Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the communications filed on June 29, 2026. The Applicant’s Amendment and Request for Reconsideration has been received and entered.
Claims 1-9, 12-16, 18-20, 22, 23, and 25 are currently pending and have been examined. Claims 1, 5, 19, and 20 have been amended. Claims 10, 11, 17, 21, and 24 have been cancelled. Claim 25 is newly added.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 29, 2026 has been entered.
Response to Arguments
Applicant’s amendments necessitated the new grounds of rejection.
Regarding the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 112(a), the Examiner respectfully argues that Figure 3 and paragraphs [0033]-[0035] do not disclose “mathematical if-then logic” [emphasis added](App. Remarks Page 9), but rather discloses natural language in the decision tree model format. Specifically, as per Applicant’s Specification [0033]-[0034], “the decision tree 300 includes a plurality of decision nodes 3021-302n (hereinafter individually referred to as a ‘decision node 302’ or collectively referred to as ‘decision nodes 302’) connected by a plurality of branches labeled either ‘YES’ or ‘NO.’ The decision nodes 302 represent decisions to be made (e.g., decisions concerning the product to be purchased), while the branches represent the possible decisions (e.g., yes or no) that can be made at a decision node 302. . . . In this case, a decision process may begin with a first decision node 3021 of the decision tree 300 and may progress through subsequent decision nodes 302 based on whether the answer to a previous decision node 302 was ‘YES’ or ‘NO.’” – these “YES” and “NO” labels are natural language. Indeed, neither Figure 3, paragraphs [0033]-[0035], nor any other portions of the original disclosure adequately describe "initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions [emphasis added]" as subject matter which the Applicant actually had possession of at the time of the invention.
Further, the Examiner respectfully argues that paragraphs [0015], [0051], and [0038] do not disclose “using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface [emphasis added].” Paragraphs [0015] and [0051] disclose “changes can be made to the decision trees” and “can be translated into changes on the customer-facing site”, but attribute no intent to those changes nor describe any artificial intelligence technique operating on them. Paragraph [0038] discloses “the processing system may understand the intent of a decision node 302 of the decision tree 300, and may use artificial intelligence techniques . . . and/or natural language processing in order to generate a text query designed to elicit an input from the user that will help to resolve the decision node 302”, however the intent of a decision node is not the claimed intent of a change (made to the decision tree). Thus, these two disclosures operate on different objects and the original disclosure as a whole does not adequately describe “using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface” as subject matter which the Applicant actually had possession of at the time of the invention.
Accordingly, the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 112(a) is maintained.
Regarding the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 112(b), as discussed above, the Examiner respectfully argues that Figure 3 and paragraphs [0033]-[0035] do not disclose “mathematical if-then logic” [emphasis added](App. Remarks Page 9), but rather discloses natural language in the decision tree model format. Specifically, as per Applicant’s Specification [0033]-[0034], “the decision tree 300 includes a plurality of decision nodes 3021-302n (hereinafter individually referred to as a ‘decision node 302’ or collectively referred to as ‘decision nodes 302’) connected by a plurality of branches labeled either ‘YES’ or ‘NO.’ The decision nodes 302 represent decisions to be made (e.g., decisions concerning the product to be purchased), while the branches represent the possible decisions (e.g., yes or no) that can be made at a decision node 302. . . . In this case, a decision process may begin with a first decision node 3021 of the decision tree 300 and may progress through subsequent decision nodes 302 based on whether the answer to a previous decision node 302 was ‘YES’ or ‘NO.’” – these “YES” and “NO” labels are natural language. Thus, the disclosure does not disclose any meaningful structure/algorithm explaining how one would “initializ[e], by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions [emphasis added]” and consequently, the metes and bounds of these claims are unclear.
Further, as discussed above, the Examiner respectfully argues that paragraphs [0015] and [0051] disclose “changes can be made to the decision trees” and “can be translated into changes on the customer-facing site”, but attribute no intent to those changes nor describe any artificial intelligence technique operating on them. Paragraph [0038] discloses “the processing system may understand the intent of a decision node 302 of the decision tree 300, and may use artificial intelligence techniques . . . and/or natural language processing in order to generate a text query designed to elicit an input from the user that will help to resolve the decision node 302”, however the intent of a decision node is not the claimed intent of a change (made to the decision tree). Therefore, these two disclosures operate on different objects. Thus, the disclosure does not disclose any meaningful structure/algorithm explaining how one would “us[e] the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface [emphasis added]” and consequently, the metes and bounds of these claims are unclear.
