DETAILED ACTION
This action is responsive to the claims filed on November 20, 2023. Claims 1-11 are under examination.
The Specification is objected to for its undescriptive title.
Claims 1-7, 10, and 11 are objected to for minor informalities.
Claims 1-11 are rejected under 35 USC 112(a) as lacking enablement.
Claims 1-11 are rejected under 35 USC 112(b) as indefinite.
Claims 1-11 are rejected under 35 USC 101 as ineligible.
Claims 1-11 are rejected under 35 USC 102(a)(1)/(a)(2) as anticipated by Zhong.
Claim 8 is additionally/alternatively rejected under 35 USC 103 as unpatentable over Zhong in view of Kadyrov.
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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claims 1-11 objected to because of the following informalities:
Antecedence
The following are features that lack appropriate antecedence:
Claims 1, 10, and 11: “setting”; “the received conversion”; “the simulation result”
Claim 2: “reception”; “specification”; “the simulation result”
Claim 3: “reception”; “specification”; “the simulation result”; “the received change amount”;
Claim 4: “reception”; “specification”; “the simulation result”; “the numeric value related to the second action accomplishing the received change amount”
Claim 5: “the simulation result”
Claim 6: “weighting calculation”; “change” ;“one or more features of the web page” (relative to the already recited one or more features)
Claim 7: “the received action history”
Extract
Claims 1, 10, and 11 recite, “extract a second action from first actions on the basis of […].” However, the Applicant’s specification shows that the second actions are not extracted from the first actions. The second actions are groupings of the first actions. The word extract has a number of definitions (See below for Merriam Webster definitions) that all suggest removing an element from a set and do not suggest to the person of ordinary skill in the art the grouping conducted in the specification. An appropriate replacement term is required. For purposes of examination, the extracting step will be interpreted to mean the grouping of actions, as demonstrated in the specification in the Applicant’s specification paragraphs [0033]-[0036].
Extract The Second Information
Claim 7 recites, “extract the second action on a basis of a learning model.” However, it is unclear whether this is an element of the extract step of claim 1 or is an additional extraction step. For purposes of examination, it will be interpreted that the limitation qualifies the extraction recited in claim 1.
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Weights and Model
From the claim it is unclear what relationship the weights have relative to the model (e.g., whether the weights are weights of the model). For purposes of examination, the weights will be interpreted to be weights of the model.
Simulate
Claim 9 recites, “simulate a relationship between a change of a numeric value related to the second action and a change of a numeric value related to the conversion, on a basis of the first result and the second result.” It is unclear from the claim language whether this is an additional simulation step or merely qualifies the simulation step of claim 1. For purposes of examination, it will be interpreted to qualify the simulation of claim 1.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-11 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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Independent claims 1, 10, and 11 substantively recite, “receive setting of a conversion which is a specific action performed by the plurality of users on the website.” First, it is unclear from this language whether the conversion is the “setting” or the “conversion.” Paragraph [0033] of the Applicant’s specification states, “[t]he processor 21 receives setting of a webpage which is the conversion target (step 2). Specifically, the processor 21 receives, from the information terminal 30 operated by an engineer or the like, specification of a webpage with is the conversion target.” There is a significant difference between that which is presented in the specification and the claims. That is, the claim recites, “setting of a conversion,” but the specification does not explain what that is. Further, “setting of a conversion has no meaning as a term of art. Also, the language, “receive setting of a conversion” is awkward and grammatically incorrect. The specification states that the specification applies to a webpage, not a conversion, and further differentiates an action from a conversion and a webpage, contrary to what the “which is a specific action performed by the plurality of users on the website” suggests.
Under MPEP 2164.01(a), the most significant Wands factors for this circumstance include:
(F) The amount of direction provided by the inventor: As indicated, the claims and the specification appear to disagree as to what step 2 in the specification represents. In the specification, a webpage is received. In the claims, “a setting of a conversion is received.” The claim continues to state that either the conversion or the setting (unclear from the claim language) is a specific action. However, the specification differentiates between the setting, conversion, and action. Worse than providing no direction, the inventor provides conflicting direction.
(G) The existence of working examples: In the examples provided, including paragraphs [0043]-[0045], it is unclear to which elements the claim limitation refers. That is, the examples specify webpages, actions, and conversions that are specifiable.
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure: With the lack of clarity in the claims, a person of ordinary skill in the art could not be certain that they had practiced the invention or otherwise infringed upon the claimed invention, even with infinite experimentation.
For purposes of examination, the limitations will be interpreted to mean receive data associated with a website.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Receive Setting of a Conversion
Claim 1 recites, “receive setting of a conversion, which is a specific action performed by the plurality of users on the website.” First, it is unclear from this language whether the conversion is the “setting” or the “conversion.” Paragraph [0033] of the Applicant’s specification states, “[t]he processor 21 receives setting of a webpage which is the conversion target (step 2). Specifically, the processor 21 receives, from the information terminal 30 operated by an engineer or the like, specification of a webpage with is the conversion target.” There is a significant difference between that which is presented in the specification and the claims. That is, the claim recites, “setting of a conversion,” but the specification does not explain what that is. Further, “setting of a conversion has no meaning as a term of art. Also, the language, “receive setting of a conversion” is awkward and grammatically incorrect. The specification states that the specification applies to a webpage, not a conversion, and further differentiates an action from a conversion and a webpage, contrary to what the “which is a specific action performed by the plurality of users on the website” suggests. For purposes of examination, the limitations will be interpreted to mean receive data associated with a website.
