DETAILED ACTION
Acknowledgement
This non-final office action is in response to the request for continued examination (RCE) filed on 03/02/2026.
Status of Claims
Claims 1, 3, 15, 17, 29, and 31 have been amended.
Claims 1, 3-15, 17-29 and 31-42 are now pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
Response to Arguments
The double patenting rejection is withdrawn in light of the present claim amendments being distinct from claims in co-pending application 18/312,892.
The 35 U.S.C. 112(b) rejection is withdrawn in light of amendments.
Applicant's arguments filed on 03/02/2026 regarding the 35 U.S.C. 101 and 103 rejections of claims 1, 3-15, 17-29 and 31-42 have been fully considered. The Applicant argues the following.
(1) As per the 101 rejection, the Applicant argues that amended independent claims 1, 15, and 29 recite a practical application of the alleged abstract idea and significantly more than the alleged abstract idea.
The Examiner respectfully disagrees. The Examiner maintains the position that additional elements recited in the claims and listed in steps 2A(2) and 2B do not integrate the abstract idea into a practical application or provide significantly more than the abstract idea because these additional elements do not improve the functioning of a computer beyond its original capacity or improve upon another technology or technological component/system. The amended limitation of “…automatically executing a machine-executable script associated with each assigned distributed actor without human intervention, wherein each assigned distributed actor is generated to include a respective machine-executable script that is machine- interpretable and one or more natural language descriptions of the functions performed by the assigned distributed actor that are human-interpretable” is considered an additional element. However, executing machine-executable scripts are considered mere instructions to implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The Applicant has not provided details on how the machine-executable scripts improves upon technology or provides significantly more. Therefore, the 35 U.S.C. 101 rejection is maintained.
(2) As per the 103 rejection, the Applicant argues that the combination of Horvitz and Grenader do not teach, disclose, or even suggest the amended limitations of claims 1, 15, and 29.
The Examiner finds the Applicant’s arguments persuasive. Therefore, the previous 103 rejection has been withdrawn. However, upon further search and consideration, a new ground of 103 rejection for claims 1, 15, and 29 is made. See details below.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 12/08/25, 03/13/26, 03/20/26, 03/25/26, and 04/07/26 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1, 3-15, 17-29 and 31-42 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 15, and 29 include the limitation of “performing the at least one skill offered by the one or more assigned distributed actors to address the unfulfilled need by automatically executing a machine-executable script associated with each assigned distributed actor without human intervention, wherein each assigned distributed actor is generated to include a respective machine-executable script that is machine- interpretable and one or more natural language descriptions of the functions performed by the assigned distributed actor that are human-interpretable”. This limitation is not supported in the Applicant’s specification in paragraphs [0003] and [0098] or elsewhere in the specification. The specification connects and/or associates machine-executable scripts, machine-interpretable, and natural language descriptions of functions to the website 100. The specification does not associate these features with each (i.e. all) of the distributed actors 950, 952, 954, 956, 958, 960, 962, 964, 966, 968. Therefore, claims 1, 15, and 29 contain new matter and are rejected under 35 U.S.C. 112(a). Dependent claims 3-14, 17-28, and 31-42 are also rejected under 35 U.S.C. 112(a).
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, 3-15, 17-29, and 31-42 are rejected under 35 U.S.C. 101 because the claimed invention, “Distributed Actor-Based Information System & Method”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1, 3-15, 17-29, and 31-42 are directed to a statutory category, namely a process (claims 1 and 3-14), a manufacture (claims 15 and 17-28), and a machine (claims 29 and 31-42).