Accordingly, the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 112(b) is maintained.
Regarding the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 101, Applicant’s arguments have been fully considered but they are not persuasive for the reasons set forth infra.
As found infra, the Examiner respectfully argues that causing a user endpoint device to connect to a web site is merely a limitation recited at a high level of generality for performing a generic computer function of receive, process, and/or display data. This limitation is no more than a mere instruction to apply the abstract idea (purchasing of the recommended product) using generic computer components.
Moreover, the Examiner respectfully notes that the Applicant’s asserted improvement of “the technical field of user interfaces by allowing for interactions via user interfaces to be easily changed or to take different paths without requiring the writing of additional code” (App. Remarks. Page 12]) is based on limitations that have been rejected under 112(a) and 112(b) and interpreted accordingly, found infra. Still, the Examiner respectfully argues that even if the limitations were interpreted as presently recited in the claims, simplifying consumers’ ability to shop and interactions would at most improve the abstract idea and is not an improvement that is a technical solution to a technical problem. Further, while Applicant asserts an improvement to “the technical field of user interfaces by allowing for interactions via user interfaces to be easily changed or to take different paths without requiring the writing of additional code”, the user interfaces being changeable in real time, the Examiner respectfully argues that the referenced limitations, as recited, do not support this asserted technical improvement. Indeed, “in real time” is a generic speed benefit of computerization, “making a change to the decision tree”, under the broadest reasonable interpretation, recites a mental process (as a person could modify a decision tree using pen and paper); and “using the artificial intelligence technique” merely recites a generic AI model at a high level of generality and is thus treated as mere instruction to apply an abstract idea.
Accordingly, the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 101 is maintained.
Regarding the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 U.S.C. 103, Applicant’s arguments have been fully considered but they are not persuasive. Particularly, Applicant’s arguments are directed to the instantly amended claims and are thus moot in view of the new grounds of rejection. The Examiner respectfully notes that the limitations to which Applicant bases arguments has been rejected under 112(a) and 112(b) and interpreted accordingly, found infra.
Accordingly, the rejection of claims 1-9, 12-16, 18-20, 22, 23, and 25 under 35 USC 103 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9, 12-16, 18-20, 22, 23, and 25 is 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 pre- AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1, and similarly claims 19 and 20, recite "initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions" (emphasis added) and “wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface.” (emphasis added).
The recited subject matter of claim 1 does not conform to the disclosure in such a manner that one of ordinary skill in the art would recognize as being adequately described as the invention or as subject matter which the Applicant actually had possession of at the time of the invention. A review of the disclosure does not reveal the manner in which the a decision tree is initialized to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions and wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface.
Claims 2-9, 12-16, 18-20, 22, 23, and 25 depend from claim 1 and thus inherit the deficiencies of claim 1.
It is noted that this is not an enablement rejection. Applicant's failure to disclose any meaningful structures/algorithms regarding these limitations raises questions concerning whether Applicant truly had possession of these features at the time of filing.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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 1-9, 12-16, 18-20, 22, 23, and 25 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 pre-AIA , the applicant, regards as the invention.
Claim 1, and similarly claims 19 and 20, recite "initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions" (emphasis added) and “wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface.” (emphasis added).
The metes and bounds of these claims are unclear because a person having ordinary skill in the art cannot determine how to avoid infringement.
As discussed above, the disclosure does not disclose any meaningful structure/algorithm explaining how one would initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions and wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface. For examination purposes, the Examiner has interpreted these limitations as merely initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions and wherein the user interface is changeable.
Claims 2-9, 12-16, 18-20, 22, 23, and 25 depend from claim 1 and thus inherit the deficiencies of claim 1.