Does An Action Which Is Set For The Conversion
Claim 7 recites, “extract the second action on a basis of a learning model, the learning model being a model which receives an action history and which outputs a possibility that a virtual user based on the received action history does an action which is set for the conversion.” Claim 9 recites, “ provide, as input, a first action history to a learning model which receives an action history and which outputs whether a virtual user based on the received action history does an action which is set for the conversion.” Does an action which is set for the conversion has no meaning to a person of ordinary skill in the art and is nonsense in the English language. This appears to be a significant enough translation error that it puts a person of ordinary skill in the art in doubt of what the claim intended. For purposes of examination, this will be interpreted to mean the likelihood that an action results in a conversion.
Dependent claims which depend from rejected claims are rejected based on their dependence.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2-4 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which they depend, or for failing to include all the limitations of the claim upon which they depend.
Claims 2-4 recite, exclusively, contingent limitations, as described in MPEP 2111.04(II). That is, because there is no guarantee that the conditions recited in the claims occur, under the broadest reasonable interpretation, the limitations are given no patentable weight. In order to remove the contingency of the limitations, the Applicant must either link the contingency (e.g., by antecedence) to actions already taken (e.g., branches/choices explicitly taken/made) or amend the claims to include operations where the contingencies are satisfied prior to the recitation of the contingencies. For example:
ingesting a pie;
responsive to the pie ingestion, ingesting an antacid.
Because claims 2-4 currently recite exclusively contingent limitations, the broadest reasonable interpretation includes that the steps are never undertaken. Accordingly, under the broadest reasonable interpretation, claims 2-4 do not further limit the scope of claim 1. Accordingly claims 2-4 are rejected under 35 USC 112(d).
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed subject matter is directed to an abstract idea without significantly more. In their broadest reasonable sense, the claims include simulating using known customer data, sales projections, which is a fundamental economic practice. This is especially the case when considering the broadest reasonable scope of the term, simulate. For this reason alone, the claims are ineligible. Further, the claims recite mental processes that are capable of being performed in the mind and/or with the aid of pen and paper, which are abstract ideas. This is evident from the examples of FIGs. 6-16, with mathematical (e.g., averages, weighted averages, weighted quantities representing relevance) and replacement operations all practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator. The dependent claims 7-9 introduce a generic machine learning model and perform longstanding operations with the generic machine learning model, which fails to confer eligibility under MPEP 2106.05(f). The claims fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2, or Step 2B.
Independent Claims
Claim 1 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite mental processes, which are abstract ideas, as well as fundamental economic practices. The fundamental economic practices and/or commercial interactions, which are methods of organizing human activity, were addressed in the introduction paragraph of this section, and are applied to the method as a whole rather than to any particular element. That is, under the category of organizing human activity, the claimed recited operations are elements of an abstract idea. The mental process grouping will be applied element-by-element, in accordance with the eligibility tests of the MPEP.
Claim 1 recites:
extracting a second action from first actions on a basis of the first actions and information about, for each action history, whether the received conversion is included in the action history, the first actions being included in the obtained action histories; (Mental Process – Grouping actions or extracting subsets of actions from a group of actions to determine combined actions is practically performable in the mind or with simple aids, such as pen, paper, or a calculator.)
simulating a relationship between a change of a numeric value related to the extracted second action and a change of a numeric value related to the conversion; (Mental Process – This is accomplished in the Applicant’s specification by replacing some values as the basis of a calculation and then performing a calculation (e.g., see the Applicant’s paragraphs [0037]-[0047] and FIGs. ), all of which are practically performable in the mind or with simple aids, such as pen, paper, or a calculator.)
Claim 1 recites mental processes, which are abstract ideas.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
Additional limitations:
An information processing system comprising:
one or more processors configured to:
These are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
obtaining action histories of a plurality of users on a website;
receiving setting of a conversion which is a specific action performed by the plurality of users on the website;
This is mere data gathering akin to the MPEP 2106.05(g) examples: “i. Performing clinical tests on individuals to obtain input for an equation” “v. Consulting and updating an activity log, Ultramercial,” “i. Limiting a database index to XML tags” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Accordingly, this is extra-solution activity and fails to integrate the abstract ideas into a practical application.
output the simulation result.
This is “apply-it” activity similar to the MPEP 2106.05(f) and 2106.05(a) examples: “collecting, displaying, and manipulating data.”; “iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished”; “vi. Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result” “iii. Gathering and analyzing information using conventional techniques and displaying the result.” Further, this is insignificant extra-solution activity similar to the MPEP 2106.05(g) examples: “a printer that is used to output a report of fraudulent transactions”; “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” “ii. Printing or downloading generated menu.” Therefore, under MPEP 2106.05(f), 2106.05(a), and 2106.05(g), the limitation fails to integrate the abstract idea into a practical application.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to integrate the abstract idea into a practical application.