Step 2A (1): Claims 1, 3-5, 7-15, 17-19, 21-29, 31-33, and 35-42 are directed to an abstract idea of Mental Processes and Certain Methods of Organizing Human Activity, based on the following claim limitations: maintaining a group of distributed actors…that perform a respective skill…; maintaining a quality-of-service statistics for each of the group of distributed actors; monitoring an environment to detect the existence of an unfulfilled need; assigning one or more distributed actors to address the unfulfilled need based, at least in part, upon the quality-of-service statistics and the at least one skill offered by the one or more distributed actors, thus defining one or more assigned distributed actors; and performing the at least one skill offered by the one or more assigned distributed actors to address the unfulfilled need…; wherein assigning one or more distributed actors to address the unfulfilled need includes one or more of: immediately assigning to the one or more distributed actors; inquiring on the availability of the one or more distributed actors; and allowing a user to choose the one or more distributed actors from a group of potential distributed actors; wherein monitoring an environment to detect the existence of an unfulfilled need includes: detecting the existence of a request; wherein detecting the existence of a request includes one or more of: receiving a request from a human distributed actor; and receiving a request from a non-human distributed actor; wherein the one or more assigned distributed actors interact, directly or indirectly, with one or more distributed sub-actors to address at least a portion of the unfulfilled need; addressing at least a portion of the unfulfilled need with the at least one skill offered by the one or more assigned distributed actors; wherein addressing at least a portion of the unfulfilled need with the at least one skill offered by the one or more assigned distributed actors includes: generating one or more response portions with the at least one skill offered by the one or more assigned distributed actors; forming a bespoke response to the unfulfilled need based, at least in part, upon the one or more response portions; providing the bespoke response to a party associated with the unfulfilled need; effectuating, in whole or in part, the bespoke response; wherein maintaining a group of distributed actors includes: maintaining a database that defines the group of distributed actors; wherein the quality-of-service statistics define one or more of: a user satisfaction level for each of the group of distributed actors; and a review score for each of the group of distributed actors”. The claims describe a process of maintaining skills and quality-of-service statistics for a group of distributed actors (i.e. resources), monitoring an environment for an unfulfilled need and/or a request, and assigning distributed actors to address the unfulfilled need and/or request. Maintaining skills and quality-of-service statistics and monitoring an environment for an unfulfilled need and/or request can practically be performed in the human mind via observation, evaluation, and with pen and paper. Assigning distributed actors to unfulfilled needs or requests reflects certain methods of organizing human activity as assignment facilitates interaction between a person that has an unfulfilled need and/or request and a computer that is fulfilling the need and/or request. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes and Certain Methods of Organizing Human Activity. Mental Processes includes concepts performed in the human mind such as observations, evaluations, judgments, and opinions and include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Certain Methods of Organizing Human Activity includes managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1, 3-15, 17-29, and 31-42 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1, 6, 13, 15, 20, 27, 29, 34, and 41 recite additional elements of “a computer-implemented method, executed on a computing device; wherein the group of distributed actors includes one or more non-human distributed actors that perform a respective skill without human intervention; automatically executing a machine-executable script associated with each assigned distributed actor without human intervention, wherein each assigned distributed actor is generated to include a respective machine-executable script that is machine- interpretable and one or more natural language descriptions of the functions performed by the assigned distributed actor that are human-interpretable; wherein the group of distributed actors include one or more of: a software platform; a software application; a virtual machine; and a web-based service; database; a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations; and a computing system including a processor and memory configured to perform operations”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing devices and instructions that are used to perform/implement the abstract on a computer. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1, 3-15, 17-29, and 31-42 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1, 6, 13, 15, 20, 27, 29, 34, and 41 recite additional elements of “a computer-implemented method, executed on a computing device; wherein the group of distributed actors includes one or more non-human distributed actors that perform a respective skill without human intervention; automatically executing a machine-executable script associated with each assigned distributed actor without human intervention, wherein each assigned distributed actor is generated to include a respective machine-executable script that is machine- interpretable and one or more natural language descriptions of the functions performed by the assigned distributed actor that are human-interpretable; wherein the group of distributed actors include one or more of: a software platform; a software application; a virtual machine; and a web-based service; database; a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations; and a computing system including a processor and memory configured to perform operations”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1, 3-15, 17-29, and 31-42 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are 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.
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.
Claims 1, 3-15, 17-29, and 31-42 are rejected under 35 U.S.C. 103 as being unpatentable over Horvitz et al. (US 2014/0278634 A1) in view of Lange et al. (US 2022/0050661 A1).