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 1-9, 12-16, 18-20, 22, 23, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without adding significantly more to the abstract idea itself.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? (YES)
Claims 1-9, 12-16, 18, 22-23, and 25 are directed to a method, which is a process. Claim 19 is directed to a “non-transitory computer-readable medium”, which is a manufacture. Claim 20 is directed to a device, which is a machine. Therefore, claims 1-9, 12-16, 18-20, 22, 23, and 25 are directed to one of the four statutory categories of invention.
Step 2(A): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? (YES)
Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (YES)
Taking claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity and mental processes groupings of the abstract ideas, including:
A method comprising:
receiving, by a processing system including at least one processor, a signal indicating that a user wishes to initiate a purchase of a product;
initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions;
presenting, by the processing system via a user interface, a first query to the user, wherein the first query is selected for presentation based on the decision tree, and wherein the processing system uses an artificial intelligence technique to generate the first query in a manner that captures an intent of a first portion of the decision tree in a natural language form;
receiving, by the processing system via the user interface in response to the first query, a first user input comprising at least one of: a feature preference related to the product or a budget constraint related to the product;
presenting, by the processing system via the user interface, a subsequent query to the user, wherein the subsequent query is selected for presentation based on the first user input and the decision tree, and wherein the processing system uses the artificial intelligence technique to generate the subsequent query in a manner that captures an intent of a subsequent portion of the decision tree in the natural language form;
receiving, by the processing system via the user interface in response to the subsequent query, a subsequent user input comprising at least one of: a feature preference related to the product or a budget constraint related to the product, wherein the subsequent user input is different from the first user input; and
presenting, by the processing system, information about a recommended product that is identified by using the first user input and the subsequent user input to traverse the decision tree, to the user via the user interface, wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface; and
initiating, by the processing system, a purchase of the recommended product in response to a second signal received via the user interface by causing a user endpoint device of the user to connect to a web site via which the product is offered for purchase.
Applicant’s specification discloses that a “decision tree” comprises a series of questions that a user must answer so that a product or service matching the user’s preferences and budget can be determined (paragraph [0024], lines 4-10 and Fig. 3). The limitations emphasized above in bold recite the abstract idea of asking a user a series of questions in order to identify, recommend a product or service that matches the user’s preferences and budget, and initiate a purchase of the recommended product. This is an abstract idea because it covers certain methods of organizing human activity (i.e., commercial interactions including sales and marketing activities). The limitations emphasized above in bold also recite the abstract idea of initializing a decision tree (i.e., a series of questions) to collect information from a user related to the purchase of the product. This is an abstract idea because it covers mental process or can be performed by pen and paper. Claims 2-9, 12-16, 18-20, and 22-24 recite the same abstract idea identified in claim 1.
Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (NO)
Claims 1-9, 12-16, 18-20, 22, 23, and 25 recite additional elements beyond the abstract idea, including:
A method comprising:
receiving, by a processing system including at least one processor, a signal indicating that a user wishes to initiate a purchase of a product;
initializing, by the processing system, a decision tree to collect information from the user related to the purchase of the product, wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions;
presenting, by the processing system via a user interface, a first query to the user, wherein the first query is selected for presentation based on the decision tree, and wherein the processing system uses an artificial intelligence technique to generate the first query in a manner that captures an intent of a first portion of the decision tree in a natural language form;
receiving, by the processing system via the user interface in response to the first query, a first user input comprising at least one of: a feature preference related to the product or a budget constraint related to the product;
presenting, by the processing system via the user interface, a subsequent query to the user, wherein the subsequent query is selected for presentation based on the first user input and the decision tree, and wherein the processing system uses the artificial intelligence technique to generate the subsequent query in a manner that captures an intent of a subsequent portion of the decision tree in the natural language form;
receiving, by the processing system via the user interface in response to the subsequent query, a subsequent user input comprising at least one of: a feature preference related to the product or a budget constraint related to the product, wherein the subsequent user input is different from the first user input;
presenting, by the processing system, information about a recommended product that is identified by using the first user input and the subsequent user input to traverse the decision tree, to the user via the user interface, wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface; and
initiating, by the processing system, a purchase of the recommended product in response to a second signal received via the user interface by causing a user endpoint device of the user to connect to a web site via which the product is offered for purchase.