Claim 1 fails to recite any additional limitations that integrate the abstract idea into a practical application.
Claim 1 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
Additional limitations:
An information processing system comprising:
one or more processors configured to:
These are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
obtaining action histories of a plurality of users on a website;
receiving setting of a conversion which is a specific action performed by the plurality of users on the website;
This is well-understood, routine, and conventional (WURC) activity akin to the MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “v. Electronically scanning or extracting data from a physical document” “i. Determining the level of a biomarker in blood by any means “ “v. Analyzing DNA to provide sequence information or detect allelic variants” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because this activity is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the limitation fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
output the simulation result.
This is well-understood, routine, and conventional (WURC) activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network”; “iii. Electronic recordkeeping”; “iv. Storing and retrieving information in memory”; “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Further, this is “apply-it” activity similar to the MPEP 2106.05(f) and 2106.05(a) examples: “collecting, displaying, and manipulating data.”; “iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished”; “vi. Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result” “iii. Gathering and analyzing information using conventional techniques and displaying the result.” Further, this is insignificant extra-solution activity similar to the MPEP 2106.05(g) examples: “a printer that is used to output a report of fraudulent transactions”; “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” “ii. Printing or downloading generated menu.” Therefore, under MPEP 2106.05(f), 2106.05(a), 2106.05(g), and 2106.05(d) the limitation fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
The additional limitations fail to combine with the other elements of claim 1 to provide significantly more than the abstract idea that would confer an inventive concept.
Claims 1 is ineligible.
Claim 10 recites an implementation of (the implicit storage of) the information processing system with one or more processors of claim 1, and claim 11 recites the operations of claim 1, so claims 10 and 11 are ineligible for at least the same reasons as claim 1.
Dependent Claims
Dependent claims 2-9 are also ineligible for at least the following reasons.
Claim 2
wherein the one or more processors are configured to:
This is a generic computing element that fails to confer eligibility under MPEP 2106.05(f).
in response to reception of specification of the second action, output the simulation result about the received second action.
This is well-understood, routine, and conventional (WURC) activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network”; “iii. Electronic recordkeeping”; “iv. Storing and retrieving information in memory”; “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Further, this is “apply-it” activity similar to the MPEP 2106.05(f) and 2106.05(a) examples: “collecting, displaying, and manipulating data.”; “iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished”; “vi. Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result” “iii. Gathering and analyzing information using conventional techniques and displaying the result.” Further, this is insignificant extra-solution activity similar to the MPEP 2106.05(g) examples: “a printer that is used to output a report of fraudulent transactions”; “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” “ii. Printing or downloading generated menu.” Therefore, under MPEP 2106.05(f), 2106.05(a), 2106.05(g), and 2106.05(d) the limitation fails to confer eligibility.
Claim 2 fails to recite any additional limitations that confer eligibility.
Claim 2 is ineligible.
Claim 3
wherein the one or more processors are configured to:
This is a generic computing element that fails to confer eligibility under MPEP 2106.05(f).
in response to reception of specification of a change amount of the numeric value related to the second action, output the simulation result about the received change amount of the numeric value related to the second action.
In its broadest reasonable sense, this merely outputs data, which fails to confer eligibility for at least the same reasons as the output step of claim 1.
Should it be found otherwise, the underlying determinations to determine the output from the input are practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator.
Claim 3 fails to recite any additional limitations that confer eligibility.
Claim 3 is ineligible.
Claim 4
wherein the one or more processors are configured to:
This is a generic computing element that fails to confer eligibility under MPEP 2106.05(f).
In response to reception of specification of a change amount of the numeric value related to the conversion, output, as the simulation result, a change amount of the numeric value related to the second action accomplishing the received change amount.
In its broadest reasonable sense, this merely outputs data, which fails to confer eligibility for at least the same reasons as the output step of claim 1.
Should it be found otherwise, the underlying determinations to determine the output from the input are practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
Claim 4 fails to recite any additional limitations that confer eligibility.
Claim 4 is ineligible.
Claim 5
wherein the one or more processors are configured to:
This is a generic computing element that fails to confer eligibility under MPEP 2106.05(f).
based on the simulation result, extract one or more features of a web page included in the website,
Extracting features of an element with data content based on a simulation result is practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
and output a relationship between the one or more extracted features and a change of the numeric value related to the conversion.
In its broadest reasonable sense, this merely outputs data, which fails to confer eligibility for at least the same reasons as the output step of claim 1.
Should it be found otherwise, the underlying determinations to determine the output from the input are practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
Claim 5 fails to recite any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 6
wherein the one or more processors are configured to:
This is a generic computing element that fails to confer eligibility under MPEP 2106.05(f).
based on weighting calculation in accordance with the one or more features, output a relationship between change of one or more features of the web page and a change appearing in the conversion.
In its broadest reasonable sense, this merely outputs data, which fails to confer eligibility for at least the same reasons as the output step of claim 1.