As per claim 1, 15, and 29 (Currently Amended), Horvitz teaches a computer-implemented method, executed on a computing device, comprising (Horvitz e.g. FIG. 4 is a flow diagram summarizing some example steps that may be taken by a geospatial crowdsourcing service or the like when a task is received, which may be in real time, or based upon a scheduled task [0057]. The techniques described herein can be applied to any device. Accordingly, the below general purpose remote computer described below in FIG. 5 is but one example of a computing device [0062]. A method implemented at least in part on at least one processor, comprising (claim 1).); Horvitz teaches a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising; (Horvitz e.g. Computer 510 typically includes a variety of computer readable media and can be any available media that can be accessed by computer 510 (Fig. 5 and [0066]. One or more computer-readable media having computer-executable instructions, which when executed on at least one processor perform step (claim 19).); Horvitz teaches a computing system including a processor and memory configured to perform operations comprising: (Horvitz e.g. With reference to FIG. 5, an example remote device for implementing one or more embodiments includes a general purpose computing device in the form of a computer 510 [0065]. A system comprising, one or more processors, a memory communicatively coupled to the processor, and a spatiotemporal crowdsourcing configured to execute on the one or more processors from memory,.. (claim 13).)
Horvitz teaches maintaining a group of distributed actors, wherein the group of distributed actors includes one or more non-human distributed actors that perform a respective skill without human intervention; (Horvitz e.g. The subject matter described herein are directed towards spatiotemporal crowdsourcing technology (e.g., implemented as a service) configured to receive a task that includes task-related criteria [0005]. Actor-related data is accessed to select an actor set needed for accomplishing the task, including selecting one or more actors until the actor set is sufficient to accomplish the task [0007]. The actor set includes at least one human actor and at least one non-human actor (claim 14). FIG. 1 is a block diagram showing example components of an example spatiotemporal crowdsourcing implementation, in the form of a service 102 [0018]. In general, users 104 register as actors (members) in the spatiotemporal crowdsourcing service via a network (e.g., Internet) interface, shown as coupling to a user task preference and ability component 106 [0018]. The preference and qualification information is stored in an actor data store 108, and may include a list of capabilities, competencies and/or experience (e.g., a math tutor for algebra to calculus, five years teaching), price/rate (including overtime), preferences (e.g., evenings OK but not weekends, will work within thirty minutes of a general location), assets e.g., (have a bike or car), calendar data, schedule data and/or possibly other pertinent information. This information may be updated in real time as it changes, either with explicit instructions from the user and/or automatically through sensing and inference [0019]. Users are one type of actor that may be summoned to accomplish a task. Although not explicitly shown in FIG. 1, other example types of actors that may have actor data stored in the data store108 include non-human participants, such as a sensor (e.g., as a security camera, traffic camera and/or microphone), an automobile equipped with communication capabilities, tagged equipment, and so forth. Such other non-human actors may be summoned as part of accomplishing a task, as described below [0020].)
Horvitz teaches maintaining quality-of-service statistics for each of the group of distributed actors; (Horvitz e.g. Reputation data may be associated with a user's data, e.g., in the data store 108, such as provided via evaluations, manual ratings, automatic ratings and the like, such as entered by a task owner. A certain reputation measure (e.g., a minimum average rating level) may be specified in the task related criteria [0022].)
Horvitz teaches monitoring an environment to detect the existence of an unfulfilled need; (Horvitz e.g. FIG. 1 shows a task being received at a planning component 112, in which the task includes any number of criteria. Example criteria include a task deadline, a maximum cost, any reputation requirements, any acquaintance requirements and so on. Other criteria may specify a number of workers, skill sets required for the workers, non-human assets needed, and so forth. Basically any task requirement that may be matched against known data regarding actors' preferences and abilities to perform that task may be used as part of the task criteria [0024]. The planning component 112 (or another component of the service 102) may receive task completion state information, to ensure that tasks are completed by the deadline, with any re-planning as needed (e.g., an extra worker is needed) [0027]. State data also may be accessed, to determine the current state of an actor [0058]. Re-planning may be performed as state information comes in, e.g., the task is behind schedule and the task owner is willing to increase the budget to hire another worker, a confirmed worker did not show up, a piece of equipment broke down, and so forth. Tracking continues until the task is complete, as evaluated at step 416 [0060].)