Applicant’s specification discloses that the processing system comprises a generic computing device that includes a processor, memory, and an input/output device (paragraph [0054] and Fig. 5, “502”, “504”, and “506”). It is noted that, according to 112(b) rejection, supra, the limitation wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface has been interpreted to mean merely wherein the user interface is changeable. Whether taken individually or in combination as a whole, the limitations emphasized above in bold are recited at a high level of generality for performing generic computer functions of receiving, processing, storing, and displaying data. These limitations are no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 2 recites the additional element beyond the abstract idea, including: an endpoint device.
Claim 13 recites the additional element beyond the abstract idea, including: a chatbot interface.
Claim 14 recites the additional element beyond the abstract idea, including: a website.
Claims 19 and 20 recite additional elements beyond the abstract idea, including: a processing system, processor, memory, signal, and user interface.
Claim 22 recites the additional element beyond the abstract idea, including: metadata.
Claim 24 recites the additional element beyond the abstract idea, including: a website.
Applicant’s specification discloses that the “endpoint device” and the processing system comprise a generic computing device that includes a processor, memory, and an input/output device (paragraph [0019], lines 8-10; paragraph [0054] and Fig. 5, “502”, “504”, and “506”). Whether taken individually or in combination as a whole, the limitations emphasized above in bold are recited at a high level of generality for performing generic computer functions of receiving, processing, storing, and displaying data. These limitations are no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2(B): Does the claim recite additional elements that amount to significantly more than the judicial exception? (NO)
The next step is to analyze the claims to determine whether there are additional limitations recited that amount to significantly more than the abstract idea.
Claims 1-9, 12-16, 18, and 22-24 recite the additional elements of: a processing system, processor, a signal, a database, a user interface, artificial intelligence, a second signal, and a web site. Applicant’s specification discloses that the processing system comprises a generic computing device that includes a processor, memory, and an input/output device (paragraph [0054] and Fig. 5, “502”, “504”, and “506”). Whether taken individually or in combination as a whole, the limitations emphasized above in bold are recited at a high level of generality for performing generic computer functions of receiving, processing, storing, and displaying data. These limitations are no more than mere instructions to apply the exception using generic computer components.
Claim 2 recites the additional elements of: an endpoint device. Claim 13 recites the additional elements of: a chatbot interface. Claim 14 recites the additional elements of: a website. Claims 19 and 20 recite the additional elements of: a processing system, processor, memory, signal, and user interface. Claim 22 recites the additional element of: metadata. Claim 24 recites the additional elements of: a website. Applicant’s specification discloses that the “endpoint device” and the processing system comprise a generic computing device that includes a processor, memory, and an input/output device (paragraph [0019], lines 8-10; paragraph [0054] and Fig. 5, “502”, “504”, and “506”). Whether taken individually or in combination as a whole, the limitations emphasized above in bold are recited at a high level of generality for performing generic computer functions of receiving, processing, storing, and displaying data. These limitations are no more than mere instructions to apply the exception using generic computer components.
Taking the additional elements individually, the computer components perform purely generic computer functions. Taking the additional elements in combination, the claims as a whole are directed to an abstract idea that is implemented using generic computer components. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. The claims do not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9, 12, 14-16, 18-20, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over US 7,076,456 B1 (“Rofrano”) in view of US 10,909,488 B2 (“Hecht”) in view of US 10,074,121 B2 (“Grimaud”).
Claim 1: Rofrano teaches a method comprising:
receiving, by a processing system including at least one processor (Fig. 1, “1”; col. 4, lines 25-34), a signal indicating that a user wishes to initiate a purchase of a product (Fig. 2, “20”);
initializing, by the processing system (Fig. 1, “1”; col. 4, lines 25-34), a decision tree to collect information from the user related to the purchase of the product (col. 3, lines 34-48), wherein the decision tree models, in a non-natural language format, a plurality of decisions to be made concerning the product and a plurality of possible outcomes of the plurality of decisions (col. 5, lines 19-26: “In the method of FIG. 2, a host system 1 queries the database and determines which questions to ask. The host system 1 acts on questions that a salesperson would normally ask a customer, if such a salesperson were actually present. Such questions, and associated relevant answers, are created from a database of products and associated features. Feature constraints are created from a list of all possible features for each category of products associated 25 with a particular question.”; col. 4, line 60 through col. 5, line 18 “Next, in step 22, the question with the highest rank (or next highest rank in subsequent steps) is obtained. In step 24, whether the question is relevant is determined. The question is relevant if at least one of its answers is relevant, and an answer is relevant if it has no product constraints or if its product constraints, as combined with all previous answer product constraints, yield a positive or non-zero product count. If the question is relevant, then in step 26, the question and associated answers are presented to the customer. . . . Next, in step 28, the customer selects an answer. Based on the customers selection, step 30 determines whether constraints on the product selection are required. If so, product selection is constrained in step 32. Regardless of whether or not product selection is constrained, step 34 determines whether to ask another question. . . . If other questions remain to be asked, the entire method is repeated, starting with step 22, by obtaining the question with the next highest rank.”