Should it be found otherwise, the underlying determinations to determine the output from the input are practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
Claim 6 fails to recite any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claim 7
wherein the one or more processors are configured to: […] learning model, the learning model being a model […]
These are generic computing elements recited at a high level of generality, so they fail to confer eligibility under MPEP 2106.05(f).
extract the second action on a basis of a [mind] which receives an action history and which outputs a possibility that a virtual user based on the received action history does an action which is set for the conversion.
Extracting information based on other information, to output still other information is practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
outputs a possibility that a virtual user based on the received action history does an action which is set for the conversion.
This merely outputs data, which fails to confer eligibility for at least the same reasons as the output step of claim 1.
Claim 7 fails to recite any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claim 8
wherein the one or more processors are configured to: update weights through learning, the weights being set for the respective first actions in a process of calculation performed by the learning model, […] on a basis of the updated weights
This is a recitation of a generic machine learning model being trained in generic conventional ways (e.g., supervised learning), so this is a generic computer implementation recited at a high level, which, under MPEP 2106.05(f), fails to confer eligibility. Also, supervised learning and the use of a model trained thereby is now a longstanding practice.
and extract the second action
Extraction of data is an inference that is practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
Claim 8 fails to recite any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 9
wherein the one or more processors are configured to: […] learning model […]
These are generic computing elements recited at a high level of generality, so they fail to confer eligibility under MPEP 2106.05(f).
provide, as input, a first action history to a [mind] which receives an action history and which outputs whether a virtual user based on the received action history does an action which is set for the conversion, and
Technically, this limitation does not necessarily recite an inference, so it is mere data gathering for the input (which fails to confer eligibility for the same reasons as the receive step of claim 1) and insignificant extra solution activity and WURC for the output (for the same reasons as the output step of claim 1), which both fail to confer eligibility.
Should it be found that this incudes an inference, the determination of whether a customer performs a conversion-related action based on input of the customer’s action history is practically performable in the mind or with simple aids, such as pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea.
obtain a first result related to the conversion on a basis of the provided first action history;
This is mere data gathering and WURC for the same reasons as the receive step in claim 1. Specifically, see the MPEP 2106.05(g) example. It is also insignificant extra-solution activity and WURC for the same reasons as the output step of claim 1.
Claim 9 fails to recite any additional limitations that confer eligibility.
Claim 9 is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-7,9-11: Zhong
Claim(s) 1-7,9-11 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as anticipated by US 2016/0034948 A1 to Zhong et al. (Zhong).
Claims 1, 10, and 11
Regarding claim 1, Zhong teaches:
An information processing system comprising: one or more processors configured to: (Zhong [0037] “FIG. 2 depicts a diagrammatic representation of example system architecture 200 comprising one or more clients 202 and attribution platform 220.” [0039] “FIG. 2 depicts a diagrammatic representation of example system architecture 200 comprising one or more clients 202 and attribution platform 220.” – Info processing system with processors.)
obtain action histories of a plurality of users on a website; (Zhong [0042] “software running on a server computer in platform 220 may receive a client file containing attribution data from an attribution data collecting computer associated with a client. For example, a client may represent an online retailer and may collect click stream data from visitors to a Web site own and/or operated by the online retailer. The attribution data thus collected can provide a detailed look at how each visitor got to the Web site, what pages were viewed by the visitor, what products and/or services the visitor clicked on, the date and time of each visit and click, and so on. – Action history is obtained for each of a plurality of users.)
receive setting of a conversion which is a specific action performed by the plurality of users on the website; (Zhong [0041] “The input data may be from a log file, a memory, a streaming source, or ad and page tags. Within this disclosure, the term “attribution data” refers to any and all data associated with online advertising events such as clicking on an ad, viewing an ad (an impression), entering a search query, conversion, and so on, and may include click history data, click intelligence data, post-click data, visitor profile data, impression data, etc.” – Input data includes a conversion, as well as selections and actions performed by users on a website. [0115] “In a step 404, for each event definition (i.e., a particular granularity level), event sets for each user/conversion are created. This is essentially to arrange events by user and conversion. For each incidence of the conversion, this step may include listing all the event item exposures the user had prior to the conversion. Events are defined and tracked from the raw impression/click/conversion data obtained from the ad tags and page tags or log files or other data collected.” Each event/setting/action is separately selected and is processed as it is selected.)
extract a second action from first actions on a basis of the first actions and information about, for each action history, whether the received conversion is included in the action history, the first actions being included in the obtained action histories; (Zhong [0115]-[0116] “In a step 404, for each event definition (i.e., a particular granularity level), event sets for each user/conversion are created. This is essentially to arrange events by user and conversion. For each incidence of the conversion, this step may include listing all the event item exposures the user had prior to the conversion. Events are defined and tracked from the raw impression/click/conversion data obtained from the ad tags and page tags or log files or other data collected. In a step 406, for each event definition, create event subsets that need counts. That is, for each event set of size K (that associates with a conversion), generate K−1 event subsets as explained above.” – Second actions (event subsets) are extracted/determined based on the First actions (events) from the action histories of the users, including whether the sets of events resulted in a conversion.)