Horvitz teaches assigning one or more distributed actors to address the unfulfilled need based, at least in part, upon the quality-of-service statistics and the at least one skill offered by the one or more distributed actors, thus defining one or more assigned distributed actors; and (Horvitz e.g. The spatiotemporal crowdsourcing service selects an actor set (e.g., one or more human workers and/or entities) for accomplishing the task, including having the task-related criteria and actor-related data used to determine inclusion in the actor set [0005]. FIG. 1 shows a task being received at a planning component 112, in which the task includes any number of criteria. Example criteria include a task deadline, a maximum cost, any reputation requirements, any acquaintance requirements and so on. Other criteria may specify a number of workers, skill sets required for the workers, non-human assets needed, and so forth. Basically any task requirement that may be matched against known data regarding actors' preferences and abilities to perform that task may be used as part of the task criteria [0024]. When a task needs to be performed, a subset of one or more actors is summoned by the planning component 112, which in the example of FIG. 1. In general, the planning component 112 uses the task criteria matching module 114 to work with the preference and ability component 106 to match the criteria associated with the task with an actor set (e.g., one or more users and/or any other actor or actors) [0025].)
Horvitz teaches performing the at least one skill offered by the one or more assigned distributed actors to address the unfulfilled need… (Horvitz e.g. Users are one type of actor that may be summoned to accomplish a task. Although not explicitly shown in FIG. 1, other example types of actors that may have actor data stored in the data store108 include non-human participants, such as a sensor (e.g., as a security camera, traffic camera and/or microphone), an automobile equipped with communication capabilities, tagged equipment, and so forth [0020]. One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061]. When the summoning is done and the appropriate actors have confirmed their availability, step represents 414 tracking the task completion state, e.g., versus the deadline, as task state information becomes available. Tracking continues until the task is complete, as evaluated at step 416 [0060].)
Horvitz does not explicitly teach, however, Lange teaches by automatically executing a machine-executable script associated with each assigned distributed actor without human intervention, wherein each assigned distributed actor is generated to include a respective machine-executable script that is machine-interpretable and one or more natural language descriptions of the functions performed by the assigned distributed actor that are human-interpretable. (Lange e.g. Implementations are described herein for analyzing existing graphical user interfaces (“GUIs”) to facilitate automatic interaction with those GUIs, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those GUIs (Abstract). Humans may engage in human-to-computer dialogs with interactive software applications referred to herein as “automated assistants” (also referred to as “chatbots,” “interactive personal assistants,” “intelligent personal assistants,” “personal voice assistants,” “conversational agents,” “virtual assistants,” etc.) [0001]. Suppose a user navigates a web browser to a particular retailer's website, and once the website is open on the user's device, the user could submits a spoken request, e.g., to an automated assistant, “Search for energy-efficient dishwashers.” In various implementations, a previously-generated script may be associated with that website/webpage and/or with the user's intent to search that website/webpage, e.g., in a database available to the automated assistant. The script may be executed with the parameters provided in the user's free-form natural language input (intent=search <website>, parameter=“energy-efficient dishwashers”) to cause a search field on the webpage to be automatically populated with the parameters [0004]. The scripts mentioned above may be generated automatically, e.g., with little or no human intervention, which provides a practical way to scale an automated assistant's ability to automatically navigate across myriad GUIs. These scripts may be generated “offline” or in “real time,” e.g., as a user uses an automated assistant to attempt to interact with a heretofore unknown webpage. In some implementations, the scripts may be generated by attempting to resolve a user's intent vis-a-vis a GUI that is active while the user submits a free-form natural language input, e.g., spoken or typed. Additionally or alternatively, in some implementations, the scripts may be generated in batches, e.g., with lists of GUIs (e.g., lists of uniform resource locators, or “URLs,” associated with webpages) and free-form natural language inputs for which resolution should be attempted on those GUIs [0005]. FIG. 1 is an example environment in which techniques disclosed herein may be implemented is illustrated [0027]. In various implementations, fulfillment (or “resolution” or “carrying out”) of the user's intent may cause various fulfillment information (also referred to as “responsive” information or “resolution information”) to be generated/obtained, e.g., by fulfillment module 124. As will be described below, the