presenting, by the processing system (Fig. 1, “1”; col. 4, lines 25-34) via a user interface (Fig. 1, “2”), a first query to the user, wherein the first query is selected for presentation based on the decision tree (Fig. 2, “26”; col. 3, lines 49-51),
receiving, by the processing system (Fig. 1, “1”; col. 4, lines 25-34) via the user interface (Fig. 1, “2”) in response to the first query, a first user input comprising at least one of: a feature preference related to the product (Fig. 2, “28”; see Table 2: “Are you more interested in high-quality or compatibility?”) or a budget constraint related to the product;
presenting, by the processing system (Fig. 1, “1”; col. 4, lines 25-34) via the user interface (Fig. 1, “2”), a subsequent query to the user, wherein the subsequent query is selected for presentation based on the first user input and the decision tree (Fig. 2, “34” loops back to ask another question; see Table 2: “Do you plan to take this camera on vacations or trips?”),
receiving, by the processing system (Fig. 1, “1”; col. 4, lines 25-34) via the user interface (Fig. 1, “2”) in response to the subsequent query, a subsequent user input comprising at least one of: a feature preference related to the product (Fig. 2, “28”) or a budget constraint related to the product, wherein the subsequent user input is different from the first user input (Table 2 shows that the answer choices to question 1 are limited to “High Quality” or “Compatibility”, and that the answer choices to question 2 are limited to “Yes” or “No”); and
presenting, by the processing system, information about a recommended product that is identified by using the first user input and the subsequent user input to traverse the decision tree, to the user via the user interface (col. 6, lines 48-55: “The host system 1 searches for the next highest ranked question, and in this example, determines that no more questions with relevant answers exist. Therefore, the product constraints become ‘FORMAT=S MM,’ ‘BODY=Compact’ and ‘LIGHT=Built-in,’ yielding one product that meets the constraints. Finally, as can be determined from Table 1, Camcorder 1 is presented to the customer.”), wherein the user interface is changeable in real time without performing manual coding by making a change to the decision tree and using the artificial intelligence technique to translate an intent of the change into a natural language form that is presentable via the user interface. (Fig. 2; col. 4, line 60 through col. 5, line 18 “Next, in step 22, the question with the highest rank (or next highest rank in subsequent steps) is obtained. In step 24, whether the question is relevant is determined. The question is relevant if at least one of its answers is relevant, and an answer is relevant if it has no product constraints or if its product constraints, as combined with all previous answer product constraints, yield a positive or non-zero product count. If the question is relevant, then in step 26, the question and associated answers are presented to the customer. Note that the associated answers may be limited to only relevant answers, thereby precluding the customer from being presented with an answer selection that may result in a zero product count. Next, in step 28, the customer selects an answer. Based on the customers selection, step 30 determines whether constraints on the product selection are required. If so, product selection is constrained in step 32. Regardless of whether or not product selection is constrained, step 34 determines whether to ask another question. If no other questions are to be asked, in step 36, the customer is presented with products meeting the customer's requirements. If other questions remain to be asked, the entire method is repeated, starting with step 22, by obtaining the question with the next highest rank.”)