simulate a relationship between a change of a numeric value related to the extracted second action and a change of a numeric value related to the conversion; and output the simulation result. (Zhong [0013] “Embodiments can leverage probabilistic modeling from aggregate-level data from non-converting users for each advertising attribute associated with an advertiser's advertising channel (e.g., campaign, site, placement, geo location, etc.)” [0015] “In some embodiments, a method for determining fractional attribution using user-level data and aggregate-level data may include determining, by a server computer using aggregate channel data within each channel of a plurality of channels, marginal conversion probabilities for individual channel attributes within each channel.” – The grouped subsets comprise elements of channels. [0158] “A regression modeling approach can be used to build a predictive model that can predict total (multi-channel) conversions, based on channel volumes. According to embodiments, a what-if analysis to produce a “delta key performance indicator (KPI)” that can be attributed to a given channel.” See Also, the equations in [0083]-[0103] – The numerical effect of the channel(s) on the conversions is simulated by a model that outputs results.)
Claim 10 recites an implementation of (the implicit storage of) the information processing system with one or more processors of claim 1, and claim 11 recites the operations of claim 1, so claims 10 and 11 are rejected for at least the same reasons as claim 1.
NOTE: Despite the contingent limitations having no patentable weight, an art rejection will be made for each of claims 2-4 based on an assumption that the condition precedent is triggered/that a branch is taken.
Claim 2
Regarding claim 2, Zhong teaches the features of claim 1, and further teaches:
in response to reception of specification of the second action, output the simulation result about the received second action. (Zhong [0013] “Embodiments can leverage probabilistic modeling from aggregate-level data from non-converting users for each advertising attribute associated with an advertiser's advertising channel (e.g., campaign, site, placement, geo location, etc.)” [0015] “In some embodiments, a method for determining fractional attribution using user-level data and aggregate-level data may include determining, by a server computer using aggregate channel data within each channel of a plurality of channels, marginal conversion probabilities for individual channel attributes within each channel.” – The grouped subsets comprise elements of channels. [0158] “A regression modeling approach can be used to build a predictive model that can predict total (multi-channel) conversions, based on channel volumes. According to embodiments, a what-if analysis to produce a “delta key performance indicator (KPI)” that can be attributed to a given channel.” See Also, the equations in [0083]-[0103] – The numerical effect of the channel(s) on the conversions is simulated by a model that outputs results.)
Claim 3
Regarding claim 3, Zhong teaches the features of claim 1, and further teaches:
in response to reception of specification of a change amount of the numeric value related to the second action, output the simulation result about the received change amount of the numeric value related to the second action. (Zhong [0106] “It may be desirable to define an event as specifically as possible; e.g., a user seeing exactly n impressions from campaign x with creative y on site z exactly m days ago. However, defining events at that deep level of granularity may encounter data sparsity—often there is not enough data to robustly derive the conditional probabilities described in the previous section. It may sound counterintuitive as the system easily collects billions of impressions and hundreds of millions of users every month from a large advertiser. However, not many users would share the same event of “seeing exactly n impressions from campaign x with creative y on site z exactly m days ago”. When the number of users is small, there would be low confidence in the conditional probabilities estimated.” – Conditional probabilities show numerical probabilities of outcomes based on certain given information, such as channel/set of events.)
Claim 4
Regarding claim 4, Zhong teaches the features of claim 1, and further teaches:
in response to reception of specification of a change amount of the numeric value related to the conversion, output, as the simulation result, a change amount of the numeric value related to the second action accomplishing the received change amount. (Zhong [0106] “It may be desirable to define an event as specifically as possible; e.g., a user seeing exactly n impressions from campaign x with creative y on site z exactly m days ago. However, defining events at that deep level of granularity may encounter data sparsity—often there is not enough data to robustly derive the conditional probabilities described in the previous section. It may sound counterintuitive as the system easily collects billions of impressions and hundreds of millions of users every month from a large advertiser. However, not many users would share the same event of “seeing exactly n impressions from campaign x with creative y on site z exactly m days ago”. When the number of users is small, there would be low confidence in the conditional probabilities estimated.” [0158] “A regression modeling approach can be used to build a predictive model that can predict total (multi-channel) conversions, based on channel volumes. According to embodiments, a what-if analysis to produce a “delta key performance indicator (KPI)” that can be attributed to a given channel. In particular, the what-if analysis sets the volume for a channel to 0 and uses the delta change in predicted conversions as a measure of the conversion contribution from the channel.” – Conditional probabilities show numerical probabilities of outcomes based on certain given information, such as channel/set of events. These are all interrelated by the statistical methods of Zhong.)