fulfillment information may in some implementations be provided to a natural language generator (“NLG” in some FIGS. 126, which may generate natural language output based on the fulfillment information. Also, in some implementations, fulfillment module 124 may be configured to execute scripts for automatically interacting with GUIs, which may be generated using techniques described herein [0054]. GUIs may take the form of webpages that are written in various markup languages, such as the hypertext markup language (“HTML”), extensible HTML (“XHTML”), the extensible markup language (“XML”), and so forth. In some cases, webpages are associated with other types of documents that, for instance, impose styles on and/or add functionality to webpages. For example, cascading style sheets (“CSS”) allow for description of a visual presentation of a markup language document, such as an HTML, XHTML, and/or XML document. In addition, many webpages may include and/or are linked to client-side code such as JavaScript that is executable at web browser 111 of client device 106 to facilitate local interactivity [0059]. GUI navigation engine 128 also may have access to a database of scripts 129 that are generated using techniques described herein. Each script may be associated with a GUI, and may include instructions that are performable to automatically navigate through and/or interact with at least a portion of the GUI. As an example, a script may be generated in association with a webpage. When that webpage is rendered by web browser 111 of an automated assistant-equipped client device 106, automated assistant 120 may retrieve the script, e.g., in response to a user's free-form natural language input requesting interaction with the webpage. Automated assistant 120 and/or another component may execute the script to trigger automatic interaction with one or more interactive elements of the webpage [0062].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Horvitz’s crowdsourcing service non-human distributed actors to enable performing the at least one skill offered by automatically executing a machine-executable script associated with each assigned non-human distributed actor without human intervention as taught by Lange in order to enable users to engage with an automated assistant (via a spoken or typed dialog session) and provide a practical way to scale an automated assistant’s ability to automatically navigate across myriad GUIs (Lange e.g. [0003] and [0005]).
As per claims 3, 17, and 31 (Currently Amended), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein assigning one or more distributed actors to address the unfulfilled need includes one or more of: immediately assigning to the one or more distributed actors; inquiring on the availability of the one or more distributed actors; and allowing a user to choose the one or more distributed actors from a group of potential distributed actors (Horvitz e.g. The spatiotemporal crowdsourcing service selects an actor set (e.g., one or more human workers and/or entities) for accomplishing the task, including having the task-related criteria and actor-related data used to determine inclusion in the actor set [0005]. Note that the selection of actors for the actor set may be dynamic, e.g., selection may change as a task progresses [0025]. The planning component 112 operates in conjunction with the other components 106, 114 and 116 to coordinate the summoning of the equipment and personnel to a specified location at a desired time, based upon who is available, when and where, and their pricing, along with any other criteria such as experience, reputation and so forth [0027]. When the summoning is done and the appropriate actors have confirmed their availability, step 414 represents tracking the task completion state, e.g., versus the deadline, as task state information becomes available (Fig. 4 and [0060]).)
As per claims 4, 18, and 32 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein monitoring an environment to detect the existence of an unfulfilled need includes: detecting the existence of a request (Horvitz e.g. FIG. 1 shows a task being received at a planning component 112, in which the task includes any number of criteria. Example criteria include a task deadline, a maximum cost, any reputation requirements, any acquaintance requirements and so on [0024]. FIG. 4 is a flow diagram summarizing some example steps that may be taken by a geospatial crowdsourcing service or the like when a task is received, which may be in real time, or based upon a scheduled task [0057].)
As per claims 5, 19, and 33 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 4, the computer program product of claim, and the computing system of claim 32, Horvitz teaches wherein detecting the existence of a request includes one or more of: receiving a request from a human distributed actor; and receiving a request from a non-human distributed actor (Horvitz e.g. FIG. 4 is a flow diagram summarizing some example steps that may be taken by a geospatial crowdsourcing service or the like when a task is received, which may be in real time, or based upon a scheduled task [0057]. Step 410 evaluates whether the needed distributed actors have confirmed and the summoning is done. Note that if the task criteria cannot be met, step 412 represents notifying the owner of the issue (i.e. human) [0059]. The actor set includes at least one human actor and at least one non-human actor (claim 14).)