Rofrano does not teach wherein the processing system uses an artificial intelligence technique to generate the first query in a manner that captures an intent of a first portion of the decision tree in a natural language form; and wherein the processing system uses the artificial intelligence technique to generate the subsequent query in a manner that captures an intent of a subsequent portion of the decision tree in the natural language form. However, Hecht teaches and wherein the processing system uses an artificial intelligence technique to generate the first query in a manner that captures an intent of a first portion of the decision tree in a natural language form; and wherein the processing system uses the artificial intelligence technique to generate the subsequent query in a manner that captures an intent of a subsequent portion of the decision tree in the natural language form (col. 145, line 37 through col. 146, line 22 “questions may be presented in natural language form…machine learning, machine translation, neural networking, and/or any other suitable means of preparing and mapping questions”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Hecht into the invention of Rofrano. One of ordinary skill in the art would have been motivated to do so in order to present the questions in a language that was appropriate for a particular user, as taught by Hecht.
Rofrano/Hecht do not teach initiating, by the processing system, a purchase of the recommended product in response to a second signal received via the user interface by causing a user endpoint device of the user to connect to a web site via which the product is offered for purchase. However, Grimaud teaches a purchasing helper (Fig. 3, “304”) that uses a decision tree (Fig. 5, “502”) to pose questions to a consumer (col. 2, lines 25-35; col. 3, lines 29-35) before presenting a suggested product (Figs. 8, “806”) and allowing the consumer to complete a purchase by causing a user endpoint device of the user to connect to a web site via which the product is offered for purchase (Fig, 8, “814”, Fig. 2, col. 8 , lines 45-67 “FIG. 2 is a block diagram 200 illustrating an example embodiment of a user 202 using a user device 204 to interface with a plurality of retailers 210 a-c. The user device 204 displays a virtual store 220 based on a consumer information model 222. . . . The user device 204 can present the relevant products available at the retailers #1-3 210 a-c to the user 202 in the virtual store 220. Responsive to the virtual store 220 presenting the product data 226 a-c to the user, the user 202 can select and purchase one of the suggested products, or can navigate to a different product to purchase.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Grimaud into the invention of Rofrano/Hecht. One of ordinary skill in the art would have been motivated to do so in order to allow the consumer to initiate and complete a transaction for the recommended product, as taught by Grimaud.
Claim 2: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that the signal comprises an electronic signal received via the user interface that is displayed on an endpoint device of the user (This limitation is inherent because Rofrano discloses that the invention is implemented over a computer network between a plurality of client computers (Fig. 1, “2”) and a host computer (Fig. 1, “1”)).
Claim 3: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach a plurality of decision nodes, wherein each decision node of the plurality of decision nodes represents one decision of the plurality of decisions; and a plurality of branches connecting the plurality of decision nodes, wherein each branch of the plurality of branches represents a possible decision made at one decision node of the plurality of decision nodes (col. 3, lines 34-51 teaches a question and answer trees that are traversed; Fig. 2; col. 4, line 60 through col. 5, line 18)
Claim 4: Rofrano/Hecht/Grimaud teach the limitations of claim 3 as noted above. Rofrano/Hecht/Grimaud also teach wherein a combination of a subset of the plurality of decision nodes and a subset of the plurality of branches comprises a path through the decision tree. (col. 3, lines 34-51; Fig. 2; col. 4, line 60 through col. 5, line 18)
Claims 5: Rofrano/Hecht/Grimaud teach the limitations of claim 4 as noted above. Rofrano/Hecht/Grimaud also teach wherein the path ends in an outcome node representing one outcome of the plurality of possible outcomes (col. 6, lines 48-55; Fig. 2; col. 4, line 60 through col. 5, line 18).
Claim 6: Rofrano/Hecht/Grimaud teach the limitations of claim 5 as noted above. Rofrano/Hecht/Grimaud also teach wherein the outcome node represents the recommended product. (col. 6, lines 48-55; Fig. 2; col. 4, line 60 through col. 5, line 18).
Claim 7: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that each of the first query and the subsequent query is designed to narrow down a set of possible products to identify the recommended product (Table 1 shows a list of products and Table 2 shows a list of questions that are designed to narrow down the list of products to a recommended product; col. 6, lines 48-55).