Claim 5
Regarding claim 5, Zhong teaches the features of claim 1, and further teaches:
based on the simulation result, extract one or more features of a web page included in the website, and output a relationship between the one or more extracted features and a change of the numeric value related to the conversion. (Zhong [0012] “An end user may be exposed to one or more advertising channels, e.g., he may receive directed e-mail advertisements, or may visit one or more Web sites or search engines having advertisements hosted by a client of the attribution platform. The end user may use a Web browser application running on a user device and may click on or otherwise convert (e.g., visit the advertiser's Web page, sign up for additional mailings, or make a purchase) upon exposure to one or more of the advertising channels.” [0185]-[0159] “A regression modeling approach can be used to build a predictive model that can predict total (multi-channel) conversions, based on channel volumes. According to embodiments, a what-if analysis to produce a “delta key performance indicator (KPI)” that can be attributed to a given channel. In particular, the what-if analysis sets the volume for a channel to 0 and uses the delta change in predicted conversions as a measure of the conversion contribution from the channel. The deltas may be normalized across all channels to get a channel weight.” “That is, delta KPI=predicted KPI (with all channels)—predicted KPI (without [what-if] channel).” [0170] “A channel may be split into sub-channels as needed. For example, one might want to split Retargeting Display and Non-Retargeting display into two different sub-channels as the conversions rates for then can differ by more than one order of magnitude. In addition they are designed to target users at very different of funnel stages. Another example is Branded Search vs. Non-Branded Search—intuitively Branded Search is at a later stage than Non-Branded Search as the users searching for branded keywords are likely already past the awareness stage and in the consideration stage for the particular brand.” - Based on the simulation result, features of a webpage are extracted, and a relationship is output between the one or more extracted features and a change of the numeric value related to the conversion.)
Claim 6
Regarding claim 6, Zhong teaches the features of claim 5, and further teaches:
based on weighting calculation in accordance with the one or more features, output a relationship between change of one or more features of the web page and a change appearing in the conversion. (Zhong [0013] “Embodiments can leverage probabilistic modeling from aggregate-level data from non-converting users for each advertising attribute associated with an advertiser's advertising channel (e.g., campaign, site, placement, geo location, etc.) The conversion probabilities for each ad attribute are combined with channel-level weights from a separate aggregate-level regression model. This approach is driven by data in that the importance (which serves as the basis of calculating attribution fraction) of each advertisement event is derived based on data on both converted users and non-converting aggregate-level data.” [0015] “The server computer may combine the marginal conversion probabilities to produce an importance weight for each touch point of a plurality of touch points on a converting path characterized by a set of attributes. The server computer may normalize these importance weights across the plurality of touch points on the converting path to obtain attribution results.” [0087] “The second property, Correlation with Conversion, holds that the weight for each event should be roughly correlated with the event's ability to drive conversions based on historical data. If E1 historically has driven conversions better than E2 and E3 together, then E1 deserves more credit than either E2 and E3.” – Based on weighting calculation in accordance with the one or more features, output a relationship between change of one or more features of the webpage and a change appearing in the conversion and a change appearing in the conversion.)
Claim 7
Regarding claim 7, Zhong teaches the features of claim 1 and further teaches:
extract the second action on a basis of a learning model, the learning model being a model which receives an action history and which outputs a possibility that a virtual user based on the received action history does an action which is set for the conversion. (Zhong [0042] “For example, a client may represent an online retailer and may collect click stream data from visitors to a Web site own and/or operated by the online retailer. The attribution data thus collected can provide a detailed look at how each visitor got to the Web site, what pages were viewed by the visitor, what products and/or services the visitor clicked on, the date and time of each visit and click, and so on.” [0087]-[0093] “The second property, Correlation with Conversion, holds that the weight for each event should be roughly correlated with the event's ability to drive conversions based on historical data. If E1 historically has driven conversions better than E2 and E3 together, then E1 deserves more credit than either E2 and E3. The third property of the model should take into account as much as possible the interactions among different events. For example, if individually each of the three events has driven conversions equally well, but when E2 and E3 are together they have driven conversions much better, a higher credit weight should be given to either E2 or E3 than to E1. Let conversion be represented by C, in mathematical terms, this means If P(C|E1)≅P(C|E2)≅P(C|E3) but P(C|E2, E3)>>P(C|E1), then w2>>w1 and w3>>w1. Embodiments make use of data-driven probabilistic models. That is, all the conditional probability estimates discussed herein are based on historical data. In particular, each conditional probability P(A|B) can be derived from historical data by dividing the number of users who (at least) had events A and B by number of users who (at least) had event B. […] Embodiments may make use of any of a variety of models, although some may be more or less desirable, depending on the nature of the data. A first model (Model 1) may be the Naïve Bayes model: Consider the naïve Bayes model […]” – A machine learning model is used to determine the likelihood of a conversion based on user history, including click streams with channels and subchannels (e.g., subsets of actions).)