As per claims 6, 20, and 34 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein the group of distributed actors include one or more of: a software platform; a software application; a virtual machine; and a web-based service (Horvitz e.g. Users are one type of actor that may be summoned to accomplish a task. Although not explicitly shown in FIG. 1, other example types of actors that may have actor data stored in the data store108 include non-human participants, such as a sensor (e.g., as a security camera, traffic camera and/or microphone), an automobile equipped with communication capabilities, tagged equipment, and so forth. Such other non-human actors may be summoned as part of accomplishing a task, as described below [0020]. Via contemporary computer-aware connectedness, mobile actors also may provide current state information (e.g., via a mobile device application) such as including current GPS coordinates and velocity at a certain sampling rate, and possibly a destination. A non-human mobile actor may likewise provide such state information, e.g., via GPS coordinates and velocity, a nearby truck may be summoned to help accomplish a task, regardless of who is actually driving the truck [0021]. Embodiments can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates to perform one or more functional aspects of the various embodiments described herein [0063].)
As per claims 7, 21, and 35 (Original), Horvitz in view of Lange teach the computer-implemented method of claim , the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein the one or more assigned distributed actors interact, directly or indirectly, with one or more distributed sub-actors to address at least a portion of the unfulfilled need (Horvitz e.g. Note that the selection of actors for the actor set may be dynamic, e.g., selection may change as a task progresses. If a larger task is broken up into smaller tasks, or subtasks, each subtask may have an actor set selected for that task at whatever time is appropriate including dynamically; for example, a single actor may be selected for a subtask such as part of a package delivery, with a next single actor selected ( e.g., dynamically based on proximity and availability) for the next subtask part of the delivery, and so on [0025]. The planning component 112 operates in conjunction with the other components 106, 114 and 116 to coordinate the summoning of the equipment and personnel to a specified location at a desired time, based upon who is available, when and where, and their pricing, along with any other criteria such as experience, reputation and so forth [0027]. One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061].)
As per claims 8, 22, and 36 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches further comprising: addressing at least a portion of the unfulfilled need with the at least one skill offered by the one or more assigned distributed actors (Horvitz e.g. One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061]. Other criteria may specify a number of workers, skill sets required for the workers, non-human assets needed, and so forth. Basically any task requirement that may be matched against known data regarding actors' preferences and abilities to perform that task may be used as part of the task criteria [0024].)
As per claims 9, 23, and 37 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 8, the computer program product of claim 22, and the computing system of claim 36, Horvitz teaches wherein addressing at least a portion of the unfulfilled need with the at least one skill offered by the one or more assigned distributed actors includes: generating one or more response portions with the at least one skill offered by the one or more assigned distributed actors (Horvitz e.g. FIG. 4 is a flow diagram summarizing some example steps that may be taken by a geospatial crowdsourcing service or the like when a task is received [0057]. If the actor data store is arranged as a data store, an optimized query may be made against the data store to obtain the actor set of actors that meet the criteria (Fig. 4 step 402 and [0058]). One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061]. Step 406 represents summoning the actor to appear at the specified location at the specified time. If the actor does not confirm (non-human actors may have automated confirmation or a person confirm on their behalf) within a confirmation time [0059]. The planning component 112 may specify that to be hired, each worker needs to confirm that he or she will be at the specified location at the specified time with any specified equipment [0028]. Step 410 evaluates whether the needed actors have confirmed and the summoning is done. The process continues until the needed actors have done so [0059]. Note that if the task criteria cannot be met, step 412 represents notifying the owner of the issue (Fig. 4 and [0059]).
As per claims 10, 24, and 38 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 9, the computer program product of claim 23, and the computing system of claim 37, Horvitz teaches further comprising: forming a bespoke response to the unfulfilled need based, at least in part, upon the one or more response portions (Horvitz e.g. Step 406 represents summoning the actor to appear at the specified location at the specified time. If the actor does not confirm (non-human actors may have automated confirmation or a person confirm on their behalf) within a confirmation time. Note that the candidate pool may be larger than the number of actors needed at step 404 so that the query and/or function need not be re-run each time an actor does not confirm in time [0059]. Step 410 evaluates whether the needed actors have confirmed and the summoning is done. The process continues until the needed actors have done so [0059]. The planning component 112 ( or another component of the service 102) may receive task completion state information, to ensure that tasks are completed by the deadline, with any re-planning as needed (e.g., an extra worker is needed) [0027]. Re-planning may be performed as state information comes in, e.g., the task is behind schedule and the task owner is willing to increase the budget to hire another worker, a confirmed worker did not show up, a piece of equipment broke down, and so forth. Tracking continues until the task is complete, as evaluated at step 416 [0060]. State information as to when the task is completed is distributed to the participants so as to stop working on the task [0029].)