Claim 8: Rofrano/Hecht/Grimaud teach the limitations of claim 7 as noted above. Rofrano/Hecht/Grimaud also teach repeating the presenting the subsequent query and receiving the subsequent user input until the processing system is able to identify the recommended product using the decision tree (Fig. 2, “34” loops back to ask another question; Table 1 shows a list of products and Table 2 shows a list of questions that are designed to narrow down the list of products to a recommended product; col. 6, lines 48-55).
Claim 9: Rofrano/Hecht/Grimaud teach the limitations of claim 7 as noted above. Rofrano/Hecht/Grimaud also teach repeating the presenting the subsequent query and receiving the subsequent user input until the processing system is able to identify no more than a threshold number of products including the recommended product (Fig. 2, “34” loops back to ask another question; Table 1 shows a list of products and Table 2 shows a list of questions that are designed to narrow down the list of products to a recommended product; col. 6, lines 48-55).
Claim 12: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that the user interface comprises a graphical user Interface (Fig. 1, “2”).
Claim 14: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that the user interface is part of a web site provided by a seller of the product (col. 4, lines 18-21).
Claim 15: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that the recommended product matches any feature preferences and budget constraints indicated in the first user input and the subsequent user input (col. 6, lines 48-55: “The host system 1 searches for the next highest ranked question, and in this example, determines that no more questions with relevant answers exist. Therefore, the product constraints become ‘FORMAT=S MM,’ ‘BODY=Compact’ and ‘LIGHT=Built-in,’ yielding one product that meets the constraints. Finally, as can be determined from Table 1, Camcorder 1 is presented to the customer.”).
Claim 16: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that a modification made to the decision tree is reflected in real time in the user interface (col. 3, lines 56-57 teaches changing the rank of a question; Fig. 1 shows that invention is implemented over a computer network), and wherein the modification includes at least one of: an addition of a new node to the decision tree or a deletion of an existing node from the decision tree. (col. 3, lines 56-57 teaches “changing the rank of a question; Fig. 1 shows that invention is implemented over a computer network”; col. 6, lines 56-65 “The above description and examples illustrate how efficiently a series of questions are handled by executing a ranked list, wherein, if a question's answers are irrelevant (or contain only one relevant answer), the question is automated deleted (or the constraints of the only relevant answer are automatically applied). Therefore, redundancy is eliminated and no manual “pruning” from a question and answer tree is required.”; Fig. 2; col. 4, line 60 through col. 5, line 18 “Next, in step 22, the question with the highest rank (or next highest rank in subsequent steps) is obtained. In step 24, whether the question is relevant is determined. The question is relevant if at least one of its answers is relevant, and an answer is relevant if it has no product constraints or if its product constraints, as combined with all previous answer product constraints, yield a positive or non-zero product count. If the question is relevant, then in step 26, the question and associated answers are presented to the customer. Note that the associated answers may be limited to only relevant answers, thereby precluding the customer from being presented with an answer selection that may result in a zero product count. Next, in step 28, the customer selects an answer. Based on the customers selection, step 30 determines whether constraints on the product selection are required. If so, product selection is constrained in step 32. Regardless of whether or not product selection is constrained, step 34 determines whether to ask another question. If no other questions are to be asked, in step 36, the customer is presented with products meeting the customer's requirements. If other questions remain to be asked, the entire method is repeated, starting with step 22, by obtaining the question with the next highest rank.”)
Claim 18: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud also teach that the decision tree is selected to be initialized from among a plurality of decision trees based on the product indicated in the signal (Fig. 2: “Customer initiates catalog purchase and asks for assistance 20, Get question with next highest rank 22”; col. 5, lines 19-26: “The host system 1 acts on questions that a salesperson would normally ask a customer, if such a salesperson were actually present. Such questions, and associated relevant answers, are created from a database of products and associated features. Feature constraints are created from a list of all possible features for each category of products associated 25 with a particular question.”)
Claim 19: This claim is rejected under the same rationale as set forth above in claim 1.
Claim 20: This claim is rejected under the same rationale as set forth above in claim 1.