Claim 9
Regarding claim 9, Zhong teaches the features of claim 1 and further teaches:
provide, as input, a first action history to a learning model which receives an action history and which outputs whether a virtual user based on the received action history does an action which is set for the conversion, and obtain a first result related to the conversion on a basis of the provided first action history; provide, as input, a second action history to the learning model, the second action history being generated on a basis of the first action history, and obtain a second result related to the conversion on a basis of the provided second action history; and (Zhong [0013] “Embodiments can leverage probabilistic modeling from aggregate-level data from non-converting users for each advertising attribute associated with an advertiser's advertising channel (e.g., campaign, site, placement, geo location, etc.)” [0015] “In some embodiments, a method for determining fractional attribution using user-level data and aggregate-level data may include determining, by a server computer using aggregate channel data within each channel of a plurality of channels, marginal conversion probabilities for individual channel attributes within each channel.” – The grouped subsets comprise elements of channels. [0158] “A regression modeling approach can be used to build a predictive model that can predict total (multi-channel) conversions, based on channel volumes. According to embodiments, a what-if analysis to produce a “delta key performance indicator (KPI)” that can be attributed to a given channel.” See Also, the equations in [0083]-[0103] – The numerical effect of the channel(s) on the conversions is simulated by a model that outputs results. [0042] “For example, a client may represent an online retailer and may collect click stream data from visitors to a Web site own and/or operated by the online retailer. The attribution data thus collected can provide a detailed look at how each visitor got to the Web site, what pages were viewed by the visitor, what products and/or services the visitor clicked on, the date and time of each visit and click, and so on.” [0087]-[0093] “The second property, Correlation with Conversion, holds that the weight for each event should be roughly correlated with the event's ability to drive conversions based on historical data. If E1 historically has driven conversions better than E2 and E3 together, then E1 deserves more credit than either E2 and E3. The third property of the model should take into account as much as possible the interactions among different events. For example, if individually each of the three events has driven conversions equally well, but when E2 and E3 are together they have driven conversions much better, a higher credit weight should be given to either E2 or E3 than to E1. Let conversion be represented by C, in mathematical terms, this means If P(C|E1)≅P(C|E2)≅P(C|E3) but P(C|E2, E3)>>P(C|E1), then w2>>w1 and w3>>w1. Embodiments make use of data-driven probabilistic models. That is, all the conditional probability estimates discussed herein are based on historical data. In particular, each conditional probability P(A|B) can be derived from historical data by dividing the number of users who (at least) had events A and B by number of users who (at least) had event B. […] Embodiments may make use of any of a variety of models, although some may be more or less desirable, depending on the nature of the data. A first model (Model 1) may be the Naïve Bayes model: Consider the naïve Bayes model […]” – A machine learning model is used to determine the likelihood of a conversion based on user history, including click streams with channels and subchannels (e.g., subsets of actions). [0086]-[0088] “The first desired property is Monotonicity, which means that if two events (e.g., E1 and E2) were combined into one composite event E12 then the fraction credit w12 for E12 should most likely be no less than w1 or w2. That is, w12≧w1 and w12≧w2. The intuition is that two events a converted user has with a marketer's campaigns should deserve no less credit than each of those two events individually. The second property, Correlation with Conversion, holds that the weight for each event should be roughly correlated with the event's ability to drive conversions based on historical data. If E1 historically has driven conversions better than E2 and E3 together, then E1 deserves more credit than either E2 and E3. The third property of the model should take into account as much as possible the interactions among different events. For example, if individually each of the three events has driven conversions equally well, but when E2 and E3 are together they have driven conversions much better, a higher credit weight should be given to either E2 or E3 than to E1.” [0093]-[0094] “This naïve choice does possess Properties 1 and 2 discussed above. However, this model assumes that the three events {E1, E2, E3} are independent given the conversion event C. It does not return the right answer when there are strong event correlations; that is, it does not possess Property 3.” – Probability distributions that single actions result in a conversion are determined using the model. Further, probability distributions are determined for groups of the actions, representing second actions, in Bayes probability chains to determine probability of conversion of these second actions (subsets of actions, e.g., represented by click streams between webpages).
simulate a relationship between a change of a numeric value related to the second action and a change of a numeric value related to the conversion, on a basis of the first result and the second result. (Zhong [0094]-[0096] “This naïve choice does possess Properties 1 and 2 discussed above. However, this model assumes that the three events {E1, E2, E3} are independent given the conversion event C. It does not return the right answer when there are strong event correlations; that is, it does not possess Property 3. […] A second model (Model 2) may be the Conversion Index model: If w1 is set to be the conversion index of E1 […] where Ē1 means “no event E1”. This model turns out to be very similar to the naïve Bayes model because w1 in (3) is strongly positively (although nonlinearly) correlated with P(C|E1). As in the naïve Bayes model, correlations among the three events are not taken into account. […] A third model (Model 3) may be the Conditional Importance model: Consider capturing the importance E1 […] which indicates how likely E1 is observed, given that {E2, E3, C) are observed […]” [0097]-[0102] “A fourth model (Model 4) may be the Marginal Importance model […] Overall, the Marginal Importance model in […] may provide better results than the other models discussed and possesses the three desired properties proposed above.” – Zhong uses various models to simulate the relationship between a change of numeric value related to the second action and a change of a numeric value relate to the conversion, on a basis of determined probabilities for the second action generating a conversion, which is based on the individual probabilities of the actions comprising the second action.
Claim Rejections - 35 USC § 102/103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 8: Zhong or Zhong and Kadyrov
Claim 8 is rejected under 35 U.S.C. 102(a)(1)/(a)(2) as anticipated by US 2016/0034948 A1 to Zhong et al. (Zhong) or, in the alternative, under 35 U.S.C. 103 as obvious over US 2016/0034948 A1 to Zhong et al. (Zhong) in view of NPL: “Attribution of Customers’ Actions Based on Machine Learning Approach” by Kadyrov et al. (Kadyrov).