As per claims 11, 25, and 39 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 10, the computer program product of claim 24, and the computing system of claim 38, Horvitz teaches further comprising: providing the bespoke response to a party associated with the unfulfilled need (Horvitz e.g. The planning component 112 operates in conjunction with the other components 106, 114 and 116 to coordinate the summoning of the equipment and personnel to a specified location at a desired time, based upon who is available, when and where, and their pricing, along with any other criteria such as experience, reputation and so forth [0027]. Bidding or the like may be implemented, e.g., different actors and/or groups may bid on the task [0026]. One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061]. Note that if the task criteria cannot be met, step 412 represents notifying the owner of the issue [0059].)
As per claims 12, 26, and 40 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 10, the computer program product of claim 24, and the computing system of claim 38, Horvitz teaches further comprising: effectuating, in whole or in part, the bespoke response (Horvitz e.g. When a task needs to be performed, a subset of one or more actors is summoned by the planning component 112, which in the example of FIG. 1 [0025]. One or more actors are matched to task-related criteria and summoned to accomplish a task, which may be divided into a set of coordinated tasks (subtasks) [0061]. Users are one type of actor that may be summoned to accomplish a task… Such other non-human actors may be summoned as part of accomplishing a task [0020]. For example, crowdphysics tasks may be performed using spatiotemporal crowdsourcing such as reducing traffic congestion. Given a sufficient percentage of participants' locations and velocities, a traffic flow model may be implemented, according to which participants may be notified ( e.g., audibly while driving) to get into a certain lane, drive at a certain speed, detour to avoid an accident scene and so forth. Participants may be separately notified of an optimal or advantageous departure time, (e.g., "Based upon traffic, the system recommends leaving no earlier than 5:15 pm") ([0029] and [0031]).)
As per claims 13, 27, and 41 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein maintaining a group of distributed actors includes: maintaining a database that defines the group of distributed actors (Horvitz e.g. The preference and qualification information is stored in an actor data store 108, and may include a list of capabilities, competencies and/or experience (e.g., a math tutor for algebra to calculus, five years teaching), price/rate (including overtime), preferences (e.g., evenings OK but not weekends, will work within thirty minutes of a general location), assets e.g., (have a bike or car), calendar data, schedule data and/or possibly other pertinent information. This information may be updated in real time as it changes, either with explicit instructions from the user and/or automatically through sensing and inference [0019]. Users are one type of actor that may be summoned to accomplish a task. Although not explicitly shown in FIG. 1, other example types of actors that may have actor data stored in the data store108 include non-human participants, such as a sensor (e.g., as a security camera, traffic camera and/or microphone), an automobile equipped with communication capabilities, tagged equipment, and so forth. An entity such as a truck or airplane thus may be entered into the service 102 as an actor along with its ability data, such as cargo capacity, weight, cost per mile, cost per hour, and so forth [0020]. Reputation data may be associated with a user's data, e.g., in the data store 108, such as provided via evaluations, manual ratings, automatic ratings and the like, such as entered by a task owner. A certain reputation measure ( e.g., a minimum average rating level) may be specified in the task related criteria [0022].).
As per claims 14, 28, and 42 (Original), Horvitz in view of Lange teach the computer-implemented method of claim 1, the computer program product of claim 15, and the computing system of claim 29, Horvitz teaches wherein the quality-of-service statistics define one or more of: a user satisfaction level for each of the group of distributed actors; and a review score for each of the group of distributed actors (Horvitz e.g. Reputation data may be associated with a user's data, e.g., in the data store 108, such as provided via evaluations, manual ratings, automatic ratings and the like, such as entered by a task owner. A certain reputation measure ( e.g., a minimum average rating level) may be specified in the task related criteria [0022].).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include US: Harding et al. (US 2022/0004482 A1) "Automation System and Method", FOR: White, R. (WO-2023191968-A1) “Auto-Managing Requestor Communications to Accommodate Pending Activities of Diverse Actors”, NPL: C. Hill, R. Yates, C. Jones and S. L. Kogan, "Beyond predictable workflows: Enhancing productivity in artful business processes," in IBM Systems Journal, vol. 45, no. 4, pp. 663-682, 2006.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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