Claim 22: Rofrano/Hecht/Grimaud teach the limitations of claim 18 as noted above. Rofrano/Hecht/Grimaud also teach wherein the signal includes metadata generates by an action of the user that is matched to metadata associated with the decision tree. (col. 5, line 35 through col. 6, line 55 “customer answers the first ranked question with “High quality,” the host system 1 will apply the product constraint of “FORMAT=8 MM.” As may be seen in Table 1, this answer limits the product selection to three products: CamCorder 1, CamCorder 2 and CamCorder 3. If the customer answers the first ranked question with “Compatibility,” the host system 1 will apply the product constraint of “FORMAT=VHS.” As may be seen in Table 1, this answer limits the product selection to four products: HandyCorder 1, HandyCorder 2, HandyCorder 3 and HandyCorder 4. . . . In the example having an initial product constraint of “FORMAT=8 MM,” a “Yes” answer adds the product constraint “LIGHT=Built-in” to the list of product constraints.”).
Claim 23: Rofrano/Hecht/Grimaud teach the limitations of claim 3 as noted above. Rofrano/Hecht/Grimaud also teach wherein the first portion of the decision tree comprises a first decision node of the plurality of decision nodes, and the subsequent portion of the decision tree comprises a subsequent decision node of the plurality of decision nodes. (col. 3, lines 34-51 teaches a question and answer trees that are traversed, and “each question is assigned a rank from highest to lowest, with no duplication questions . . . based on the answer to the next highest ranked question, a question with the 'new' next highest ranked may be asked”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US 7,076,456 B1 (“Rofrano”) in view of US 10,909,488 B2 (“Hecht”) in view of US 10,074,121 B2 (“Grimaud”) in view of US 2022/0398598 A1 (“Das”).
Claim 13: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud do not teach the user interface comprises a chatbot user interface. However, Das teaches guiding a customer through a decision tree using a chatbot (paragraph [0048]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Das into the invention of Rofrano/Hecht/Grimaud. One of ordinary skill in the art would have been motivated to do so in order to provide the customer with a user-friendly and interactive interface when guiding the customer thorough the decision tree.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over US 7,076,456 B1 (“Rofrano”) in view of US 10,909,488 B2 (“Hecht”) in view of US 10,074,121 B2 (“Grimaud”) in view of US 2011/0137818 A1 (“Goad”).
Claim 25: Rofrano/Hecht/Grimaud teach the limitations of claim 1 as noted above. Rofrano/Hecht/Grimaud do not teach the processing system is a part of a standalone service that is unaffiliated with a source of the product. However, Goad teaches the processing system is a part of a standalone service that is unaffiliated with a source of the product. (Fig. 1; paragraph [0035]-[0036] (Turning now to the figures, FIG. 1 illustrates an embodiment of an electronic system 100 for providing and recommending third party products and services to users (i.e., an order facilitation and recommendation service), as described in detail herein. As shown, the system 100 includes a computing system 102 operatively coupled to one or more third party product and service provider(s) 104, user(s) 106, address or location source(s) 108, and other external information source(s) 110 through a network (e.g., Internet) 112.); paragraph [0054]-[0056] (Further, the computing system 102 may request the user's permission to access his or her records from his or her current phone service provider. Additionally, if the user 106 selects a particular product or service plan from the list of available plans (e.g., from the Telco plans shown in FIG. 7, or the ranked cable service plans shown in FIG. 6), then the computing system 102 may update user or service information via the service provider's web portal. . . Further, if the user requests an additional phone line, the system can request installation services with the Telco website.); [0045]-[0046]; [0033] ;[0061] (end users, partners, and the enterprise)))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Goad into the invention of Rofrano/Hecht/Grimaud. One of ordinary skill in the art would have been motivated to do so in order to interact with a user to combine and analyze various sources of disparate information relating to third party products and services offered to a user's address or geographic location, and suggest optimal products or services to the user based on the collected information to enable the user to select the most appropriate product(s) or service(s) that fit his or her needs, as taught by Goad.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gershman, A., Meisels, A., Lüke, K., Rokach, L., Schclar, A., & Sturm, A. (2010). A decision tree based recommender system. Retrieved from https://dialog.proquest.com/professional/docview/2696700921?accountid=131444.
Yuan (US PGP 2023/0127907) -- machine learning model for natural language processing used to generate a question corresponding to a node of the decision tree to determine the user's intention.
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/JENNIFER V LEE/Examiner, Art Unit 3688
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 8/31/2026
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