Claim 8
Regarding claim 8, Zhong teaches the features of claim 7. Zhong teaches a modified Naïve Bayes model for importance of weights that are arguably learnable to relate between different actions and groups of actions in terms of conversion probabilities (Zhong [0087] “The second property, Correlation with Conversion, holds that the weight for each event should be roughly correlated with the event's ability to drive conversions based on historical data. If E1 historically has driven conversions better than E2 and E3 together, then E1 deserves more credit than either E2 and E3.” [0099] “This model also addresses the issue of not considering event interactions (as mentioned for Model 1&2). Suppose E1 & E2 together is effective and drives a high P(C|E1, E2) but it is not the case for P(C|E1, E3) and P(C|E2, E3), it can be seen that based on (5) E1 & E2 will each get more credits than E3.”), however, Zhong arguably fails to explicitly disclose, but Zhong in view of Kadyrov clearly teaches:
wherein the one or more processors are configured to: update weights through learning, the weights being set for the respective first actions in a process of calculation performed by the learning model, and extract the second action on a basis of the updated weights (Kadyrov Page 3, 3.1 Gradient Boosting over Decision Trees “Boosting over decision trees is considered one of the most efficient machine learning algorithms. The idea of boosting approach is as follows: it iteratively trains new basic classifiers that improve the composition of previously chosen ones, i.e. each new classifier compensates the errors of the composition of all the previously ones. In turn, gradient boosting optimises the differentiable loss function. The initial idea of boosting arose from the question [11]: is it possible to get strong classifier using many weak classifiers? Due to effectiveness of this machine learning approach, it is an important part of many search engines [14,4] and a tool of choice for data science athletes that won many machine learning competitions [2]. An additional motivation for using this machine learning approach comes from the reduction of the task of multi-channel attribution to the binary classification problem. We need to train a classifier that can sort out consumers into two classes: those who will perform the conversion action and those who will not. Since only 0.5% –2% of all consumers who saw ads reach conversion, this classification problem contains highly unbalanced classes. Gradient boosting over trees is just one of the techniques that address this class of problems well. Here, we use one of the most successful implementations of gradient boosting over trees named XGBoost. – Kadyrov teaches multi-attribute probability classification for conversion determinations using XGBoost. XGBoost is trained by modifying weights/leaf values using gradient descent. The outputs could be used to determine appropriate second actions based on the probability conversions of the first, as was done with the statistical models of Zhong.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify some of the statistical machine learning methods of Zhong by the gradient boosting machine learning methods of Kadyrov because the person of ordinary skill in the art would be motivated by the aim of Zhong to provide data-driven fractional attribution without bias, to look to Kadyrov, which teaches fractional attribution that eliminates the bias of unbalanced data due to the relatively low overall conversion rate. (Zhong [0014] “The new fractional attribution approach is data-driven, without preconceived bias on the importance of different campaigns or sites. It is also a general approach that works with any number of different types of advertising campaigns, provided that user identification is tracked across different channels and thus data from different channels can be joined together to give a complete picture of user's interactions with the advertiser's advertising campaigns. This approach can be applicable to scenarios when user-level data are not available for non-converting users.”; Kadyrov Abstract “A multichannel attribution model based on gradient boosting over trees is proposed, which was compared with the state of the art models: bagged logistic regression, Markov chains approach, shapely value. Experiments on digital advertising datasets showed that the proposed model is better than the solutions considered by ROC AUC metric. In addition, the problem of probability prediction of conversion by the consumer using the ensemble of the analyzed algorithms was solved, the meta-features obtained were enriched with consumers and offline activities of the advertising campaign data.” Page 3, 3.1 Gradient Boosting over Decision Trees “Since only 0.5%-2% of all consumers who saw ads reach conversion, this classification problem contains highly unbalanced classes. Gradient boosting over trees is just one of the techniques that address this class of problems well. Here, we use one of the most successful implementations of gradient boosting over trees named XGBoost.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “BOOSTING AND NAIVE BAYESIAN LEARNING” by Elkan (Teaches applying boosting to naïve Bayes)
NPL: “Attribution of Customers’ Actions Based on Machine Learning Approach” by Martin et al. (Teaches improving upon conversion prediction using boosting)
US 2018/0341879 A1 to Chittilappilly et al. (Teaches using models to determine attribute-based conversion data)
US 2019/0180357 A1 to Arora et al. (Teaches using models to determine attribute-based conversion data)
WO 2013/0106304 A1 to Kent (Teaches determining, using models, conversion probability based on features of a webpage)
US 2022/0129777 A1 to White et al. (Teaches predicting likelihood of conversion based on positions of websites)
US 2017/0091810 A1 to McGovern et al. (Teaches touchpoint contribution values to conversions)
US 2022/0292542 A1 to Kaspar et al. (Teaches using cascaded machine learning models to determine the likelihood of conversion for different sales attribuites)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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