within the GUI.
However, Shadpour teaches tracking, using a processor of the computer system … to determine an amount of use of each information … (Abstract, and ¶¶ 3 and 17); and
automatically rearranging the information in the GUI to display the most used information to an updated position within the GUI (Abstract, and ¶¶ 3 and 17).
Shadpour suggests that it is advantageous to move the most used information to a position on the GUI closest to the beginning based on a determined amount of use so that the information can be easily accessed (Shadpour ¶ 17).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to combine the steps of tracking an amount of use of each information and automatically rearranging the information in the GUI to display the most used information to an updated position within the GUI as taught by Shadpour, into Connell. One of ordinary skill in the art would have been motivated to do so that the information can be easily accessed (Shadpour, ¶ 17). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 19, it recites substantially similar limitations as claim 12. Therefore, claim 19 is rejected using the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Weinrauch et al. (US 2012/0109690 A1) discloses a system and method for rapidly exchange accident related information. Data locator codes representing people and respective vehicles the people are insured to drive are generated and stored by central server. Software is installed on mobile devices that interface with the central server, and the software is configured with at least one of the data locator codes. A notification regarding an accident event involving two parties is received via the software. The central server receives accident information, which may include a data locator code the other driver, as well as images, voice messages or text information representing the accident. The central server uses the information to contact respective insurers of the parties, processes the data, and the parties can depart the scene without a need for police or other emergency care personnel.
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/SAM REFAI/Primary Examiner, Art Unit 3621